The use of the internet to satisfy information needs is widespread among cancer patients. Patients’ decisions regarding whether to act upon the information they find strongly depend on the trustworthiness of the information and the medium. Patients who are younger, more highly educated and female are more likely to trust online information. The objectives of this systematic review were to examine the extent to which cancer patients trust in cancer-related online information, internet websites as a source of cancer-related information or the internet as a medium of cancer information.A systematic review was conducted using five databases (PROSPERO registration number: CRD42017070190). Studies of any kind were included if they measured cancer patients’ trust in online health information. Study quality was assessed using the Research Triangle Institute (RTI) item bank. A narrative synthesis was undertaken to examine the included studies.Of the 7,314 citations obtained by the search, seven cross-sectional studies were included in the synthesis. A total of 1,054 patients reported having some or a great deal of trust in online cancer information; 154 patients reported moderately trusting such information; and 833 patients reported having no or little trust in online cancer information, internet websites as a source of cancer-related information or the internet as a medium of cancer-related information. Two of the seven studies reported between group comparisons for the above-stated patient characteristics. The methodological quality of the included studies was diverse.The results of the included studies indicates that approximately half of cancer patients appear to trust cancer-specific online information, internet websites as a source of cancer-related information or the internet as an information medium. However, the small number of included studies, high heterogeneity of participants, methods and outcomes calls for further systematic research. It is important to understand that cancer patients do and will increasingly use trusted cancer information websites to search for information concerning their disease. Therefore, physicians and other health care providers should provide more support and advice to these patients. Information needs are among the most prevalent unmet supportive care needs of cancer patients throughout their cancer journey . The majThe prevalence of cancer patients who used the internet to look for cancer-related information in a Dutch sample, two American samples and a Swedish sample ranged from 60% to 75% , and theThere are various motivational reasons for cancer patients to search for cancer information on the internet. Patients reported going online because they wanted to develop questions to discuss with their physician, verify information given by their physician, or seek alternative treatments  and becaInformation obtained from the internet can impact clinical care in different ways. Cancer-related online information can change patients’ choice of treatment, their choice of physician, and their decisions regarding enrollment in a clinical trial . Most caCancer patients who search the internet for cancer-related information tend to be younger and more highly educated than those who do not , and theThe quality of the cancer-related information that can be accessed on the internet is highly variable. Several studies used the DISCERN scale, a validated instrument developed to help consumers evaluate the quality of health-related information on treatment choices for a specific health problem , to evalFew studies have investigated which types of websites cancer patients visit to satisfy their information needs. Nonetheless, cancer patients consider health information websites to be a more valuable source of health information than forums or blogs . These pThe varying quality of the cancer-related information available online presents cancer patients with significant challenges in evaluating and selecting reliable online information sources and, more specifically, in assessing the credibility and trustworthiness of these sources. Trust is an important factor associated with the intention to use information found on a website . People The decision whether to trust a cancer-related online information can be a complex one as information-searching skills, prior experiences with the source and medium of the information, and characteristics of the source may influence the cancer patient’s decision. There are different levels  of trust that have been studied in the literature. The interpersonal level appears to be the appropriate level for determining trust in online information, as information is provided by an author (trustee) and communicated over a certain channel (the internet) to a receiver (trustor) . A definThe terms trust and credibility are often used interchangeably. However, credibility can be described as perceived information quality, or the evaluation of information quality by a user . After eConsumers’ trust in health information websites can be influenced by various factors. Two systematic reviews  assessedIn summary, it can be stated that cancer patients can only benefit from online cancer information if they can trust the information or the internet as a medium of this information. To date, no systematic review that analyzed cancer patients’ trust in online health information has been published. The primary goal of this systematic review is to identify the extent to which cancer patients trust cancer-related online information, internet websites as a source of cancer-related information or the internet as a medium of cancer-related information. As a secondary goal, the review seeks to determine whether trust in cancer-related online information differs across patients of different ages, genders, health statuses, education levels or cancer types.A systematic literature review was conducted to explore cancer patients’ trust in cancer-related online information, internet websites as a source of cancer-related information or in the internet as a medium of cancer information. The protocol for the systematic review was registered in the international prospective register of reviews (PROSPERO) with the registration code CRD42017070190 . AdditioWe performed an electronic literature search of the electronic databases Medline, CINAHL, Web of Science, PsycINFO and PSYINDEX using prespecified search terms with no restriction on the publication period. All searches were performed on the 4th of January 2017 . Additionally, the reference lists of the included studies were manually searched for potentially relevant studies.To systematically identify search terms that could address the research questions, the PICO criteria were adapted. PICO criteria can help facilitate the process of finding an answer to a clinical question, by identifying appropriate keywords that can be used to conduct a literature search . The focAll studies obtained from the initial search had to fulfill the inclusion criteria of the two selection phases. During the first phase, the corresponding author screened the titles and abstracts of all studies. Consistent with the broad research questions, studies of any type were included if the study title or abstract stated that cancer patients or cancer survivors of any age and with any type of cancer participated in the study and if one of the reported outcomes appeared to be participants’ trust, perceptions of credibility or distrust in online cancer-related information, internet websites as a source of cancer-related information or in the internet as a medium of cancer information.During the second phase, two researchers independently assessed the full texts of the remaining potentially relevant articles. The eligibility criteria used in the full text screening addressed two aspects: study characteristics and report characteristics . The incData extraction was performed by the corresponding author (LL) and cross-checked by another member (MLP) of the research group. The following data were extracted from the included studies: (1) study characteristics, including the author name, year of publication, title of publication, place of data collection, study design, and sample size; (2) characteristics of the study participants, such as age, gender, cancer type, education status, health status; (3) outcome characteristics, such as questionnaire or items used to measure trust; and (4) the measured outcome of trust, perceived credibility or distrust . Additionally, six corresponding authors of the studies in question were contacted for further information ; four of them responded. Most provided additional information concerning their publications. Three authors provided additional descriptive information, and the fourth author shared the complete data file of the study results.The methodological quality assessment of the included papers was independently performed by two researchers in the study group and based on the RTI item bank . The RTIA narrative synthesis was undertaken to examine the included studies . FurtherNo planned meta-analysis was conducted to answer the secondary study goal  because only two of the included studies reported between-group comparisons for these patient characteristics. However, to illustrate the between-group comparisons of individual studies, we calculated the mean differences (MDs) by subtracting the mean score of one group of participants , which was expected to score higher, from the mean score of the second group of participants . For clarity, all the mean trust scores reported in the included studies were transformed into a 5-point scale .https://doi.org/10.6084/m9.figshare.7701014.v1). After the removal of duplicate articles, 6132 titles and abstracts were scanned for eligibility. Of these, 54 studies fulfilled the eligibility criteria of the first selection phase. Four additional studies were added after the reference lists of the 54 potentially relevant studies were manually searched. The full text of two articles could not be retrieved despite contacting the authors. Of the remaining 51 articles, seven  resulted in 7,314 citations . All cits, seven  met the The characteristics of all the articles are described in All but one study used one item scored on a 3- to 5-point scale to measure the participants’ trust in information found on the internet  or the iThe study results were grouped into three themes based on the study goals: (1) characteristics of the included study samples; (2) cancer patients’ trust in cancer- or health-specific online information, in internet websites as a source of cancer-related information or in the internet as a medium of cancer information; and (3) between-group comparison of patient characteristics . A full overview of the results is displayed in In the five studies that reported the participants’ age, the mean age was 56 years . In the five studies that reported the participants’ education levels, the largest group in each study was highly educated. Two studies did not report their participants’ education levels .Three studies  includedApproximately half of the 2,041 cancer patients who participated in all of the included studies combined appeared to trust online cancer information   and −0.0The detailed methodological quality ratings of the included studies are displayed in Question six gives an indication of the detection bias of the included studies. Three studies  used valThis review includes seven studies that describe cancer patients’ trust in cancer-related online information. Overall, approximately half of the cancer patients in the included studies reported that they trusted cancer-related online information, internet websites as a source of cancer-related information or the internet as a medium of cancer-related information.There appears to be differences in the results of the seven included studies. The lowest trust scores were reported in the three studies with the largest samples of cancer patients with various types of cancer . In thesThe internet appears to be the second-most-trusted cancer information medium behind health care professionals . PatientThe number of breast cancer patients in one of the included studies  that repIt was not possible to answer the secondary research question: Does trust in online health information differ between cancer patients with different ages, genders, health status or education levels? Most of the included studies did not examine how patients’ characteristics influence the amount of reported trust in online health information as measuring trust was not their main research goal. The results of the included studies that did examine within-group differences did not always confirm the results of recent systematic reviews that identified consumer characteristics that might influence the reported trust in health information websites : In one In terms of the methodological quality of the included studies, it should be noted that the assumptions of this review are exclusively based on the results of cross-sectional studies, which are likely to have different biases . OverallSix of the studies only used one item to measure the primary outcome of trust, creating a possible source of bias. Latent variables are usually complex and not easily measured. The use of multiple items helps to average out errors that are inherent in single items and therefore have higher reliability and criterion validity than a single item . Single This systematic review has some limitations. Its main limitation is the lack of knowledge of the webpages that formed the basis of the study participants’ trust assessments. As stated in the introduction, the quality of the cancer-related information that can be accessed on the internet is highly variable . When paIn addition to the limitations, this review also has distinct strengths. First, because the authors aimed to provide a broad picture of cancer patients’ trust in online health information, the study used a systematic search strategy of five electronic databases, which resulted in a heterogeneous sample of studies and did not exclude studies due to their design or quality. Additionally, the search strategy used appeared to be successful as only four additional studies could be identified through manual searches of the reference lists of studies that fulfilled the eligibility criteria of the first selection phase, and none of these four studies was included in the review. A further strength is the methodological quality assessment of the included papers, which was independently performed by two researchers according to the reporting guidelines of the PRISMA statement . FinallyThis systematic review included seven cross-sectional studies out of 7,314 citations obtained from a search. The results of the included studies indicate that approximately half of cancer patients appear to trust information found on the internet, internet websites as a source of cancer-related information or trust the internet itself as a medium of cancer information. However, the small number of included studies, high heterogeneity of participants, methods and outcomes, and the diverse quality of the included studies call for further systematic research.Further research on cancer patients needs to be conducted using a validated instrument to measure patients’ perceived trust and the credibility of health information websites, especially for groups of patients such as older adults and those with a low socioeconomic status, who appear to have lower online information searching skills and tend to be less likely to trust cancer information found online.Furthermore, it is important to understand that cancer patients’ decision making is influenced by online information and that even if the physician remains the most trusted medium of advice, patients do and will increasingly use cancer-related websites to search information concerning their disease and its treatment. Therefore, physicians, nurses and other health care providers should provide more support and advice to patients seeking health information. Additionally, patients should be encouraged to ask their doctors questions and to discuss the results of their online information searches with them to ensure that false information is not included.10.7717/peerj.7634/supp-1Table S1Click here for additional data file.10.7717/peerj.7634/supp-2Table S2Click here for additional data file.10.7717/peerj.7634/supp-3Table S3Click here for additional data file.10.7717/peerj.7634/supp-4Table S4Click here for additional data file.10.7717/peerj.7634/supp-5File S1Click here for additional data file.10.7717/peerj.7634/supp-6Supplemental Information 1Click here for additional data file.


Eye movements are an important index of the neural functions of visual information processing, decision making, visuomotor coordination, sports performance, and so forth. However, the available optical tracking methods are impractical in many situations, such as the wearing of eyeglasses or the presence of ophthalmic disorders, and this can be overcome by accurate recording of eye movements by electrooculography (EOG). In this study we recorded eye movements by EOG simultaneously with high-density electroencephalogram (EEG) recording using a 128-channel EGI electrode net at a 500-Hz sampling rate, including appropriate facial electrodes. The participants made eye movements over a calibration target consisting of a 5×5 grid of stimulus targets. The results showed that the EOG methodology allowed accurate analysis of the amplitude and direction of the fixation locations and saccadic dynamics with a temporal resolution of 500 Hz, under both cued and uncued analysis regimes. Blink responses could be identified separately and were shown to have a more complex source derivation than has previously been recognized. The results also showed that the EOG signals recorded through the EEG net can achieve results as accurate as typical optical eye-tracking devices, and also allow for simultaneous assessment of neural activity during all types of eye movements. Moreover, the EOG method effectively avoids the technical difficulties related to eye-tracker positioning and the synchronization between EEG and eye movements. We showed that simultaneous EOG/EEG recording is a convenient means of measuring eye movements, with an accuracy comparable to that of many specialized eye-tracking systems. A wide variety of human brain functions have been studied with event-related analyses, in the form of averaged event-related potentials (ERPs). In the vast majority of those experiments, visual processing has been examined with large eye movements precluded, by asking participants to hold their gaze still in order to maintain fixation, and to refrain from blinking while the stimuli are flashed near the fovea. To further prohibit saccadic eye movements, visual stimuli are often presented for a short duration within the saccadic reaction time or at a small stimulus size.However, the practice of precluding oculomotor activity in most electroencephalographic (EEG) studies eliminates a major component of visual information processing. It has long been established that the eyes produce a continual stream of saccadic eye movements to bring the fovea to bear on aspects of interest in the visual scene. In addition, confining experiments to conditions without eye movements cannot elucidate the cortical mechanisms involved in control of the eye movements themselves.To investigate the brain activity specific to eye movement control, we need to measure brain responses through EEG signals and eye movements simultaneously. It has been justified that combining EEG and eye tracking is possible . This reliable technique for measuring saccadic eye movements is based on the mechanism that the eyes are the origin of a steady potential field that can be modeled as a dipole with its positive pole at the cornea and negative pole at the retina. Electrodes placed in the periocular region are used to measure these electric potentials. If the eyes move from the center position to a peripheral location, the cornea approaches one electrode while the retina approaches the opposing one. This change in the orientation of the dipole results in a change in the measured EOG signal. The EOG technique has the advantage in concurrent eye tracking that the simultaneous measurements of EEG signals and eye movements by EOG can be achieved by using the same high-density electrode net. This effectively avoids the technical difficulties related to eye-tracker positioning and synchronization between EEG and eye movements.x, y space, or compared them with the errors reported for other techniques.Some previous algorithms have been developed for the detection of saccades in EOG. Niemenlehto  employedThis present study was designed to assess the efficacy EOG signals measured from the high-density EEG electrode net for the accurate analysis of the amplitudes and directions of saccades. This article also provides a sophisticated analysis of the nature of blink responses through detailed measures of EOG signals around eyes.We recruited seven participants for the simultaneous measurement of EEG and EOG , all with normal or corrected-to-normal vision. They were each informed about the purpose of the study and the procedures, and had provided a written consent before the measurements. All procedures conformed to the Declaration of Helsinki and were approved by the Smith-Kettlewell Institutional Review Board.2. A red target (size ~ 0.5°) appeared randomly within this square grid at 25 possible stimulus locations.The stimuli were presented on a 19-in. color monitor  at a viewing distance of 33 cm. A 5×5 stimulus array was configured in a square region of 34° × 34°, subtended in the center of a dark screen with a background luminance of 3.06 cd/msaccadic overlap paradigm), the preceding target stayed on for another 200 ms after the next target had appeared. In the other condition (saccadic gap paradigm), instead of both staying on, the first target was removed 200 ms before the appearance of the second target. The saccade latency is typically about 100 ms longer for the overlap than for the gap paradigm. The experimental participants were required to saccade to each target location and to maintain fixation until the next target appeared. A chin-rest was used to stabilize the head throughout the experiments. In both conditions, the choice of sequential locations was randomized across the array, subject to the constraint that the length of each saccade in terms of combined vector was limited to no more than 24° (to avoid nonlinearities of the trajectory reconstruction beyond that range). Thus, the largest saccade was equal to the length from the center to a corner of the stimulus array. Each experimental block consisted of 96 saccade trials. The participants were allowed a 5-min break after each block presentation, to relieve any peri-ocular muscle fatigue.The participants performed the fixation task illustrated in Fig. EEG data were recorded using an EGI Geodesic Netstation high-density, whole-head recording system , which incorporates 128 electrodes distributed around the head and face, in addition to the scalp electrodes. The impedance of all electrodes was maintained below 60 kΩ according to the recommended value for this system. The data were recorded at a sampling rate of 500 Hz and band-pass filtered between 0.01 and 250 Hz. The recorded signals for all electrodes were referenced to the vertex electrode (Cz).Eye movements were recorded through the measurement of electric potentials (EOG) using the Netstation electrodes located around the eyes, which included channels appropriate for recording vertical and horizontal EOGs. The vertical EOG for each eye was derived from the differential signal between electrodes located in the infraorbital ridge of the eye and the one immediately above the eye , and the horizontal EOG was derived from the differential signal between electrodes located in the middle of both eyes (#17) and the one near the outer canthus of each eye (#1 for the right eye and #32 for the left eye).All recorded EEG data were preprocessed and analyzed in Matlab . Electrical mains 60 Hz and monitor refresh 100-Hz pickup were first removed by an innovative frequency splatter filtering procedure  over all the artifact-free epochs. To define the saccade onset, the criterion time point for the detected saccade was backdated to a time point of 25 samples (50 ms) before the currently inspected point. Once the onset of the saccade was found, the EOG data were resegmented into the range from – 400 to 400 ms relative to the saccade onset for the detection of saccade offsets, as follows: The offsets of saccades were obtained through the velocity function of the normalized vector sum of the horizontal and vertical EOG signal amplitudes from – 400 to 400 ms relative to the onset of saccade. The saccade offset was then defined as the first point at which the value of the velocity function fell to less than 15% of its maximum velocity.The custom software for saccade detection with a target cue operates as follows. A recording of combined EOG signals of horizontal and vertical EOG were processed continually throughout each epoch. The onset of saccades was specified analytically as any point of significant deviation from the local noise level, as defined by the running average of the standard deviation (RASD) of the combined EOG signal amplitude over the previous 500 ms , without respect to the target presentation. First, the whole block was divided into intervals of 100 samples (200 ms). The presence of a saccade was assumed to generate an increase in the Sensitivity was specified as the proportion of saccades detected with this uncued procedure in an experimental run that actually contained cued saccades to provide the ground truth for the saccade occurrence. Specificity was defined as the proportion of correct rejections provided by the same procedure applied to intertarget intervals that did not contain saccade .The effectiveness of the uncued procedure was assessed in terms of its sensitivity and specificity. The horizontal and vertical saccade amplitudes were calibrated with a three-parameter affine scaling function of both the horizontal and vertical EOG signals, based on the target locations. The saccade amplitudes were derived from the EOG signals for the differences in voltage between the onsets and offsets of the saccades (obtained as above). There were separate sets of slope and intercept scaling factors for the horizontal and vertical affine scaling functions. The three parameters for each of horizontal and vertical EOG scaling functions are dominant slope, nondominant slope and intercept. Therefore, both the horizontal and vertical EOG signals can be represented as follows:a and b are the parameters representing two slopes, and c is a parameter representing the intercept. ∆X and ∆Y are the differential target displacements along the x- and the y-axis, respectively. Whether a or b is the dominant slope depends on the direction being calibrated. When calibrating horizontal EOG amplitude, a is the dominant slope, whereas b is the dominant slope for vertical EOG calibration. Only the dominant slope and intercept were maintained as scaling factors, such that horizontal EOG signals can be expressed on the basis of the target locations, as follows:where ah and ch are a set of slope and intercept scaling factors for this horizontal affine scaling function. The vertical EOG signals can be expressed as follows:where bv and cv is a set of slope and intercept scaling factors for this vertical affine scaling function. With regard to the stability of the calibration over time, these affine calibration expressions were assumed to be specific to the electrode locations. Therefore, in common with typical eye movement calibration procedures, the affine transformation was invariant over time once established for a given set of electrode placements.where n orthogonal vectors characterizing the input EEG data matrix, where n is the number of parallel signal vector inputs (matrix rows). To distinguish the directions of the saccades for the PCA analyses, all the saccades were separated into four direction sectors according to their closest adjacent cardinal direction  before applying the PCA. For each participant individually, the EEG amplitudes were averaged in each sector, yielding four 2-D time-by-electrode matrices. To get each principal component, the four two-dimensional (2-D) time-by-electrode matrices for the four conditions were concatenated in the second (electrode) dimension to get an omnibus 2-D matrix. A full svd on this omnibus 2-D matrix yielded orthogonal temporal component waveforms and spatial distributions. Only the primary temporal component (eigenvector) and spatial distribution were retained from the first svd of the concatenated the data, the rest of the temporal components obtained being discarded. This first eigenvector, weighted by its eigenvalue, was then subtracted from all the data and a second svd was implemented on the remaining data to yield further temporal PCA component waveforms and spatial distributions. Again, only the first primary temporal component and spatial distribution were retained to form the second iterative principal component. A series of sequential svd was performed, each on a remaining data, until eight iterative components were reached. This whole process was called iterative PCA (iPCA). It had the advantage over the classic single PCA that the constraint of orthogonality among the produced principal components by a single PCA was eliminated by the series of iterative PCA, thus providing the capability of revealing more physiological components of the eye movements and cortical process. In addition, multiple secondary peaks in an extracted component waveform might be eliminated through the iterations of PCA, which is easier for component interpretation.To identify eye movement control components from the data, we performed an iterative form of PCA on the matrix of saccade-defined epochs to avoid the orthogonality constraint, which is unlikely to represent the physiological organization of the EEG responses. PCA in general is a blind source separation algorithm that enables the separation of statistically distinguishable sources from multidimensional data, and was implemented using the singular value decomposition (svd) function in MATLAB. In its full implementation, svd derives the set of μV) positive deflections above the eyes , negative deflections with a smaller amplitude (~20 μV) below the eyes (#126 and #127), and almost zero deflections at the temples (#1 and #32). The question of what electrical source would give rise to this pattern of signal changes during the blink action of closing the eyelids to cover the eyeballs is addressed in the Discussion.During the EEG recording, large deflections were easily seen at the vertical EOG electrode sites  whenever blinks occurred. Therefore, to determine the nature of the blink artifacts, we added a short EEG recording with one participant to examine the blink potentials. During a 2-s EEG recording, the participant was asked to blink as fast as possible, which provided a frequency of ~ 8 Hz. Figure The electrical activity changes generated by saccadic eye movements were recorded from electrodes in the periocular regions. Due to the standing corneo-retinal dipole charge of the eyeball, a change in the orientation of the eyeball generated corresponding electrical changes at the electrodes. Figure The differential horizontal and vertical EOG signals, respectively, for the overlap paradigm caused by saccadic eye movements with arbitrary directions are shown in Figs. The overall goal of the methodology was to be able to detect saccades occurring in any direction. The first step in defining the occurrence of the recorded saccades with a target cue was to combine the horizontal and vertical saccade traces to provide for detection of the onsets of the eye movements. The combined EOG signals are a vector sum of baseline-corrected horizontal and vertical EOG signals, as follows:t test of saccadic reaction times between the overlap and gap paradigms across seven participants, which revealed that the difference in saccadic reaction time between the overlap and gap paradigms was statistically significant .The amplitudes of the combined EOG signals were then used to detect saccades with a custom algorithm (see the Cued Saccade Detection section). The blink-free EOG waveforms after saccade detection are shown in Fig. We also noticed that there was a presaccadic blip in the EOG signals  from the procedure for calibration of the EOG signals to the target locations should be factored. Therefore, the vector combined saccade amplitudes can be expressed as follows:ah and ch are the dominant slope and intercept of the three-parameter affine scaling function for the horizontal prediction, and bv and cv are those for the vertical prediction.where To visualize the results of the calibration, the randomized saccade trajectories obtained from the calibration are shown in a 5×5 stimulus array. Figures The average errors of these calibrated EOG signals across the 34° field of that calibration targets can also be shown as heat maps of the error distributions Fig. , illustrApplying an iterative form of PCA  on the saccade-defined whole-head EEG data revealed a set of meaningful, dissociable response components in the saccade-triggered potentials, shown in Fig. The novel approach to the accurate recording of eye movements developed in this article is to record EOG simultaneously with scalp EEG recording, to provide an accurate and convenient means of eye tracking without specialized eye-tracking equipment. Two interesting issues are discussed here. One is to compare the accuracy and linearity of EEG/EOG measurement with those of conventional video-based eye-tracking systems. The other is to consider the nature of the blink response measured by the EOG.There is a general trend toward combining eye-tracking capability with other types of brain imaging, such as functional magnetic resonance imaging and EEG. The use of dedicated eye trackers in this situation is both cumbersome and unreliable, since they can take a while to set up and do not work for all eye configurations. Moreover, they are particularly challenging for use with those who wear spectacles or low-vision aids. In the case of EEG recording of brain activity, it is greatly advantageous to have a technique for recording eye movements that does not require the use of additional equipment, but is derived directly from the same EEG electrode net as the brain signals. The combined EEG/EOG approach will work for any eye, regardless of color or optical quality, and in combination of the regular refractive devices or optical aids.As the calibrated saccadic direction vector results show, the EOG signals recorded simultaneously with EEG by using the high-density electrode net can attain results as accurate as many video-based eye tracking devices. The average angular accuracy reported across an array of detection techniques used for various types of eye tracking was 1.56° ± 0.23° . In the data from Fig. μV, implying eye rotations of up to ~ 30° during free blinks.One possible explanation for these results is that the assumption that the eyes are stationary during rapid blinks is incorrect. In principle, if the eyes moved upward to a sufficient extent during the blinks, they could invert the infraorbital signal while more than doubling the forehead signal. According to the calibration data of Fig. There are three lines of reasoning against this interpretation, however.The participant in the blink control study in Fig. Riggs, Kelly, Manning, and Moore , p.334 fAccording to the difference in the time courses of the eye rotation signals and the more rapid blink signals reported by Riggs et al. , the blidifferential insulation/conduction relationships according to the composition of the eyelids. If the skin operates as an electrical insulator, it could account for the reduction in the cheek signal with closure of the lower lid. If, on the other hand, the levator muscle underlying the upper lid acted as an electrical conductor, it could account for the large increase in the forehead signal, sufficient to overcome any insulator effects. We conclude that the muscle acts as an electrical conductor and the skin acts as an electrical insulator to produce the variety of blink responses that we see from the facial electrode derivations.Given this analysis, the only plausible explanation for the variations in sign of the eye blink responses is that there are The present eye-tracking capability using EOG signals from a high-density electrode net verifies that EOG can be used for simultaneous measurements of brain responses and eye movements. This capability will be particularly useful for measuring fixation- or saccade-related EEG potentials in order to examine the neural mechanisms involved in the control of eye movements, without the difficulties of synchronization between separate EEG and video-based eye-tracking devices. Further studies aiming to determine fixation- or saccade-related potentials under free-viewing conditions could be based on the present results, further validating the accuracy of calibrated EOG signals for 2-D eye tracking.


One of the lines of research on organic devices is focused on their miniaturization to obtain denser and faster electronic circuits. The challenge is to build devices adding atom by atom or molecule by molecule until the desired structures are achieved. To do this job, techniques able to see and manipulate matter at this scale are needed. Scanning tunneling microscopy (STM) has been the selected technique by scientists to develop smart and functional unimolecular devices. This review article compiles the latest developments in this field giving examples of supramolecular systems monitored and fabricated at the molecular scale by bottom-up approaches using STM at the solid/liquid interface. The development of functional and smart organic materials for optoelectronics has been a challenge for researchers in the field of nanoengineering. The interest lies in the possibility of designing and developing materials with specific functionalities and integrating them in electronic devices, such as sensors , light-eA molecular device is an assembly of molecular elements designed to behave in a certain manner, operating in a specific capacity and developed for a precise function. Each element contributes with a single function cooperating for the performance of the complex molecular device. The individual components can operate as a result of an external stimulus and can be easily replaced by other elements to accomplish a desired function. Each element can be individually stimulated as a result of an external source. This idea was introduced for the first time by Drexler . He propThe development of materials at the molecular level requires specific fabrication techniques that allow visualization and manipulation of matter at this scale. This is the point that this review article aims to address, showing how molecular devices can be fabricated using a bottom-up approach based on atom-by-atom or molecule-by-molecule assembly until a functional and complex structure is achieved. Scanning Tunneling Microscopy (STM) is the elected technique to play with matter at the atomic level. Namely, STM has been used to monitor in real-time formation of highly organized self-assembled monolayers where the molecules arrange themselves into packed 2D crystals fully covering the surface of the substrate onto which they are deposited. Several high-resolution STM images of the formation of molecular systems by self-assembly have been reported revealing the parameters involved in the monolayer formation: whether they are physical parameters  or chemical parameters . All these monolayer properties are important for the design of organic devices.Recently, STM has been used to build molecular systems with multicomponents, e.g., self-assembled monolayers with more than one molecular element or vertical supramolecular structures synthesized in situ ,8,15,16.Conducting organic self-assembled and self-organized monolayers are the starting point of an organic device fabrication. They are spontaneously formed on a substrate, usually on a conducting substrate, and they can be composed of one or more than one type of molecules, depending on the aimed application. Due to the high reproducibility of this technique, it is easy to use self-assembled materials in the development of large-scale devices. The molecular ordering is strongly dependent on the intra- and intermolecular interactions, temperature, concentration, solvent and on the chemical/physical interactions with the substrate  or on the molecular recognition . The porous array can be modified for specific purposes using rational molecular design. Two methodologies are available for the fabrication of nanoporous surfaces depending on the type of building blocks: intrinsic and extrinsic ,40. NanoNanoporous monolayers developed with extrinsic building blocks depend on the self-assembly of acyclic molecules that together form extrinsic pores. Among the most used extrinsic building blocks we find trimesic acid (TMA) ,42, dehyThe solvent can play an important role in the formation of nanoporous networks. Lackinger et al. ,42 demonA second class of extrinsic building blocks are alkyl-/alkoxy-hexadehydrotribenzo[12]annulene (DBA) molecules . STM images of a self-assembled porphyrins monolayer on Cu(111) showed different molecular conformations of porphyrins as a function of the applied tip-voltage. Recently, Ferreira and co-workers  reportedThe pyrrole groups of brighter lines are in Trans conformation and the darker lines correspond to a Cis conformation. When the tip-voltage increases to 0.7 V the Trans molecules switch to a Cis conformation and the monolayer becomes homogeneous as it is indicated by the line profile of Movement is essential to life, may it be in the form of transport, translocation, contraction, or others, since ultimately biological systems rely directly or indirectly on movement. No wonder molecular motors, which have the ability of powering movement, are intrinsic to nature. Molecular motors are commonly seen in nature as, for example, in adenosine triphosphate synthase, myosin, kinesin, and dynein. These are motor proteins that use chemical energy to attain directional movement. Inspired by these biological machines, molecules and molecular complexes that can be assembled to form rotors and motors have been designed and fabricated at the nanoscale to simulate biological functions. A motor is commonly defined as an energy converter system, i.e., a system capable of converting one type of energy—e.g., electromagnetic, chemical, electrical—into kinetic energy. Specifically, a rotary motor can convert this energy into unidirectional rotary movement in a controlled manner. Rotary molecular motors are commonly composed of three components: the rotor, the axle and the stator ,67,68,69eactions ,71.In the field of molecular motors, the focus has been on studies at room temperature and some important contributions have been published with dynamic molecular systems under these conditions ,73,74,75The development of molecular motors needs to overcome various barriers. Apart from their stability at room temperature, the focus is to develop fast machines able to “walk” over longer distances. To explore how fast and how far a nanomotor can travel, the first world-wide nanocar competing event took place using the STM to control and monitor the results ,77,78. TThe first definition of a molecular wire (MW) was given by the winner of the Nobel Prize in Chemistry Jean-Marie Lehn in 1988, that defined this structure as a conducting chain of a molecule or a supramolecule which conducts electrons between two electrodes . The MW The conductance measurements at single molecule level are experimentally challenging and each system needs specific experimental protocols. The STM BJ experiments seem to be perfect taking into account that the electrodes make a contact with the molecular system. However, it is difficult to know how this affects the structure and the vibrational properties of the structure that is being analyzed.Scanning Tunneling Spectroscopy (STS) is another technique to measure electrical properties of individual wires. Its main limitation is the absence of contact between the tip and the MW that compromises the accuracy of the measurements. However, STS can be supported by STM images that can be acquired simultaneously with current measurements. This allows the precise localization of MWs ensuring that the tip is analyzing the desired structure. The most reported works about MWs are related to supramolecular structures previously synthesized in laboratory or locally synthesized in high vacuum conditions .A recent work  shows hoThe imaging allows the positioning the tip at the top of a single wire. At this moment, the individual current is measured by the application of a bias voltage between the tip and the substrate. The characterization of the conduction mechanisms is identified analyzing the evolution of the MW electrical resistance as a function of the molecular length, associating a tunneling mechanism for shorter wires with 4–5 nm and a hopping mechanism for longer wires. This work can be taken as a model to build similar supramolecular structures synthesized in situ. Each step can be controlled by imaging with molecular resolution while measuring, at the same time, the individual electrical transport.The described stepwise method opens the way for the design of a variety of MWs by changing the building blocks to optimize the conductive properties.One of the questions that we may ask when looking at the developments of unimolecular devices is “when will they be integrated in electronic circuits?” The answer can be foreseen by the fast evolution in this research field covering a wide range of applications and prototypes which are already working ,86,87,88to create unimolecular systems stable at room temperatures;to develop encapsulation methods while maintaining the integrity of the organic device.Besides the interest in biological systems, there are other challenges to overcome:A recent work, shows how it is possible to build flexible MWs stable at room temperature . MWs witThe stability of an organic device also depends on the encapsulation and this research is still at the beginning. All devices need to be encapsulated to be protected from corrosion and to avoid degradation in the presence of air. L. Moro et al. published a recent article in which he gives an overview of the requirements to develop encapsulation methods for organic devices . This isAt the moment, the main concern of scientists is to integrate the single molecule device in a final electronic circuit. To achieve this goal, it is necessary to overcome important constraints: long-term stability, biocompatibility, and environment effects.


In the experiment of inertial confinement fusion, soft X-ray spectrum unfolding can provide important information to optimize the design of the laser and target. As the laser beams increase, there are limited locations for installing detection channels to obtain measurements, and the soft X-ray spectrum can be difficult to recover. In this paper, a novel recovery method of soft X-ray spectrum unfolding based on compressive sensing is proposed, in which (1) the spectrum recovery is formulated as a problem of accurate signal recovery from very few measurements , and (2) the proper basis atoms are selected adaptively over a Legendre orthogonal basis dictionary with a large size and Lasso regression in the sense of ℓ1 norm, which enables the spectrum to be accurately recovered with little measured data from the limited detection channels. Finally, the presented approach is validated with experimental data. The results show that it can still achieve comparable accuracy from only 8 spectrometer detection channels as it has previously done from 14 detection channels. This means that the presented approach is capable of recovering spectrum from the data of limited detection channels, and it can be used to save more space for other detectors. Investigation of laser-driven inertial confinement fusion (ICF) is of great significance in the search for the ideal energy resource, as well as the application of fundamental scientific research . ICF is The solution of the unfolding problem is an ever-present issue in X-ray spectrometry. To recover the original spectrum it is necessary to use the detector response function, by solving the so called inverse problem . There aThe maximum entropy unfolding technique  solves tOne technique is selecting the reasonable basis function to reconstruct the spectrum. The response function , Piece-wThe other technique is choosing an appropriate calculation method to determine coefficients of the basis function. The following calculation methods are commonly used at present: Iteration based on maximum entropy ,15,16, tThese methods may need more measurement data to achieve good recovery performance for a spectrum. Nevertheless, with the laser beams increasing, it is too difficult to obtain more detection channel data with the soft X-ray spectrometer, which yields some difficulties in a practical ICF facility. Since there is a lot of other physical information that needs measured to research the soft X-ray, including the temperature and radiation, the possible install positons and the opening of the center target for spectrometers is limited, and their orientation also leads them to interfere with each other. It is hard to add detection channels to achieve good performance in recovering the spectrum. Thus, it is necessary to find a new recovery method to unfold a spectrum with finite detection channels.(1)The spectrum recovery is formulated as a problem of an accurate signal recovering from a few measurements , which applies the measurement matrix to convert the spectrum signal into a voltage signal, and enables the signal be reconstructed with a small amount of measured data.(2)The proper basis atoms are selected adaptively over the Legendre orthogonal basis dictionary with a large size and Lasso regression in the sense of the ℓ1 norm, which enables the spectrum to be recovered with high accuracy from the small amount of measured data of the limited detection channels.(3)By employing this method, since the soft X-ray spectrometer may recover the spectrum with limited detection channel data, it provides the possibility of saving space for other detectors.Compressive sensing (CS), proposed by Donoho and Candès , is a neThe rest of this paper is organized as follows. In the next section, we introduce the soft X-ray spectrometer and the previous principle of the energy spectrum recovery with multi-channels, followed by a brief review of compressive sensing theory. In this section, we first present the soft X-ray spectrometer in the ICF experiment, including its structure, operating principle, and drawbacks in the process of spectrum unfolding. Then we demonstrate compressive sensing theory through discussion of sparse representation, the measurement matrix, and sparse coefficients reconstruction.In indirect-drive inertial confinement fusion (ICF) experiments, high power laser beams irradiate the high-Z inner wall of the hohlraum and the energy is converted into soft X-ray radiation, which is used to drive the capsule located inside the hohlraum to implode, or to irradiate a package of materials to study their properties ,22,23. IThe soft X-ray spectrometer works using the filter method shown in Taking a 14 channel spectrometer as an example, the detecting response is shown in In The recovery method  based onThe purpose of the diagnosis is to recover the radiation spectrum Let The coefficient vector To clearly comprehend this, we supposed that according to the response data from 14 detection channels, which is shown in Owing to the significance of compressive sensing, a method to reconstruct signals from significantly fewer sampling points than traditional methods is required. We present a novel spectrum unfolding method based on compressive sensing, which may enable basis atoms to be selected adaptively over a large size of basis dictionary with high-order basis atoms, and the spectrometer may achieve high accuracy spectral information from limited detection channels.Compressive Sensing, also known as compressive sampling, is a novel sensing paradigm that is widely used in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use . To be mThe theory of sparse representation was first proposed in signal field and later widely applied to solve undetermined equations . It reveGiven the set of sampling points Supposing a measurement matrix . Y∈ℝm×1  under twThe restricted isometry constant nd Wakin ; that ised basis .Since the dictionary irculant , Toeplitirculant , Chirp sirculant , GaussiaIn order to recover the function with limited sampling points, we must seek the sparse representation over the dictionary. The sparse coefficient The above problem is called the category , and Concategory  and Orthcategory , among ocategory  can balacategory . Convex category  and Lasscategory , have anTo make this possible, Lasso regression relaxes the ℓ0 norm to the ℓ1 norm.The ℓ1 norm question is a convex problem which has the most similarity to the ℓ0 norm. It can be written as Lagrange multiplier:The last equation is the famous Lasso (least absolute shrinkage and selection operator) model, which consists of a least square estimate and a ℓ1 norm penalty.Where http://www2.imm.dtu.dk/projects/spasm/.There are a lot of operators for solving the Lasso problem, such as quadratic programming  and the In this section, spectrum recovery is formulated as a problem of accurate signal recovery from a few measurements; that is, compressive sensing. We will detail that the proper basis atoms are selected adaptively over a Legendre orthogonal basis dictionary with a large size and Lasso regression in the sense of the ℓ1 norm, which enables the spectrum to be recovered with high accuracy from the minimal measured data of the limited detection channels. The entire process of the spectrum unfolding method based on compressive sensing is given in The spectrum signal In the process of sparse representation of the spectrum, we prefer to choose the basis function which has orthogonal properties, like Legendre polynomials. The Legendre orthogonal basis function is good at expressing the spectral curve with high-order basis atoms, and it is easily constructed and calculated. By measuring with the sparse representation of the signal on the orthogonal basis, a few coefficients can be used to present spectrum unfolding. In addition, the Legendre polynomial is orthogonal with respect to the ℓ2 norm, which means in the interval , the energy range should be transferred into  in advance.The Legendre polynomials can be determined by the recursive definition. Suppose that The basis function of Legendre orthogonal polynomials can be defined asGiven the sampling points of photon energy, the sparse basis matrix The basis dictionary can be generated with a large number of basis atoms. Due to high order expansion on the Legendre basis function and a plentiful basis dictionary, the proper basis atoms can be selected adaptively, and higher accuracy spectral information may be achieved.The measurement matrix After the measurement matrix To make it clearer, we supposed that according to the response data from 14 detection channels, which are shown in eference ; i.e., lFrom Equation (17), the work recovering the spectrum is turned to solving the problemEquation (18) is an NP hard problem, which has many groups of uncertain solutions. According to the brief of Due to the sensing matrix on (LAR)  is emploon (AIC)  is appliSubsequently, the best coefficient vector Finally, the best recovery spectrum The process of the proposed spectrum unfolding method based on compressive sensing is shown in In this section, we provide two groups of contrasting experiments to prove that this novel method enables accurate signal recovery from a few measurements.To verify the spectrum unfolding method, we conducted self-inspection of numerical experiments. In order to imitate the real experiment, we input the known spectrum In addition to the self-inspection of numerical experiments, we also reproduced the experiment of the recovery performance with different spectrum unfolding methods, in which both the Piece-wise B-spline and Gaussian Bump methods were used in past research. We contrasted these two methods with the compressive sensing method in recovery performance. Furthermore, in the experiment we also explored the accuracy of the three methods with a small amount of measured data from the limited detection channels of the soft X-ray spectrometer.The recovery error is a metric to evaluate the error between the original spectrum signal and the recovered one. In order to calculate the recovery error, the following formulas were used in this literary work:1. Root mean squared error (RMSE)Since the actual spectrum of the benchmark functions was known, the actual errors at any points could be computed. The RMSE is given by2. Mean absolute error (MAE)The mean absolute error is given byTo show the robustness of the method, two types spectrum were regarded as the original spectrum; that is, the Plank spectrum and Triple-peak spectrum, which are commonly used in spectrum recovery experiments. In order to see the recovery performance, three methods were applied to contrast these. The first one is proposed in this paper based on the compressive sensing method, the second is the Piece-wise B-spline spectral method, and the third is the Gaussian Bump. The basic parameter settings based on the compressive sensing method were as follows: The photon energy ranged from 50 eV to 6000 eV, the number of sampling points for photon energy was Experimental figures show the spectrum recovery performance with three different spectrum unfolding methods in different recovery spectrum as the measurements decreased. The line of blue stars represents the original spectral signal; that is, the Plank spectrum and Triple-peak spectrum. The other lines in different colors and styles represent the recovery signal based on three different methods, namely the Piece-wise B-spline, Gaussian Bump, and Compressive Sensing. To illustrate the problem more evidently, the recovery error was used to show the recovery performance with different spectrum unfolding methods, and RMSE and MAE were calculated by Equations (25) and (26). The smaller the value was, the better the recovery performance that could be achieved. As shown in The accuracy of the spectrum recovery is reasonable, as the ratio of the error ous work . ∆∁ can Throughout the two groups of experiments, the results showed that the spectrum unfolding method based on compressive sensing has the best recovery performance of these methods, and it can still achieve comparable accuracy from only 8 spectrometer detection channels as it has done from 14 detection channels previously; hence, about 42.86% of the space can be saved.The laser facility for researching Soft X-ray is a complex system, with limited locations for installing detection channels to obtain measurements, and thus the soft X-ray spectrum can be difficult to recover. In this paper, we proposed a novel recovery method for unfolding Soft X-ray spectrum based on compressive sensing. This method has the following characteristics: (1) Spectrum recovery is formulated as a problem of accurate signal recovery from a few measurements ; and (2) we use an orthogonal basis function, such as the Legendre polynomials, to sparsely represent the spectral signals, and the corresponding coefficients can be obtained by Lasso regression in the sense of the ℓ1 norm. Since the basis atoms are selected adaptively over the basis dictionary with a large size, the under-fitting problem with limited detection channels is solved, and the spectrometer can achieve higher accuracy spectral information with less measured data.In order to prove and demonstrate the performance and robustness of the recovery method based on compressive sensing, we conducted self-inspection of the numerical test for recovering two spectra: The Plank spectrum and the Three-peak spectrum. The results show that comparable accuracy can be achieved from only 8 spectrometer detection channels as was previously achieved from 14 detection channels. This means that the presented approach has the capability of recovering a spectrum from limited detection channels data, and it can be used to save more space for other detectors.


EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing model. Our proposed framework considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences. Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The framework was implemented on the DEAP dataset for an emotion recognition experiment, where the mean accuracy of emotion recognition achieved 81.10% in valence and 74.38% in arousal, and the effectiveness of our framework was verified. Our framework exhibited a better performance in emotion recognition using multi-channel EEG than the compared conventional approaches in the experiments. Emotion has a great influence on human cognition   or kNN (k-Nearest Neighbor)  to detect emotions automatically and continuously. The results showed that EEG signals and facial expressions carried adequate information for detecting emotions  approaches for multi-channel EEG processing are popular, especially for artifact removal and source extraction.You et al. presented a method of blind signal separation (BSS) for multi-channel EEG, which combined the Wavelet Transform and ICA together. The high-frequency noises were removed from the collected EEG by using the noise filtering function of wavelet transform, so that the ICA could extract the EEG source signals without regard to the problem of noise separation. The experimental results approved the effectiveness of this method in the BBS of multi-channel EEG  with other preprocessing methods (CSP) find out whether and to what extent spatial filtering of EEG data can improve single trial classification accuracy. The results showed that Informax outperformed the other two ICA algorithms  and defined a low bound of data length for robust separation results. AMICA showed an impressive performance with very short data length but required a lot of running time. FastICA took very little time but required twice the data length of AMICA  network were effective for emotion recognition -based methods. It was capable of providing real-time operations. The method also achieved a satisfactory classification performance , we observe that autoencoder network and linear EEG mixing model have similar expressions. Therefore, we have tried to build and solve the linear EEG mixing model using autoencoder.The purpose of this work is to determine an encoder that allows us to decompose the source signals from the collected EEG signals. To achieve a better performance, an autoencoder is used that consists of multiple layers, called a stacked autoencoder (SAE). The formula of SAE has the same form as the formula of standard autoencoder. The structure and the hyper parameters for the SAE we designed are shown in We assumed that the source signals came from 12 different functional brain regions based on previous research  and was generally used as the optimizer of the neural network. Mini-batch gradient descent randomly selected a mini batch of data to calculate gradient of the loss function at every step, leading to a fast convergence speed and computational efficiency. As for other optimizers, batch gradient descent needed all data samples to calculate the gradient, which was time-consuming and complicated. Stochastic gradient descent used one sample at each step to reduce the computational complexity and improve the speed, but the drawback was related to its instability and possibility in causing fluctuations. Adam optimizer was faster than SGD and exhibited the advantage of adaptive learning rate, although it might have a convergence problem due to the unstable learning rate  was used as the loss function. Then, the training data is fed into the model, where the adjusted R-squared between the test data and its reconstructed data is calculated to validate the model. The expression of the adjusted R-square is shown in (3).R2 is the sample R-square and p is the number of predictors. The value of P was set to 32 in this work since the channel number of EEG signals is 32 in the database of DEAP. N is the total sample size. The expression of R2 is given in (4).Where When the training data  were fed into the model, the adjusted R-square between the test data and the reconstructed data was calculated to validate the model. Once the adjusted R-square exceeded 0.9, it meant the “code” output by the encoder of our model almost retained all the information of the source EEG signals. In other words, the “code” could represent the EEG source signals successfully and the decomposition was done successfully. The process of EEG source signal extraction is shown in As a central nervous physiological signal, EEG signal is non-stationary and chaotic. To facilitate signal processing, the EEG signals are always divided into short time frames and it is assumed that the signal within the frame is stationary  and spontaneous electrical activity signals. Many studies  is applied to do the emotion classification, which is an improvement on the Recurrent Neural Network (RNN). RNN has the problem of long-term dependencies  was applied as the loss function.The LSTM model was a supervised model. Its time step was set to 125, as 125 data segments were achieved under the conditions whereby each EEG data in DEAP had a length of 63 s, and a 1 s time window with 0.5 s step size was adopted. The hidden layer of LSTM had 125 units, which was tuned by the reconstruction error.The proposed framework could effectively solve the over-fitting issue. The SAE model was trained using the reconstruction error, and the sparse and penalty constraints were added to avoid the over-fitting problem. In the training of the LSTM+FC model, three aspects of work had been conducted to handle the over-fitting/over-training issues: (1) A 1 s window with 50% overlap was applied to the EEG data segmentation, which augmented the size of data samples and guaranteed the amount of data used in model training. (2) “Dropout” operations were added in the training of the LSTM and full-connect layers to avoid overfitting, which can be seen in To show how our proposed method handled the data during network training, the training procedure was visualized by plotting the feature clustering in different epochs. In The effectiveness of our framework was evaluated on the DEAP dataset. At first, we compared our framework with other methods on a trial-oriented emotion recognition task. Then three experiment settings were designed to verify the validity of LSTM and SAE in an emotion recognition task using different EEG features.We used the EEG data from the DEAP dataset to validate our framework  and arousal (74.38%). The reason might be as follows. Chen et al. used HMM to build the relationship between the present and previous emotion states , it can generally be concluded the emotion recognition rate of our method was significantly higher than other methods by using different EEG features in both valence and arousal.The values in p < 0.01) higher in both valence and arousal, which proves that exploring the correlations in the EEG feature sequence was more effective than merely integrating the recognition result of each EEG feature frame.Compared with SVM, the emotion recognition accuracy of LSTM was significantly .In the first part, we compared the classification performances among methods with EEG decomposition via SAE or ICA or without EEG decomposition. The results in Using SAE for EEG source signal decomposition, in fact, was to encode the EEG channel. The spatial characteristics of the EEG signal, in other words, EEG channel correlations, were also extracted at this time, which was the reason why using EEG source signals could improve the emotion recognition accuracy.EEG source signal decomposition was an important step in our framework, which took extra time cost. Luckily, using SAE for EEG source signal decomposition would reduce the number of channels of EEG signals that need to be processed, and saved time for feature extraction.In the second part, we counted the total computation time of EEG source signal decomposition and feature extractions, as can be seen in According to the experimental results, using SAE for EEG source signal decomposition could improve the emotion recognition accuracy while ensuring fast recognition speed.The number of parameters in both the SAE and LSTM models were recorded, where the values were 2040376 and 5804 for LSTM and SAE, respectively. We tried to estimate the parameter number of other models in the relevant literature. However, many of them did not provide the whole parameter settings, especially the parameters regarding hidden layers, so the numbers could not be calculated, and hence a comprehensive comparison on the computational complexity using the number of parameters has not been achieved.The third experiment was designed to compare the performance of FBP and PCC in our new framework. The experiment settings and results are illustrated in p < 0.01). The reason may be that FBP is a frequency-domain feature while PCC is a spatial-domain feature. Combining EEG source signal with FBP, “SAE+FBP" can reflect the features of EEG in the spatial-frequency domain, like DASM feature and RASM feature  (Orgo et al., In this work, we focused on valence and arousal based on the literature (Chen et al., In this paper, we present a novel emotion recognition framework consisting of a linear EEG mixing model and an emotion timing model. The SAE-based linear EEG mixing model can be used for decomposition of EEG source signals and extracting EEG channel correlations, and it can also improve computation efficiency in feature extraction and upgrade the emotion recognition performance. The emotion timing model is simulated by LSTM, which increased the recognition accuracy by exploring the context correlations of the EEG feature sequence. The comparison results in our experiment approved the effectiveness of our framework, especially in the valence recognition task. This work can promote the development of brain-inspired robots, especially in human-robot interaction.XfX, ZL, and LS designed the framework, conducted experiments and wrote the manuscript; ZL and TX carried out experiments; XfX, BH, and XmX analyzed the results and presented the discussion and conclusion parts.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Unlike most excitable cells, certain syncytial smooth muscle cells are known to exhibit spontaneous action potentials of varying shapes and sizes. These differences in shape are observed even in electrophysiological recordings obtained from a single cell. The origin and physiological relevance of this phenomenon are currently unclear. The study presented here aims to test the hypothesis that the syncytial nature of the detrusor smooth muscle tissue contributes to the variations in the action potential profile by influencing the superposition of the passive and active signals. Data extracted from experimental recordings have been compared with those obtained through simulations. The feature correlation studies on action potentials obtained from the experimental recordings suggest the underlying presence of passive signals, called spontaneous excitatory junction potentials (sEJPs). Through simulations, we are able to demonstrate that the syncytial organization of the cells, and the variable superposition of the sEJPs with the “native action potential”, contribute to the diversity in the action potential profiles exhibited. It could also be inferred that the fraction of the propagated action potentials is very low in the detrusor. It is proposed that objective measurements of spontaneous action potential profiles can lead to a better understanding of bladder physiology and pathology. Intuitively, there could be only two possibilities for obtaining APs of different profiles: (i) multiple AP generation mechanisms, i.e., differences in ion channel compositions between cells, or (ii) a single AP generation mechanism with variable modulation due to factors linked to syncytial function, including innervation. A preliminary study demonstrated the origin of different AP shapes from a single basic underlying mechanism  shapes but, generally, individual cells from the same tissue, or region of tissue, tend to display a common AP shape, characteristic of that cell type. The smooth muscle layer of the urinary bladder wall (detrusor) contrasts in this regard; it is found to exhibit APs of widely varying temporal profiles , also involving the complex cellular environment. Changes in the underlying factors could potentially result in pathology such as the overactive bladder  at a constant flow rate (100 ml/h), maintaining a pH of 7.2–7.3  to the top of the dome (anterior). Tissue strips, containing a few bundles of smooth muscle, 3–4 mm long and 1–2 mm wide, were cut. Strips were pinned out on a Sylgard-lined plate at the bottom of a shallow chamber , which was mounted on the stage of an upright microscope. Preparations were superfused with warmed (35°C) physiological saline solution (PSS) . Changes in membrane potential were recorded using a high input impedance amplifier , digitized using PowerLab/4SP  at either 1 or 4 kHz, and stored on computer for later analysis.A previously established three-dimensional passive model of smooth muscle syncytium  was evaluated to test the correlation between any two parameters of interest. In case of experimental recordings, the r-value was calculated for individual cells, and the confidence of the correlation trends are measured using Student's one sample t-test.Pearson's correlation coefficient  measured from the resting membrane potential (RMP) to the peak of the APWidth : Time (in ms) measured between the crossing of half the AP height across the rising and falling phases of the APConvexity: Provides a quantitative measure of the AP foot convexity; the foot of the AP being defined as the phase from the initiation of membrane depolarization to the AP threshold. The foot contributes in moving Vm from its resting level toward the thresholdAfter-Hyperpolarization (AHP): Difference (in mV) measured between the RMP and the first local minimum following the AP peakAfter-Depolarization (ADP): Difference (in mV) measured between the first local maximum following the AHP and the RMPCX, Y indicating the measure of AP foot Convexity evaluated for a depolarization of Y mV over a time frame of X ms, where X and Y define the line. The values of X and Y for this study were selected in accordance with the requirements of this method , (ii) Convexity and AHP , (iii) Convexity and ADP , and (iv) AHP and ADP . These correlations for all the 11 cells are shown in Table The features, previously described in section 2.4, were determined for each of the APs. Once the features were obtained, the Pearson correlation coefficients between the features were calculated. Correlations were deemed statistically significant if the 99% confidence interval excluded zero. Strong correlations were found to exist between: (i) Height and Width , signifying that the APs are independent, stochastic events. The occurrence of any AP does not influence the occurrence or non-occurrence of others APs.The Inter-Event Interval (IEI) histogram was computed for the collection of APs from each of the cells. One such example is shown in Figure To analyze action potential shapes computationally using our model, we (i) stimulated the centroid cell in our syncytium and recorded the APs generated at each cell in the syncytium for different sizes of syncytium, and (ii) stimulated each cell in a 5-cube syncytium, one after another, and recorded the APs recorded at specific locations within the syncytium. This was an extension of the simulations undertaken previously  show significant variations in correlation values, even displaying a reversal of the sign of correlation coefficient for very low values of Ggap.In one such study, the strength of gap junctional conductance, Another study involving a heterogeneous variation in the gap junction coupling is described in the section In the current study we have restricted our focus to spontaneous action potentials. The origin of these signals is still open to debate. Previous studies have indicated that most of the APs observed under resting conditions are driven by sEJPs .Our interpretation is that all the APs observed in the experimental recordings are either locally generated within the cell, by neurotransmitter release from a nearby varicosity, or propagated to it from such a cell in very close vicinity. This release of neurotransmitter would not be nerve-evoked, but arise from the asynchronous release of neurotransmitter packets from the nerve terminals. This random nature of occurrence of APs is confirmed by the exponential trend of the Inter-Event Interval (IEI) histogram. If such is the case, then to explain the wide variety of shapes observed from the intracellular recordings, and their high frequency of occurrence, it is expected that the cells are densely innervated. Studies have shown that the detrusor tissue is densely innervated Gabella, , 2003, wAlso, passive signals decay quickly with distance, and thus they cannot be recorded in distant cells. The sEJPs can be detected only at cells in the immediate vicinity, as reported in our earlier work  and defines a voltage-gated channel mechanism for each. It should be borne in mind that the detrusor smooth muscle system involves several ionic species with a wide ensemble of ionic channels. These include both voltage-gated  as well as ligand-gated  channels. Moreover, the intracellular Ca2+ dynamics holds potential to exert a consequential influence over the functioning of these channels. Thus, there potentially exists an even larger scope for diversity in the AP profiles observed in the detrusor.As stated earlier, we have opted for a simpler and well-understood AP model in the current study. The HH model employed here basically considers two ionic species  covering the Department of Pharmacology, University of Oxford, approved and had oversight of all animal experiments. All experiments were carried out during or before 2009, and were hence approved under Animals (Scientific Procedures) Act 1986, but are also consistent with both UK Animals (Scientific Procedures) Act (2013) and European Communities Council Directive 2010/63/EU.SA designed the computational studies, performed the simulations, and wrote the first draft of the manuscript. MP designed the analysis of experimental data, carried out the statistical analysis, and contributed in writing the manuscript. JY and KB performed the electrophysiological recordings and helped revise the manuscript. RM contributed toward the interpretation of the results and in revising the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


There are large differences between men and women of child-bearing age in the expression level of 5 key enzymes in one-carbon metabolism almost certainly caused by the sex hormones. These male-female differences in one-carbon metabolism are greatly accentuated during pregnancy. Thus, understanding the origin and consequences of sex differences in one-carbon metabolism is important for precision medicine.We have created a mathematical model of hepatic one-carbon metabolism based on the underlying physiology and biochemistry. We use the model to investigate the consequences of sex differences in gene expression. We give a mechanistic understanding of observed concentration differences in one-carbon metabolism and explain why women have lower S-andenosylmethionine, lower homocysteine, and higher choline and betaine. We give a new explanation of the well known phenomenon that folate supplementation lowers homocysteine and we show how to use the model to investigate the effects of vitamin deficiencies, gene polymorphisms, and nutrient input changes.Our model of hepatic one-carbon metabolism is a useful platform for investigating the mechanistic reasons that underlie known associations between metabolites. In particular, we explain how gene expression differences lead to metabolic differences between males and females.The online version of this article (doi:10.1186/s12918-018-0621-7) contains supplementary material, which is available to authorized users. There are significant sex differences in one-carbon metabolism (OCM) and these differences are accentuated in pregnancy –3. WomenThe reaction diagram for the folate and methionine cycles in OCM is very complicated consisting of loops within loops see Fig.  below. FIn  we creatThe schematic diagram of the mathematical model is shown in Fig. β-synthase (CBS) reaction flux is called the transsulfuration flux. Note that, at steady state, the remethylation flux plus the methionine input flux must equal the transmethylation flux and the methionine input flux must equal the transsulfuration flux. All concentrations and Km values are in micromolar. All fluxes are in micromolar/hour.We follow standard nomenclature and refer to the sum of the fluxes of the 5 methyltransferase reactions from SAM to SAH as the transmethylation flux. The sum of the methionine synthase (MS) and betaine-homocysteine methyltransferase (BHMT) reaction fluxes is called the remethylation flux, and the cystathionine The complete mathematical model consists of 17 differential equations for 17 metabolites. Each differential equation tracks the concentration change of the metabolite, by adding the sum of the rates of the arrows coming into that variable and by subtracting the sum of the rates of the arrows leaving the variable. Contributions of the mitochondria to one-carbon metabolism have been studied in , the traKm values from the ranges in the literature. Then we adjust the Vmax values so that the metabolite concentrations at steady state are consistent with the concentrations in the literature. In fact, the Vmax values are proportional to enzyme expression levels and enzyme expression levels vary by up to 25% from individual to individual   \documenKm=1000μM for Hcy taken from [μM) and equals one otherwise. Thus, the fourth term equals one when betaine is below 315 and increases up to 1.2 as betaine concentration rises.The kinetics of CBS are standard Michaelis-Menten with ken from . The secken from , 36 and ken from , suggestWe note that once one has the concentrations of the metabolites, then one obtains the metabolite fluxes by putting the concentrations into the above formulas.Vmax value of MTHFR by 0.3 and 0.68, respectively. Similarly, total folate concentration is a parameter in the model. We test the effects of different values of total folate by multiplying this parameter by appropriate scale factors.The C677T and A1289C polymorphisms of MTHFR reduce the activity of MTHFR by 70% and 32%, respectively , 38. In Five enzymes in one-carbon metabolism, BHMT, MS, MTHFR, serine hydroxymethyltransferase (SHMT), and PEMT, are differentially expressed in males and females and the differences are not small. In Fig. Vmax values of the enzymes PEMT, BHMT, SHMT, MS, MTHFR by the factors shown in Table The model described in Methods is a model for male one-carbon metabolism. The resulting concentrations and (some) velocities at steady state are shown in Fig. After making these changes, we ran the model to its new steady state. The resulting concentrations and velocities for the female model steady state are shown by the red numbers in Fig. There are several interesting and important questions. The first is what are the mechanisms that cause these differences in enzyme expression levels in females? A good deal is known. The sex hormones estrogen, testosterone, and progesterone are found in both the males and females but in different proportions. Men have around 19 times more testosterone than women and women of childbearing age have up to 9 times more estrogen than men. Estrogen concentration increases even more throughout pregnancy . It has Other hormones also cause sex dimorphism in liver enzymes. Waxman showed that the pulsatile release of growth hormone (GH) by the pituitary in males and the almost constant release in females cause differences in cytochrome P450 expression in rats . DNA micThe second question is what are the mechanisms by which the enzymes expression changes lead to the concentration changes. These questions are easy to state but not easy to answer, because the reaction network is complicated and the long range interactions, through which a substrate concentration at one location can affect enzymes at distant locations in the network , then SAM will inhibit BHMT more preventing remethylation of Hcy to Met and SAM will activate CBS more sending more flux down the transsulfuration pathway and both effects limit how much SAM will rise. If SAM starts to fall  then the inhibition of BHMT is withdrawn and the activation of CBS is withdrawn thus remethylating more Hcy to Met and limiting the decline in SAM. As a result, over wide ranges, methionine input does not affect choline concentration very much. Since methionine input changes a lot during the day because of meals this may be an important evolutionary adaptation.Panel c shows the homocysteine concentration at steady state in the female model as a function of MTHFR activity and methionine input. As above, the white dot is wild type and the magenta dots indicate the steady state concentration of Hcy in the presence of the polymorphisms. Both polymorphisms substantially raise Hcy concentration. However, as can be seen in Panel D, in the presence of a folate deficiency that is 20% of normal, the Hcy concentration of even the wild type is higher than those caused by the polymorphisms in case of no folate deficiency (Panel c). This is another indication of the importance of folate. The mechanism by which folate affects Hcy concentration is discussed in the “The purpose of this study was to understand sex differences in one-carbon metabolism and for that purpose we added the synthesis pathway of choline and betaine to a previous mathematical model . AlthougRecently, a large scale semi-quantitative model of sex differences in liver metabolism has been published –47 basedThe enzyme PEMT catalyzes the production of PC, the first substrate on the pathway that synthesizes choline and betaine. Choline and betaine are essential nutrients obtained from the diet or from the synthesis pathway on the right side of Fig. The upregulation of PEMT by estrogen in women of child-bearing years greatly increases the concentrations of choline and betaine as we saw in the “It is important to remember that the mathematical model in this paper is for liver one-carbon metabolism. We explained, in the “This study uses mathematical modeling to investigate sex differences in one carbon metabolism, but it is only a first step because there are many other issues to be analyzed. Since estrogen levels vary during the menstrual cycle, our results suggest that Hcy levels should vary too, and they do , 67. KalWe have created a new mathematical model of one-carbon metabolism in the liver including the synthesis pathway for choline and betaine. The model was used to understand how observed enzyme expression differences lead to metabolite concentration differences. In particular, we explained why women have lower S-andenosylmethionine, lower homocysteine, and higher choline and betaine. We give a new explanation of the well known phenomenon that folate supplementation lowers homocysteine and show how the model can be used to investigate the effects of vitamin deficiencies, gene polymorphisms, and nutrient input changes. Future work will explore how one-carbon metabolism changes during pregnancy.Additional File 1Supplementary Material for Sex Differences in One-Carbon Metabolism. (PDF 710 kb)