One goal of advanced information and communication technology is to simplify work. However, there is growing consensus regarding the negative consequences of inappropriate workload on employee's health and the safety of persons. In order to develop a method for continuous mental workload monitoring, we implemented a task battery consisting of cognitive tasks with diverse levels of complexity and difficulty. We conducted experiments and registered the electroencephalogram (EEG), performance data, and the NASA-TLX questionnaire from 54 people. Analysis of the EEG spectra demonstrates an increase of the frontal theta band power and a decrease of the parietal alpha band power, both under increasing task difficulty level. Based on these findings we implemented a new method for monitoring mental workload, the so-called Dual Frequency Head Maps (DFHM) that are classified by support vectors machines (SVMs) in three different workload levels. The results are in accordance with the expected difficulty levels arising from the requirements of the tasks on the executive functions. Furthermore, this article includes an empirical validation of the new method on a secondary subset with new subjects and one additional new task without any adjustment of the classifiers. Hence, the main advantage of the proposed method compared with the existing solutions is that it provides an automatic, continuous classification of the mental workload state without any need for retraining the classifier—neither for new subjects nor for new tasks. The continuous workload monitoring can help ensure good working conditions, maintain a good level of performance, and simultaneously preserve a good state of health. Advanced information and communication technology, highly interactive work environments, and work assistance systems impose increasingly high demands on our cognitive capacity and on the ability to cope with mental workload. Although one main goal of information and communication technology is to simplify work, there is growing consensus concerning the negative consequences of inappropriate workload on human's health and the safety of persons. Reports show that employees are complaining more frequently about high mental workload and stress  but also between different subjects. User acceptance varies with respect to the complexity of the registration system constituting their main issue. However, recent developments in the field of mobile technology promise small, lightweight, and wireless systems. In addition, a main advantage of biosignals is that they can be obtained continuously, a subject can hardly manipulate them, they do not alter subject's current workload state, and they can be obtained on-the-fly during task execution.Previous studies indicated that several psychophysiological variables respond in a more or less predictable way to cognitive effort, e.g., heart rate variability  . These DFHM rely on personalized spectral features and their spatial occurrence. They are classified with support vector machines (SVMs) trained by means of established knowledge linked to the variability of the spectral features due to mental demands that have been reported in EEG research over the past 50 years. In particular, increased mental workload is mainly associated with EEG variations in the spectral power of the theta band at frontal sites  for our task battery. They overlap somewhat with regard to task demands but differ with regard to the executive function addressed Table . Their iThe 0-back task represents an easy task, where the subject will have to press the mouse button if the presented letter is “X.” In the 2-back task the subject will be asked to press the mouse button if the presented letter is the same as the next to last letter seen .All of the investigations acquired were approved by the local review board of our institution and the experiments were conducted in accordance with the Declaration of Helsinki. All procedures were carried out with the adequate understanding and written consent of the subjects.Digital signal processing and all calculations were done with MATLAB.Twenty five passive electrodes were placed at positions according to the 10–20-system for capturing the EEG  between 0.5 and 40 Hz. Independent component analysis  of every subject (54 subjects × 10 measurements × 1 segment of the first minute). For the sake of consistency, we excluded the first minute of these tasks from the further proceeding of classifier test. The labels used represented the range of low load, moderate load, and high load. The labeling process was totally blind to subject and task and relied only on the behavior of the frequency bands. Accordingly, each DFHM was visually inspected. If the frontal z-score value of the theta band power was high and the parietal z-score value of the alpha band power was low, the DFHM was labeled as high load. In contrast, all DFHM with a low frontal z-score value and high parietal z-score value were labeled as low load. The remaining, where the differences of frontal and parietal z-scores were not pronounced, were labeled as moderate load. Figure Subsequently, training of two support vector machines (SVMs) for low load and high load DFHM classification was carried out. We performed random sub-sampling, also known as Monte Carlo cross-validation. We are aware that by application of this method not all DFHM of our labeled data set might be used. However, we are also aware of our quite small set size. Given this, we consider an advantage of this method that there is no dependency of subset split-size to the number of folds .We split our data into a training and a testing subset, whereby their size corresponded to 60 and 40% of the labeled DFHM, respectively. These random partitioning of the data was repeated ten times. For each split the classifiers were retrained from scratch to fit to the new training subset and predictive accuracy was computed by means of the testing subset. Finally, the results were averaged over the splits. The empirically selected parameters and the testing results of both SVM kernels are presented in Table Our system's setup consists of the general pre-processing of the recorded EEG data, computation of z-score and DFHM, and their classification. Figure For every subject and measurement, DFHM are created of each EEG segment. Both SVM classifiers are applied on each DFHM and the new workload index is automatically computed as a logical combination of the classifiers' output every 5 s . Hence, evaluation of the DFHM workload index is done statistically with regard to a postulated hypothesis. This arises from the cognitive task demands well-known from the literature and their impact on subject's mental workload. In particular, we expect easy tasks like 0-back, switch-NUM, switch-GER, and the rest measurements to induce low workload, i.e., relatively few DFHM segments classified as high load and most DFHM classified as low load. In contrast, high workload is expected during demanding tasks like AOSPAN. Here, most of the DFHM are expected to be classified as high load and only few of them as low load. Finally, the Stroop and the switch-XXX tasks are assumed to range in the middle and to induce moderate workload. Hence, the amount of DFHM classified as high and low load is expected to range between the above mentioned tasks.post-hoc tests. Thereby, multiple paired comparisons with a Bonferroni correction were calculated. The Bonferroni correction is used to counteract the problem of multiple comparisons by adjusting the significance level by the number of the calculated comparison tests  getting three percentage values for each person and task. Thereafter, we computed two analyses of variance (ANOVA) with repeated measures design using the portion of low load segments (LLS) and the portion of high load segments (HLS) as within-subject factor, respectively. To complete the picture, we involved all measurements in our evaluation. Hence, two rest measurements and nine tasks constituted the eleven levels used. For the examination of significant differences between these levels, we employed ts Abdi, . The corpost-hoc tests. These examinations allow us to compare the new workload index with further workload relevant data later on and gain additional information that could be beneficial in case of doubts when interpreting our DFHM results.Furthermore, regarding the NASA-TLX and the accuracy rates we calculated one more repeated measures ANOVA, respectively. They both comprised one within-subject factor and only nine levels (the nine tasks but no rest measurements). The ANOVAs were completed by Bonferonni-corrected post-hoc analysis. They are presented in Tables Statistical analysis yielded significant differences in the means of the portion of high and low load segments. These changes among the tasks were examined by post-hoc tests showed significant changes of the mean workload index between most of the tasks . The new data were collected under the same conditions and procedure as the above mentioned experiment. In order to test if the new method can deal with new tasks without adjustment, we enlarged our task battery by an additional cognitive task: the stop signal task Logan, .The stop signal task is similar to the Stroop task an inhibition task. During the task, subjects are instructed to press the green mouse button as fast as possible if a horizontal left arrow is presented on the screen and the red mouse button if a horizontal right arrow appears. If a horizontal arrow is shortly followed by a vertical arrow, they are instructed to inhibit their response and not press either button. They have to respond as fast as possible and consider that their main aim is to keep the frame around the arrow green. A red frame means they are too slow. Hence, if red, they should speed up their response while still paying attention to the vertical arrow.For the validation we postulate the same hypotheses as above. If the method is valid, we have to obtain similar tendencies in the behavior of the proportion of high and low load segments among the tasks as for the first experiment. Furthermore, we expect that the new task yields similar results as the Stroop task. Finally, we have to highlight that SVM classifiers for the DFHM were not retrained for the new experiment, neither regarding the new sample set nor in respect to the new task.The proportion of HLS and LLS computed over the eight subjects for the nine tasks and the two rest measurements are presented in Figure In order to evaluate the new proposed DFHM method, in the following we consider the obtained results and discuss them also in comparison with the tendencies derived from the subjective ratings and the performance measurements.Regarding the subjectively experienced mental workload derived from the NASA-TLX overall index no significant differences could be identified among the easy tasks 0-back, switch-PAR, and switch-NUM. This was also not expected because these tasks do not differ in respect of their minimal demands. Similarly, no significant differences among these easy tasks could be found in respect to the proportions of high and low load segments derived from the DFHM method.Furthermore, analysis of the proportion of identified high and low load segments per task indicates that with increasing demands the amount of high load segments increases while the amount of segments classified as low load decreases. Moreover, the resulting DFHM workload index yields here the same tendencies as the other workload parameters from the questionnaire and the performance data.It was somewhat surprising that the subjects were not able to significantly discriminate mental workload among the five tasks 2-back, Sternberg, serial Sternberg, switch-XXX, and AOSPAN which obviously differ in their task demands. We presume that there is a ceiling effect in the subjectively measured workload. As the workload reaches the subjective ceiling, the desired validity of the subjective ratings suffers. Interestingly, subjective ratings indicate significantly less workload during the Stroop task than during the five above mentioned tasks. We attribute this to the playful character of the Stroop task that could function as motivation and also lead to significantly higher performance compared to the working memory tasks 2-back, Sternberg, and serial Sternberg. However, the index derived from the DFHM places the Stroop task regarding mental workload between the demanding AOSPAN and the working memory tasks and on a similar level as that from the switch-XXX task.Subsequently, we empirically validated the new method on a smaller sample set of eight new subjects and with one additional new task . We want to emphasize that SVM classifiers were not retrained neither in respect to the new subjects nor to the new task. Validation of the DFHM workload index indicated the same tendencies among the tasks as already obtained by the larger sample set. The additional stop signal task integrated itself as expected between the demanding AOSPAN task and the Stroop inhibition task. Noticeable is that in respect of the proportions of LLS from the Stroop and the stop signal task, the latter is found to be more demanding. This fact fits well to the statements of the subjects regarding their difficulties during the stop signal task.To sum up, the new DFHM method leads to the classification of EEG segments as high, moderate, or low load. Based on this, our results support the expected difficulty levels of the tasks resulting from their demands on the executive functions.In contrast to single power spectra  there is no need anymore for defining quantitative threshold values for the three workload states. Threshold values are problematic because they are not generally applicable to different subjects. As the new method relies on personalized DFHM and on SVM classifiers trained by means of reported knowledge on the variability of spectral features, it achieves immediate and continues classification in one of the three classes. The time-consuming, additional retraining of the classifier for new subjects and tasks is not necessary anymore. Results from the other workload relevant parameters (subjective ratings and performance data) reveal similar outcomes regarding workload and emphasize the EEG findings.Based on such consolidated findings of neuronal brain states an optimal workload range for the operator can be defined. This range corresponds to more efficient cognitive processing, thus enabling performance at its best. Simultaneously, it preserves employee's health and hence, accounts for optimized work conditions.Related research areas are also neuroergonomics (Parasuraman and Rizzo, As a limitation of our work, we have to mention that our experiment is conducted in a highly controlled laboratory environment by using clearly defined, basic cognitive tasks. Results could be different under realistic settings and real world scenarios. Especially, the contradictory behavior of the alpha band power could effect the validity of the DFHM workload index under realistic conditions with enhanced visual information processing. Further research is needed to validate these findings in such environments. The first steps for research outside the lab, in particular regarding automatic artifact rejection, have already been taken (Radüntz et al., TR implemented the task battery and was responsible for all technical and computational work of the study. Signal processing, data analysis, and interpretation were performed by TR. The manuscript was written by TR.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Chemoresistors working at room temperature are attractive for low-consumption integrated sensors. Previous studies show that this feature can be obtained with photoconductive porphyrins-coated ZnO nanostructures. Furthermore, variations of the porphyrin molecular structure alter both the chemical sensitivity and the photoconductivity, and can be used to define the sensor characteristics. Based on these assumptions, we investigated the properties of an array of four sensors made of a layer of ZnO nanoparticles coated with porphyrins with the same molecular framework but different metal atoms. The array was tested with five volatile organic compounds (VOCs), each measured at different concentrations. Results confirm that the features of individual porphyrins influence the sensor behavior, and the differences among sensors are enough to enable the discrimination of volatile compounds disregarding their concentration. Organically capped ZnO nanostructures have been widely studied—in particular for their optical and electronic properties . A notewSince porphyrins are extremely suitable as a chemically sensitive material , we haveAn important issue in these materials is the functionalization process. The standard method used to achieve organically-capped nanostructures consists of the deposition of an organic layer onto the surface of pre-formed inorganic structures. However, alternative routes where a more deep interaction between organic an inorganic molecules can be conceived. In this regard, we found that the addition of porphyrins to the ZnO hydrothermal growth solution largely alters the nanostructures order, but on the other hand, it results in a compact and thin organic coating. Furthermore, the one-pot growth confers peculiar properties to sensors which are different from those made with the standard fabrication route ,9.In spite of these promising results, the effective exploitation of these sensors is made difficult by the geometry of nanorods. Hydrothermal growth proceeds from a seed layer which forms—after the synthesis—a sort of basement of the vertical rods. When performed on planar electrodes , this assemblage results in a lack of influence of the nanorods on the observed conductivity. In other words, the vertical nanorods are almost completely shortened by the seed layer basement. This problem can be avoided by patterning the seed layer as the two electrodes. In this way, the hydrothermal growth gives rise to nanorods connecting one electrode to the other. This approach has been shown to be viable , but tooNanoparticles are a straightforward alternative. They can be grown in solution and deposited as a solid film onto the sensor substrate with a simple dip coating process. Furthermore, in contrast to nanorods, the homogeneous surface of nanoparticles facilitates the coating process.In this paper, we investigate the sensor properties of an array of four ZnO nanoparticles, each coated with a different porphyrin. The functionalized nanoparticles were fabricated following the one-pot method introduced for nanorods. Porphyrins-coated nanoparticles were deposited onto a substrate endowed with interdigitated electrodes for conductivity measurement. The sensor array was tested by measuring the sensitivity towards five volatile compounds, each representative of a chemical family of compounds: organic acid, alcohol, arene, amine, and water. Due to its chemical peculiarity, each compound is expected to interact with porphyrins with a different blend of fundamental interactions, including van der Waals forces, hydrogen bonds, 2TPPCOOH) and its metal complexes with copper (CuTPPCOOH), cobalt(II) (CoTPPCOOH), and zinc (ZnTPPCOOH) have been prepared following literature methods [5-(4’-Carboxyphenyl)-10,15,20-triphenylporphyrinato  that sequentially measured the resistance of the four sensors. During the measurement, sensors were illuminated with a white LED . The LED emission spectrum fits with the absorbance spectra of porphyrins [Solid films of porphyrins-coated ZnO nanoparticles were made by drop coating technique onto a glass substrate interdigitated electrodes (DropSense), visible in rphyrins . The relDuring the gas measurements, the sensors were always kept under the constant illumination of the white LED. The resistances in this condition were in the range of tens of KAs found in other porphyrins-based sensor arrays, all sensors show a common behavior. This evidences the fact that the chemical difference between porphyrins—here limited only to the metal ion complexed at the core of the macrocycle—is a small factor with respect to the influence of the whole macrocycle.In all sensors, the absorption of triethylamine elicits a decrease of conductivity in all sensors, but the resistance increases for all the other volatile organic compounds (VOCs).2TPPCOOH) impossible. In this case, the sensitivity can be justified considering that the core of the free-base porphyrins shows an amphoteric character, the imine group (-C=N-) being basic and the pyrrole-type nitrogen atoms (N-H) acid. Then, instead of the coordination to the metal ion, the sensitivity of H2TPPCOOH with respect to triethylamine is likely due to an acid–base interaction.The peculiar response to triethylamine may be explained considering that amines are electron donors and bases. The coordination of the amine explains the behavior of metalloporphyrins . However, the lack of metal makes the coordination with the free-base porphyrin  was used to investigate the collective behavior of the four sensors. For the scope, the sensor responses were arranged in a matrix, and PCA was calculated on the autoscaled (zero mean and unitary variance) matrix .PCA was applied to the whole set of measurements, where each concentration was measured three times. In this way, PCA results enable an appreciation of the separation of the different VOCs with respect to the dispersion of sensor signals.Two main sources of correlation can be found in our data: the intrinsic correlation among the sensors , but also a different pattern of interactions with the other tested volatile compounds. These differences appear subtle when sensors are individually compared, but are sufficiently large to identify different volatile compounds regardless of the variation of concentration.This paper was focused on the demonstration that a chemo-resistive porphyrins-based sensor array can be obtained by exploiting the photo-current generated by the porphyrins—which are typically non-conductive— when they coat the surface of ZnO. However, it remains to be demonstrated that the change of resistance can efficiently transduce all the interactions that can take place between the porphyrins and the volatile compounds and that such an array can actually be used to discriminate among real samples.


Body-worn sensor networks are important for rescue-workers, medical and many other applications. Sensitive data are often transmitted over such a network, motivating the need for encryption. Body-worn sensor networks are deployed in conditions where the wireless communication channel varies dramatically due to fading and shadowing, which is considered a disadvantage for communication. Interestingly, these channel variations can be employed to extract a common encryption key at both sides of the link. Legitimate users share a unique physical channel and the variations thereof provide data series on both sides of the link, with highly correlated values. An eavesdropper, however, does not share this physical channel and cannot extract the same information when intercepting the signals. This paper documents a practical wearable communication system implementing channel-based key generation, including an implementation and a measurement campaign comprising indoor as well as outdoor measurements. The results provide insight into the performance of channel-based key generation in realistic practical conditions. Employing a process known as key reconciliation, error free keys are generated in all tested scenarios. The key-generation system is computationally simple and therefore compatible with the low-power micro controllers and low-data rate transmissions commonly used in wireless sensor networks. Wearable wireless sensor networks are a convenient solution to communicate measurement results from person to person, or from a person to a base station. The data often include sensitive information such as coordinates or medical data, requiring secure transmission. With the current evolution of the Internet of Things (IoT) and the Internet of People (IoP), secure wearable wireless sensor networks are gaining even more importance .Given the limitations imposed by the hardware of typical wireless sensor nodes, there is a need for encryption protocols that are energy efficient and require limited memory and processing power. Most conventional encryption schemes do not meet these demands, and also require considerable packet overhead in the form of header extensions. Therefore, current research focuses on lightweight block ciphers, which are, however, vulnerable to security issues as well as denial-of-service attacks . ResearcThe wireless sensor nodes in this paper employ communication according to the IEEEThe idea of having a common and unique source of information for key generation has also been studied for body area sensor networks before , where bThe source of common information for body-worn wireless sensor nodes originates from the properties of the radio propagation channel. Wearable sensor networks are typically deployed in environments where the communication channel varies significantly due to fading and shadowing . In the Legitimately communicating users share a unique physical propagation channel of which the characteristics can be measured at both sides of the link. The channel measurements are highly correlated for the legitimate users and the mutual information between both sets of data allows to extract similar keys by both users. Some key errors might occur, but reconciliation techniques are further applied to successfully adjust the keys.An eavesdropper, however, does not share the same physical channel, and eavesdropper measurements will typically be decorrelated from the legitimate users’ data. Mutual information between the data series will also be very limited , renderiThis paper discusses practical research on the performance of a wearable channel-based key-generating system, employing realistic body worn sensor nodes, of which the hardware and network performance were documented earlier . The embThe proposed and tested key generation algorithm is very suitable for body-centric sensor networks and allows secure key generation without additional hardware. Encryption by means of the generated key can be performed employing a number of algorithms suitable for execution on a low-power micro controller . Keys geWearable sensor nodes benefit from the natural movement of the body to create sufficient channel variation for low-bitrate key generation, without the need for re-configurable antennas  or multiTo the author’s knowledge, this is the first fully functional implementation of this type of key generation on wearable autonomous wireless sensor nodes. Initial work was documented in ,19, but The wireless sensor nodes, employed to collect the channel measurements, are fully integrated flexible wearable wireless sensor nodes, as shown in In order to perform measurements for channel-based key generation, the software on the wireless sensor nodes is adapted to bilaterally assess reciprocal channel parameters in a very short time slot. The ADF7242 TRX, in combination with the C8051F920 MCU, allows a transmit-receive turnaround time of 5 ms between legitimate users.A bilateral transmission by the system for channel measurements in the The horizontal scale is 1 millisecond per division, whereas the vertical scale is the received signal power in dBm. Clearly, transmissions last 1 ms and are separated by only 5 ms. Note that this is a reciprocal channel estimation with the second transmission being the answer from the other side of the link, upon error free reception of the first transmission.The system enables reciprocal channel estimation within the coherence time of the channel, implying that the channel variation within the 5 ms time frame is known to be limited. As a consequence, the bilaterally measured values for the Received Signal Strength (RSS), will be highly correlated and share significant mutual information. The resolution of the RSS measurement is 1 dB, but imbalance due to hardware tolerances can make channel estimation less accurate.An additional source of tolerances on the channel measurements occurs due to the nature of the radio propagation. The main cause of the signal variation is small-scale fading, caused by the changing conditions of constructive or destructive interference between multiple reflected radio waves. Destructive interference causes an RSS minimum, appearing as a notch when presented graphically. At a moment of destructive interference, the channel varies very fast, increasing the difference between reciprocal measurements within the 5 ms time slot.Throughout the paper, the names Alice, Bob and Eve will be used, according to existing literature in the field of channel-based key generation . Alice aIndoor as well as outdoor measurements have been performed. Outdoor propagation measurements were also performed over larger distances, across the canal visible in Propagation can occur LoS (Line-of-Sight) when no buildings or obstacles are blocking the radio waves between communicating parties. In NLoS (Non Line-of-Sight) outdoor propagation, the line-of-sight path is obstructed by buildings and the wireless link is established mainly via reflections and scattering in the environment.Indoor radio propagation environments characteristically have much more multipath propagation, leading to different fading characteristics and more complex channel variation. As visible in The picture also clearly displays the compactness and wearability of the system, where the wireless sensor nodes are easily integrated onto a T-shirt without any external wired connections, retaining a high flexibility. Eve is a similar node mounted in a fixed position. In the picture, Eve’s position is indicated by the arrow at location Z.The measurement scenarios are listed in For both indoor and outdoor environments, two regular scenarios are tested, being Alice and Bob in LoS, with Eve either LoS or NLoS. These are the most realistic scenarios to occur, with an eavesdropper inside or outside the building for the indoor case, or hidden behind a building or not for the outdoor case. For the outdoor case, a measurement is also performed with the legitimate parties across the canal in A worst-case scenario is also tested for both the indoor as well as the outdoor environment, in which the legitimate parties Alice and Bob are NLoS, but with direct LoS from both parties to the eavesdropper, Eve. This situation is unlikely to occur in practice but is interesting to analyze because, with NLoS propagation between Alice and Bob, reflections in the vicinity of Eve will likely contribute to the propagation between the legitimate parties. Therefore, channel measurements performed by Eve in this scenario are expected to be slightly more correlated to the measurements of Alice and Bob.The transceiver chip performs channel measurements by means of an analog logarithmic detector, which are further sampled as 8-bit values, directly expressed in dB. The resolution of the measurement is 1 dB, according to the datasheet. Inaccuracy can result from channel variation, chip-to-chip parameter spread, or system imbalances such as differing antenna performance and matching. Hence, the bytes that result from the reciprocal detectors are generally not equal, despite their high correlation.Determine the moving average of the last seven RSS values (corresponding to the average over the last 7 s) .N dB above or below this value, a 1 or a 0 bit is generated, respectively. Bob is informed about the generation of a key bit via the wireless channel, without revealing the actual value of the bit.If the current RSS value crosses a threshold of If the threshold is not crossed, no bit is generated.To design a system that functions in practice by exploiting these channel measurements, a quantizer is applied at Alice’s side to extract only one bit per channel measurement according to the following procedure:Determine the moving average of the last seven RSS values.-A 1-bit is generated if Bob’s measured RSS value is above the threshold-A 0-bit is generated otherwiseIf Bob is informed by Alice that she has generated a key bit, Bob also generates a key bit:The quantizer at Bob’s side also extracts one bit per channel measurement, as follows:If Alice generates the master key, she will choose and apply the threshold level. Each time Alice uses an RSS value to generate a key bit, she will immediately inform Bob over the wireless link. The information of course does not contain the actual value of Alice’s key bit. Bob can now generate his own version of the key bit, based on his own RSS measurement. This approach is absolutely necessary in order for Alice and Bob to use sets of RSS values that are sampled in corresponding time slots.Independently choosing RSS samples on both ends of the link via equal thresholds does not work in practice. Due to the imperfect reciprocity of the measurements, the generated keys would quickly run out of synchronization since, at some time instances, only one party would generate a key bit.Note that the analysis presented in this paper is based on the worst-case assumption that Eve knows all the details about the system and is able to intercept all the information that Alice and Bob exchange, including knowledge about the time slots where Alice and Bob select RSS samples. All calculations have been performed to be consistent with this assumption.Only one RSS measurement per second is presented to the quantizer, in order to obtain subsequent random bits that are sufficiently decorrelated. The time interval of one second corresponds well to the rate of change of the RSS values recorded for normal walking speeds of about The bilateral set of raw keys needs further reconciliation to exactly match. The Transmitting the check bits reveals some information about the key to a potential eavesdropper, assuming the unlikely fact that the eavesdropper has all detailed information about the complete key-generating system. Employing the Hamming code and revealing four check bits, results in As a first observation, 1500 RSS channel samples for Scenario 1 are displayed in The KERs (Key Error Rates) and the corresponding key lengths after reconciliation are displayed in The first important observation is that the KER converges to zero for all scenarios, provided a significantly large threshold is employed, clearly illustrating effective key generation in all tested conditions. Without threshold, or with thresholds of only 1 or 2 dB, the KER is systematically lower for the outdoor scenarios. The point of zero bit errors is achieved at a threshold of only 2 dB for scenario 3, resulting in an error-free key length of 1364 bits, based on 1500 channel samples. For all other scenarios, except scenario 7, a threshold of 5 dB is necessary to obtain a KER of zero, making the performance for indoor and outdoor scenarios comparable. For the worst-case scenario 7, manifesting also the highest initial KER, a zero KER is obtained only at a threshold level of 7 dB.Note that a further increase of the threshold beyond the first zero KER point can coindicentally result in some key errors again, which is the case for scenario 2, but for a higher threshold, the KER finally converges to zero.The key length in To obtain the highest possible key generating rate, the lowest possible threshold should be used. Detecting the point of error-free keys can be easily performed by exchanging a CRC (Cyclic Redundancy Check) sum on the key bits between the legitimate parties.Note in the graph that, for scenarios 2 and 4, the key length without threshold is a bit shorter than 1500 bits, due to packet loss over the difficult link to Eve, which is either NLoS or larger distance LoS. In the analysis, only packets received successfully by all three nodes are considered.It is equally important to study the behavior for key generation attempted by Eve, assuming the worst-case situation that Eve captures signals from both Alice and Bob, knows all details about the system and is also able to intercept the check bits.Alice has the master key, Eve uses Alice’s signal strengthAlice has the master key, Eve uses Bob’s signal strengthBob has the master key, Eve uses Alice’s signal strengthBob has the master key, Eve uses Bob’s signal strengthEve tries to estimate the master key based on recorded signal levels. Four cases are possible for the measurement discussed here:All cases have been studied in detail, but we document only graphs for the worst of these four cases, where key extraction by Eve reveals the most information about the key. This corresponds to case number 1 in the list.It is important to note that convergence to a zero KER at a threshold of 12 dB for scenario 7 does not mean that Eve can intercept the key used by Alice and Bob. The legitimate parties normally choose to operate at a threshold level of 7 dB for this scenario, as this is the lowest threshold resulting in a zero KER for them. At this threshold level, the KER for Eve is still Further reasons why convergence to lower KER values for Eve is not of practical use follows from the key length shown in Error-free key generation is possible in all tested scenarios. However, due to differences in the statistics of the signal levels, the performance differs for the different scenarios. To obtain error-free keys after reconciliation, a threshold of 5 dB is sufficient for six out of seven scenarios. A comparison of the key generating rate at this threshold level is therefore most relevant. Note that the threshold value yielding error-free keys is determined automatically by exchanging a CRC calculated for the key which is generated after reconciliation. A valid CRC indicates matching keys at both sides of the link, allowing both parties to select an equal threshold level, which is as low as possible to obtain error-free keys.Limited resolution of the RSS detectorHardware imbalances of the TRX and the antennasChannel small-scale fading characteristicsObserved key lengths extracted from the measurements are clearly the largest for the outdoor scenarios, despite the presence of some packet loss in two outdoor scenarios. Outdoor keys have a smaller variance, with a length between 836 and 1001 bits. For the indoor scenarios, the key length varies between 429 and 792 bits. The reason for this performance difference can be found in the physical channel behavior, which is different for indoor and outdoor areas. Three main sources of differing reciprocal channel measurements are identified:The first two sources of errors are always present, regardless of the scenario. However, the third source is much more dominant for indoor scenarios, for which more complex propagation conditions exist . In indoFor scenario 7, a higher threshold of 7 dB is necessary, decreasing the key length to 275 bits. It is important to realize that the loss in key-generation rate, caused by applying the necessary threshold, can be significantly reduced if the transmit-receive turnaround time of the system is decreased to 1 ms or lower. Hardware with less tolerance and a better resolution could finally render the raw KER, before thresholding and reconciliation, very low. In the measurement campaign, the performance is limited due to the state-of-the-art of currently available off-the-shelf components.Key generation by the eavesdropper is shown to be cumbersome. Eve cannot extract useful keys from her channel measurements. However, some differences in the results exist, dependent on the propagation scenario. For scenarios 2, 4 and 6, the KER for the eavesdropper does not converge at all. A decrease in KER is observed at higher threshold levels for the other scenarios. Increasing the threshold quickly makes the key shorter, therefore a low, or even zero KER obtained by Eve is not useful, as the number of effective key bits is very low. Moreover, the legitimate parties operate at lower threshold levels, for which the KER for Eve is always above In a practical system, the threshold should be chosen at the moment of key generation. The measurements provide an indication about which threshold values are realistic. A threshold of 8 dB provides a zero KER in all scenarios of the measurement campaign. Given the number of scenarios tested, a fixed threshold of 10 dB is likely to produce error free keys in nearly all cases. Of course, this fixed threshold does not lead to the highest possible key-generation rate.For Alice, a more sophisticated option is to dynamically adapt the threshold, based on feedback about the correctness of the key. Bob can transmit this feedback to Alice as a CRC check sum over a number of key bits generated by him after reconciliation. Alice can hence dynamically adjust her threshold level and set it as low as possible for error-free key generation by Bob. This approach should allow a larger average key generation rate for all scenarios.To make the communication secure from the start, a predefined key should be used initially. In practice, a random initial key can be securely distributed to all the nodes, via a wired connection to a common battery-charging unit. It is highly recommended not to send unencrypted packets at the startup process, as this will allow a potential eavesdropper to analyze the system. Note that the results in this paper are actually for an eavesdropper who does know all these details of the system and intercepts all packets. However, encrypting packets from the start makes it virtually impossible for an eavesdropper to extract these details, further improving the security of the communication.the largest restriction to increase the key generation rate is the limited channel measurement rate imposed by the time correlation of subsequent channel measurements. For walking persons, channel measurement rates much higher than one sample per second will yield low-entropy keys.N body-worn antennas create an The channel measurement rate can further be improved by employing multi-antenna systems. Systems of A measurement campaign for channel-based key generation was performed, with indoor as well as outdoor measurements. A set of seven scenarios was tested, including worst-case setups in difficult conditions. Successful key generation was always possible for the legitimate parties, with key generating rates slightly higher for the considered outdoor scenarios. Interception of the keys by the eavesdropper is impossible as illustrated from the measurements. In case the eavesdropper knows all the details of the system and intercepts all packets, only very limited information about the key could be retrieved in the worst-case scenario, potentially slightly decreasing the time needed for a successful brute force attack.


Elongated styloid process syndrome (Eagle’s syndrome) is the term given to the symptomatic elongation of the styloid process or the mineralization of the stylohyoid or stylomandibular ligament. The two commonly used approaches for the surgical treatment of this syndrome are the transcervical and transoral approaches. Both have their limitations and specific intraoperative risks. Here, we report the treatment of a patient with Eagle’s syndrome using the transoral approach in conjunction with piezoelectric surgery, surgical planning, and intraoperative navigation to reduce the risk of complications.The elongated styloid process was resected in a 45-year-old Japanese man using a minimally invasive approach with an intraoperative navigation system. Preoperative preparation involved the use of a custom interocclusal splint to produce the mouth opening conditions required during surgery. Using the three-dimensional position of the navigation probe, the location of the elongated styloid process was identified. After confirmation of the resection spot via the transoral approach, the styloid process was dissected by piezoelectric surgery. Follow-up examination showed an uneventful recovery with no associated complications.The resection of the styloid process using an intraoperative navigation system and a custom interocclusal splint during a transoral approach, together with a piezoelectric cutting device, is safe and effective for the treatment of Eagle’s syndrome. Symptomatic elongation of the styloid process (SP) or the mineralization of the stylohyoid/stylomandibular ligament is termed elongated styloid process syndrome. It is also called Eagle’s syndrome, named after the American physician Eagle, who first reported a series of uncomfortable symptoms, including throat pain and foreign body sensation on the affected side, reflex otalgia, head and neck pain, and hypersalivation, in 1937 [The two different approaches used for the surgical treatment of Eagle’s syndrome are the transcervical and transoral approaches . The cerHere, we present another aspect of the treatment modalities used for Eagle’s syndrome. We report the use of a transoral approach in conjunction with piezoelectric surgery, surgical planning, and intraoperative navigation to reduce the risk of complications.A 45-year-old Japanese man was referred to our hospital by his local dentist; he had constant pain in the head and neck region, particularly on the right side. The dentist had initially suspected temporomandibular disease, but owing to atypical symptoms, he was eventually referred to our hospital for further examination. He had various symptoms, including dysphagia, foreign body sensation, and headache. Our patient had been treated by several specialists including an otolaryngologist, a neurologist, and another dentist, during the past few years with no relief or signs of improvement. At first presentation, he complained of constant neck pain and odynophagia; however, palpation of the tonsils did not worsen the pain. Our patient had no history of a trauma, surgical treatment, or neurological or infectious foci. After clinical examination and basic radiological diagnostics, an elongated SP was suspected as the cause of his problems. Computed tomography (CT) images showed a longer and thicker right SP when compared with the left side. Our patient was diagnosed with Eagle’s syndrome in view of the neck pain and radiographic images Fig. . AnalgesAn investigation of the cervical blood vessels on the right side demonstrated that the SP and the branches of the internal and external carotid arteries were located in proximity to each other. Therefore, accurate preoperative planning was essential. Furthermore, since the transoral approach was being employed, it was necessary to reproduce the same mouth opening conditions during the taking of preoperative images as that required during surgery. This is because the position of the SP and the blood vessels may change depending on the position of the mandible during the mouth opening. As it is difficult to implement the locational findings from the preoperative imaging data while performing the surgery owing to the mobile nature of the mandible, we used a custom interocclusal splint for repeated maximum opening in the same mandibular position while enabling surgical access. As part of the CT imaging preparation for navigation, a custom interocclusal splint was fabricated by pressing acrylic resin into a dental mold obtained during the first visit. First, the created bimaxillary upper and lower jaw splints were adhered with resin at the maximum mandibular opening position. Because there was the possibility of distortion with fixation of only the molar portion, we added a supplementary small strut to the anterior region without hindering the surgical approach Fig. . MaxilloThe imaging data were obtained in Digital Imaging and Communication in Medicine format and transferred to a Medtronic StealthStation S7 workstation using the Synergy Fusion Cranial 2.2.6 software . Our patient was taken to the operating room where the custom interocclusal splint was reinserted. A Patient Tracker EM, which acted as a reference array to track the navigation probe, was affixed on to our patient’s forehead. To perform patient to CT and MRI data registration, the instrumentation navigation probe was used to trace the reference array and soft tissue landmarks of the face Fig. . After dUsing the navigation pointer, the tip of the elongated SP was detected based on the navigation image on the screen and easily located through the oral mucosa Fig. . The incThe SP is an osseous projection located ventral to the stylomastoid foramen with several ligaments and muscles attached to it. It originates from the tympanohyal and stylohyal segments that are united by cartilage, which often undergoes ossification during middle age . The susThe incidence of elongated SP has been reported to range from 1.4 to 30% –10. HoweThe nonsurgical treatment of Eagle’s syndrome generally involves pharmacotherapy with anticonvulsants, antidepressants, and nonsteroidal anti-inflammatory medications; however, the results are short-lived . LidocaiThe transcervical approach generally provides an adequate field of operation with access to the SP and its surrounding structures, thus avoiding unnecessary injury to the peripheral neurovascular structures and difficulty in identifying SP. The major disadvantages of this approach include postoperative cosmetic deformity due to scar formation and the requirement of the scope of extensive fascial dissection, which may result in longer duration of surgery and uncomfortable cutaneous nerve paresthesia , 15. TraIn the present case, preoperative CT revealed a very close relationship between the SP and several branches of the carotid artery. In addition, difficulty due to narrowing of the visual field using the transoral approach was noted. Therefore, we decided to use virtual planning and intraoperative navigation as additional security measures. An intraoperative navigation system is a commonly accepted tool in cranio-maxillofacial surgery. It allows for the safe and fast localization of anatomical structures with minimal exposure . Some auIn the present case, it is important that the mandible position be maintained exactly as that during the surgery to maintain the position of the SP and surrounding blood vessels during mouth opening. As it is difficult to synchronize with the preoperative imaging data during surgery owing to the mobile nature of the mandible, we used a custom interocclusal splint to enable repeated a maximum mandibular opening position while allowing for adequate surgical access. Thereby, a navigation system was further beneficial in determining the accurate location of the object and provided the maxillofacial surgeons with appropriate intraoperative guidance for the safe and reliable use of an individual occlusion splint. We have previously reported the importance of a splint during navigation surgery for mandible lesions , 19. ConPiezoelectric surgery utilizes amplified ultrasonic microvibrations as a minute cutting edge to cut bony tissue. This safe cutting device has been proven to be an effective tool for performing bone surgery; it makes the cutting of hard tissue possible through “selective cutting” without encountering necrosis from overheating due to friction and damaging the soft tissues , 20. SelResection of the SP using an intraoperative navigation system via a transoral approach and a piezoelectric cutting device proved to be very safe and effective for the management and treatment of Eagle’s syndrome.


Electroencephalographic (EEG) event-related desynchronization (ERD) induced by movement imagery or by observing biological movements performed by someone else has recently been used extensively for brain-computer interface-based applications, such as applications used in stroke rehabilitation training and motor skill learning. However, the ERD responses induced by the movement imagery and observation might not be as reliable as the ERD responses induced by movement execution. Given that studies on the reliability of the EEG ERD responses induced by these activities are still lacking, here we conducted an EEG experiment with movement imagery, movement observation, and movement execution, performed multiple times each in a pseudorandomized order in the same experimental runs. Then, independent component analysis (ICA) was applied to the EEG data to find the common motor-related EEG source activity shared by the three motor tasks. Finally, conditional EEG ERD responses associated with the three movement conditions were computed and compared. Among the three motor conditions, the EEG ERD responses induced by motor execution revealed the alpha power suppression with highest strengths and longest durations. The ERD responses of the movement imagery and movement observation only partially resembled the ERD pattern of the movement execution condition, with slightly better detectability for the ERD responses associated with the movement imagery and faster ERD responses for movement observation. This may indicate different levels of involvement in the same motor-related brain circuits during different movement conditions. In addition, because the resulting conditional EEG ERD responses from the ICA preprocessing came with minimal contamination from the non-related and/or artifactual noisy components, this result can play a role of the reference for devising a brain-computer interface using the EEG ERD features of movement imagery or observation. Movement imagery (MI), which is the mental simulation of a given motor action, elicits the same responses in common neural substrates as does the actually executed action . Ample eAmong the neuroimaging-based markers most applicable for use in the online monitoring of MI task performance are electroencephalographic (EEG) event-related desynchronization (ERD) patterns. In general, MI task performance induces an oscillatory EEG process of desynchronization  in the EEG alpha 8–12 Hz) and beta 16–30 Hz) frequency bands, time-locked to the event onset, similar to that induced by the actual motor execution (ME) –11. Such Hz and b0 Hz freqAlthough MI and MO have been used for stroke rehabilitation training based on the fact that they induce EEG ERD responses in the alpha and beta frequency bands of motor-related neural substrates similar to those elicited by true ME, how reliable the EEG ERD responses induced by the MI and MO are in comparison to those elicited by ME has yet to be sufficiently studied . For exaIn addition to conducting an EEG experiment with multiple movement conditions, we also used independent component analysis (ICA) to identify the common neural substrates activated by the performance of the three movement conditions. In the past, ICA has been used to determine the EEG activities that occur in a given neural substrates when different tasks are performed. In principle, ICA can linearly separate independent neural activities from muscle and eye artifacts, even when those activities and artifacts are acquired together in the form of multi-channel EEG data . Such ICOn the other hand, ICA can also be used to determine the brain activities of common brain circuits across different subjects or subject groups. Onton and colleagues compared the event-related potentials (ERPs) associated with left and right motor-related activity in young and old normal subjects . They fiIn this study, the same motor-related brain areas were identified by applying ICA to the EEG data collected during the three different movement conditions, which were performed consecutively in a pseudorandomized order in the same task runs. In so doing, we expected (1) to exclude contamination from other brain or non-brain activities that were irrelevant to the sensorimotor rhythms induced by the performance of the three motor conditions, and (2) to collect sufficient data to make a direct comparison between the EEG ERD responses in shared motor-related cortices induced by the three different motor conditions. The results of this study may thus make it possible to determine the best scenarios for eliciting a given EEG ERD responses induced by MI and MO in comparison to those induced by ME with minimum artifact contamination. The resultant EEG ERD patterns could also be informative for designating the parameters, such as the onset time, duration, and depth of suppression of the ERDs, for devising a BCI-controlled stroke rehabilitation device based on the MI or MO performance.www.brainmapping.org/shared/Edinburgh.php). All the subjects were recruited from the campus of National Chiao Tung University, Hsinchu, Taiwan. They all had no history of neurological diseases or central or peripheral nervous system injury prior to the experiment and were all naive to any form of BCI neurofeedback. All the subjects provided signed written consent before the experiment, and they were all paid for their participation. The Institutional Review Board (IRB) of China Medical University Hospital (http://www.cmuh.cmu.edu.tw) in Taichung, Taiwan, approved the experimental protocol (IRB no. DMR100-IRB-221). The experiment was conducted according to the principles expressed in the Declaration of Helsinki.Thirteen normal healthy right-handed subjects  participated in this study. The handedness was determined according to the Edinburgh inventory , movement imagery (MI), and movement observation (MO), was conducted. Each EEG experimental run consisted of 15 trials, with five trials for each of the three movement conditions. The three conditions were presented in a pseudoransomized order. Each trial started with a 1-sec fixation window, during which a white cross was displayed at the center of a computer screen for the given subject to fixate on. Next, a visual cue was displayed indicating which of the three motor activities should be performed, and the subject was asked to perform the given action accordingly . For theThe EEG data were acquired using a Neuroscan SymAmp2  with 64 channels, including two EOG channels, via unipolar montage following the International 10–20 system. All electrodes were referenced to the linked mastoids, and the ground channel was placed at AFz. Electrode impedance was maintained below 5K Ohm and checked before and after the recordings. The EEG was recorded with a band-pass filter of 0.1–250 Hz and digitized at the sampling rate of 1000 Hz.The acquired EEG signals for each individual subject were first examined by visual inspection to remove bad EEG channels as well as bad EEG portions with amplitudes greater than 100 uV. Then, the EEG data were filtered using a band-pass filter of 1–50 Hz and downsampled to 250 Hz. After preprocessing, the EEG data of all four runs were concatenated for further analysis.http://sccn.ucsd.edu/EEGLAB) [Before applying independent component analysis (ICA) to the data, the two EOG channels were removed. The concatenated preprocessed data consisting of the EEG data of all 60 motor trials mixed together were then subjected to ICA decomposition. This was done to determine which independent motor-related EEG components were common to all three different motor conditions. As a result, we were able to compare the EEG ERD responses in the same neural substrates underlying the performance of the three different motor tasks. The ICA decomposition was conducted using an infomax ICA algorithm as implemented in EEGLAB (/EEGLAB) . It was In order to find the motor-related independent components from all the subjects and compute group ERDs associated with different motor conditions, all the independent components obtained using ICA decomposition were further subjected to a source localization process using the DIPFIT2 plug-in in the EEGLAB toolbox . The indThe remaining 280 independent components from all 13 subjects were then analyzed using the EEGLAB study analysis function for clustering the equivalent independent EEG components from different individual subjects. The clustering process needed to first compute the feature variables of the (1) component topography, (2) event-related time-frequency plot  in EEGLAB), and (3)  coordinates of the dipole location. The feature variables of the component topography and time-frequency plot were then further summarized using the first 10 principal components. Finally, a K-mean clustering algorithm was used to categorize the independent components into 15 clusters based on the 23-dimension feature space, with one additional outlier cluster for those independent components that failed to be clustered into any of the 15 resultant clusters. However, it was possible that some subjects might contribute more than one component into the target component cluster. When more than one component was found in a given subject, we further computed the time-frequency plots of the components from this subject in the cluster and manually selected the one most resembling the mu-rhythm ERD. The purpose of doing so was to ensure that each subject only contributed one component to the target component cluster.Within the 15 component clusters, the one with its topography mainly covering the right motor areas, which are activated by movement of the left-hand, was selected. Then, the time-domain signals of the selected components were segmented into 4-sec epochs extending from 1 sec before to 3 sec after the onset of the given visual cue. The mean component time-frequency plot was computed using all epochs  from all the selected components of the 13 subjects. The main purpose of computing this grand mean time-frequency plot was to determine the main frequency band with the most pronounced ERD in the EEG data.After the target component cluster was determined and the one equivalent independent component of right motor-related cortex from each subject was identified, the epochs were further separated into three types according to the different motor conditions  in order to compute the group mean ERD of each motor condition. The conditional ERD was computed across all trials within the same condition and across all subjects. We further computed the significance of power suppression across various time points for the selected frequency band relative to the baseline for different motor conditions. In addition, the time-point-by-time-point differences in EEG power suppression for the different motor conditions were further compared using paired t-tests to further test if the EEG power suppression caused by the MI and MO conditions was as reliable as the power suppression caused by real motor execution.In order to evaluate whether the EEG power suppression caused by the MI and MO conditions happened at the same time as the power suppression caused by motor execution, the arrival time of the EEG power suppression for each of the conditions was computed. The arrival time was defined as the time at which the power reached the half maximum of the overall suppression level from the baseline. The arrival time was derived first at the single-subject level by averaging across the trials for a given motor condition and then averaging across the entire subject population. The means and standard deviations of the conditional arrival times were obtained and compared across different motor conditions using a two-sample t-test.The preprocessed EEG data were decomposed into 62 independent components for all the subjects except for one, whose EEG data were decomposed into only 61 components due to the exclusion of one noisy channel prior to the ICA decomposition. After clustering analysis, one of the independent EEG components showing the EEG activity in the right motor-related cortex  was selected from each of the 13 subjects. In order to select the frequency band with the most pronounced ERD responses, a grand mean time-frequency plot was computed and plotted by averaging the time-frequency plots of all the trials for all three motor conditions for all the subjects . As a reAfter the most pronounced frequency band of motor-related ERD response had been identified, the conditional ERD time courses were computed by averaging the ERD time courses from 8 to 13 Hz and across all subjects for each of the motor conditions. The arrival time indicated the time at which the power of the alpha suppression reached the half maximum of the total alpha power suppression. To investigate how reliably EEG ERD responses can be induced by the performance of movement imagery and movement observation as compared to movement execution, we conducted a multitask EEG experiment involving all three different movement conditions with regard to the same action, clenching the left (non-dominant) hand. In the study of Leocani et al. , they reDetermining the ERD responses for given brain regions facilitated by the performance of ME, MI, and MO tasks can be contentious because the spatial distribution of the brain activation maps associated with these three motor conditions remains controversial. For example, Wang et al. combined the methods of ICA and hierarchical clustering analysis (HCA) to find specific activated brain areas associated with visual processing, ME, and MI, as well as with the default mode network . In theiHowever, most of the findings revealing spatial distinctions between the activation maps in response to MI and ME task performance are derived from fMRI or PET neuroimaging modalities with higher spatial resolution. Most EEG/MEG studies, in contrast, have found similar activation maps at the scalp level , 30. MorThe “simulation theory” proposed by Jeannerod asserts that the covert and overt stage of an action should share similar neural circuits, specifically, those in which the action is simulated and executed . Given tAccording to a comparison of the arrival times of the ERD responses for the three motor conditions, the arrival time of the responses for the MO condition was significantly faster than that of the responses for the ME condition . The perIn a seminal work by Hanakawa et al. , the autGiven the above evidence, we hypothesize that performances of MO, MI, and ME tasks facilitate, to different extents, different types of brain activity with instruction-related activity and execution-related activity located on opposite ends of a one-dimensional axis, where MO tasks cause activity located more toward the instruction-related side, ME tasks cause activity located more toward the execution-related side, and MI tasks cause activity located somewhere in between. Because MO tasks might more strongly activate the mirror neuron system and instruction-related brain activity, such tasks may be most effective at facilitating motor preparation activity but least effective at facilitating motor execution brain activity. As a result, among the three types of motor tasks in the present study, the ERD responses associated with the MO task had the fastest average response time but only very brief alpha power suppression because the common brain activation found in this study using ICA might better cover the execution-related side due mainly to the more pronounced brain electrophysiological activity associated with the motor execution condition . An ME tHowever, one could argue that the difference in the arrival times for the MO and ME conditions might simply be caused by that the ME condition has deeper alpha power suppression than the MO condition. As a result, reaching the half-way point from the baseline to the maximum suppression might take longer in the ME condition as compared to the MO condition. Given that the suppression level (depth) of the ERD response in the MI condition is similar to that of the ERD response in the MO condition , if the One of the purposes for this study was to show how reliable the ERDs induced by MI and MO tasks are in order to determine their suitability for use as control mechanisms in BCI applications. Given the preprocessing step of ICA, it is possible to remove the contamination from irrelevant brain or non-brain processes mixed into the channel EEG data during signal acquisition , 39. TheOn the other hand, although the ERD responses to the MI condition seemed to be slightly more pronounced than those to the MO condition, the average duration of the ERD responses and the amplitude of alpha power suppression might still be very difficult for online real-time detection . Such a By applying ICA to the combined EEG data derived from three types of motor tasks, this study demonstrates that all three of those different types of motor tasks, namely, motor execution, motor imagery, and movement observation tasks, can successfully induce alpha suppression in shared motor-related brain circuits. However, the motor-related alpha suppression induced by each type of task differs in terms of both depth and duration of suppression of the alpha EEG power. Because ICA plays an important role here in cleaning the EEG data by separating the motor-related independent EEG process from the artifactual components and other non-motor-related brain processes, the results of this work could provide the most optimistic estimation of how motor imagery- or motor observation-induced ERD can be used for BCI applications. Our results suggest that, at best, only 3–4 dB of suppression from the baseline, with around 500 ms duration starting at 500 ms after an execution cue is delivered, can be used as an EEG MI feature for a BCI application. If further taking into account the contamination in the EEG channel data in a regular BCI setup, more efficient methods of feature extraction and higher quality EEG data may be essential to success.


This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN) and a continuous pairwise Conditional Random Field (CRF) model. Semantic information and relative depth trends of local regions inside the image are integrated into the framework. A deep CNN network is firstly used to automatically learn a hierarchical feature representation of the image. To get more local details in the image, the relative depth trends of local regions are incorporated into the network. Combined with semantic information of the image, a continuous pairwise CRF is then established and is used as the loss function of the unified model. Experiments on real scenes demonstrate that the proposed approach is effective and that the approach obtains satisfactory results. Measuring the depth of a scene is a significant topic of research in photogrammetry and computer vision, which plays an essential role in various applications in 3D reconstruction, video surveillance, and robotics, etc. Much prior work has performed depth acquisition from multiple images taken in accordance with certain requirements ,2,3, butThe task is an ill-posed and inherently ambiguous problem, as one given image may correspond to an infinite number of possible real world scenes . TherefoRecently, the Convolutional Neural Network (CNN) method has become a mainstream of image processing research. Compared with those traditional methods applied to depth reconstruction, CNN can learn a high-level of representation automatically without any manual interventions. Eigen et al.  used a mIn this paper, a unified CNN framework is presented for depth reconstruction from a single image, joining a CNN and a continuous pairwise CRF model. A deep CNN network is firstly designed to automatically learn a hierarchical feature representation of the image. To get local details of the image, relative depth trends of local regions inside the image are integrated into the CNN network. Then, a continuous pairwise CRF is established as the loss function of the unified model through semantic information of a scene and the results of the CNN network in the first step. Depth reconstruction is formulated as a CRF learning problem and can be solved by maximum a posteriori (MAP) inference.The approach performs pixel-level depth reconstruction from a single image in a unified CNN model framework, shown in n indexes superpixels via over-segmentation, the pairwise CRF are modeled as:Given an image Once the parameters The unitary part to obtain depth regression of each superpixel in the image uses a deep CNN model for learning feature representation of all the superpixels. The unitary potential Usually, the depth of a superpixel is calculated with a single value. However, it is too coarse since depth values of different pixels inside the superpixel may be different. Fortunately, there are many local regions with similar structure from a sematic class, which means that their relative depth trends are nearly same, shown in To obtain depth templates for each semantic label, the normalized depth maps of all the superpixels with the same sematic label are clustered. In this paper, relative depth trends of the superpixels, which are represented by the depth templates, are incorporated into the CNN network. To obtain the absolute depth values of each pixel inside a superpixel, the outputs of the CNN network for the unary part are designed as the depth value at the superpixel center and its normalized scale factor. The structure of the CNN model is similar to that described by Liu et al. , but theThe pairwise part considers the depth relationships between neighboring superpixels, combined with their similarity and semantic information. The pairwise potential of the CRF model is constructed as:The second term The loss function of the depth reconstruction model uses the negative log-loss of the pairwise CRF, shown in Equation (1). According to Equations (4) and (5), the potential of the CRF can be expressed as:Then Equation (1) can be written as:Here (1)mean relative error (Rel): (2)root mean squared error (Rmse): (3)mean log10 error (Log10): where id is the corresponding ground-truth depth, and The proposed method is evaluated on the Make3D dataset . The MakAs pointed out in , the ranTo evaluate the quantitative results of the proposed method, several state-of-the-art methods are used for comparison. Additionally, considering the influence of the constraint information including sematic information, relative depth trends and CRF on the results, experiments with the dataset are performed, which share the same model with the proposed approach except integrating the constraint information.In the experiments, depth maps are predicted via the CNN model with different constraint information. The results are shown in From the results illustrated in (1)The method through Sematic_constrained can get more satisfactory results compared with Unconstrained, which demonstrates the semantic information is an effective cue for depth reconstruction.(2)Likewise, the relative depth trends of local regions are helpful to depth reconstruction because the results via Local_constrained outperform Unconstrained.(3)The errors of depth reconstruction through Eucli_loss are lower than Unconstrained. This is mainly because their loss functions are different. Eucli_loss uses a Euclidean loss as the loss function of the model. Unlike Eucli_loss, Unconstrained uses a pairwise CRF to establish the loss function, which can consider depth consistency and smoothness between the neighboring superpixels.(4)As result of the semantic information, the relative depth trends and the pairwise CRF incorporated into the model, the proposed approach can get more satisfactory results than other methods.To show the effectiveness of the proposed approach, several state-of-the-art methods are tested for comparison:Saxena et al. : The metLiu et al. : Based oDepth transfer : The metDC CRF : In the DCNF : The metThe results of these methods are shown in (1)DCNF  and the (2)The proposed approach can get more satisfactory results than DCNF , becauseFrom the results illustrated in Besides, depth maps are reconstructed for some images not in the Make3D dataset, but from the Internet, which further demonstrate the effectiveness of the proposed approach in Through the experiments, it is observed that the proposed method is successful at depth reconstruction from a single image with satisfactory accuracy. The proposed approach for depth reconstruction uses a unified CNN framework, joining the advantages of the CNN and the continuous pairwise CRF model. On the one hand, it can the automatically learn hierarchical feature representation of the image via CNN model rather than hand-crafted mode. On the other hand, depth reconstruction is formulated as a CRF learning problem rather than a regression problem due to the loss function that uses a continuous pairwise CRF instead of a Euclidean loss. In the continuous pairwise CRF, the depth consistency and smoothness of neighboring superpixels are considered. Additionally, the unified framework incorporates into the sematic information and relative depth trends of local regions, which can be helpful to resolve depth ambiguities and provide more local details in the image. Therefore, depth reconstruction through the proposed approach is effectiveness and has some improvements.In this paper, the development and implementation of a new approach for depth reconstruction from a single image is presented. A unified framework joining a CNN and pairwise CRF model is used to obtain depth information. A particular feature of the approach is that semantic information and relative depth trends of local regions are integrated into the unified framework. A series of experiments on Make3D dataset are presented in this paper. The experiments with different constraint information demonstrate that the semantic information, the relative depth trends of local regions and CRF model are helpful to depth reconstruction from a single image. The experimental results show that the proposed method is effective and suitable for depth reconstruction.