Pruning the ranked features to 5 leaf nodes reduced the error rate by 16%. For decoding grasped movement and in the context of BCI applications, this study identifies potential biomarkers, including the spatio-temporal characteristics of MRCPs, spectral information, and choice of classifiers for optimally distinguishing initiation and grasped movement.For brain-computer interfaces, resolving the differences between pre-movement and movement requires decoding neural ensemble activity in the motor cortex’s functional regions and behavioural patterns. Here, we explored the underlying neural activity and mechanisms concerning a grasped motor task by recording electroencephalography (EEG) signals during the execution of hand movements in healthy subjects. The grasped movement included different tasks; reaching the target, grasping the target, lifting the object upwards, and moving the object in the left or right directions. 163 trials of EEG data were acquired from 30 healthy participants who performed the grasped movement tasks. Rhythmic EEG activity was analysed during the premovement  condition and compared against grasped movement tasks while the arm was moved towards the left or right directions. The short positive to negative deflection that initiated around -0.5ms as a wave before the onset of movement cue can be used as a potential biomarker to differentiate movement initiation and movement. A rebound increment of 14% of beta oscillations and 26% gamma oscillations in the central regions was observed and could be used to distinguish pre-movement and grasped movement tasks. Comparing movement initiation to grasp showed a decrease of 10% in beta oscillations and 13% in gamma oscillations, and there was a rebound increment 4% beta and 3% gamma from grasp to grasped movement. We also investigated the combination MRCPs and spectral estimates of α, β, and γ oscillations as features for machine learning classifiers that could categorize movement conditions. Support vector machines with 3 Patients with amyotrophic lateral sclerosis (ALS) or spinal cord injury (SCI) are reported to have significant loss of voluntary motor control and extensive dysfunction of upper and lower limbs –4. RehabNeural correlates of voluntary grasped movement are relevant in the development of modern prosthetics and towards Brain-Computer Interface (BCI) research –10. InsiEEG-based studies have explored movement initiation or intention and execution to be associated with μ/α and β rhythms , althougThe neural components correlated to hand-reaching that occurred within the medial parieto-frontal circuit including the medial intraparietal area (mIP) at the boundaries with area V6A and the dorsal pre-motor areas have been explored . Neural Assessing synchrony and desynchrony of rhythmic oscillations had allowed dissecting cortical preparation or execution of voluntary movements –54. In tOther than μ or β band activity, studies have reported γ band activity in the motor and premotor areas from intracranial electrode recordings and electrocorticogram recordings –69. The There has been an increased demand for wearable technologies and especially, limited-channel systems for personal use. Comparing clinically graded systems and consumer systems and the applications –74 have In other BCI studies –80, variSince lower resolution EEG was less expensive and easier to implement than the other temporally precise methods, and with the limits of EEG spatial resolution not well understood, whether specific EEG electrodes are sufficient for movement-related spatial information remains unanswered. This study explores the EEG dynamics by employing a novel protocol to understand movement intention and execution during grasped movement tasks performed in the left and right directions. While exploring the activity space underlying grasping controlled by parieto-frontal circuits  and centIn our study, the rebound activity of β and γ oscillations after the movement and the slow negative deflection amplitude shift in the temporal domain were explored as these features could be potential cortical activity biomarkers for grasped movement. In this paper, we also evaluated feature-based classification of pre-movement and grasped movement using machine learning and have enumerated ranked features from these tasks that may be relevant for constructing efficient classifiers for clinically relevant movement EEG data.Thirty healthy volunteers aged 18 to 30 years (mean age = 22.32 ± 1.92 years) participated in this study. This non-invasive study was reviewed and approved by the institutional ethics committee at the university. All subjects involved in this study were without any known prior medical conditions and with normal or corrected-to-normal vision. All subjects were explained the aim of the study and signed informed consent were collected before their participations in the recordings. The tasks included were grasped movement of bottle towards using left hand towards the left direction and with right-hand towards the right direction.The experimental setup and the task paradigm were adapted from  see Fig. A cue-bPrior to the experiment, participants were provided training to adapt to the task by progressive steps of hand movement by grasping the object. Before recordings, the subjects repeated the same procedure three or four times until they attained familiarity to perform the task.The experimental paradigm was conducted as outlined in the following steps.All trials commenced with a relaxation phase (blank screen), considered as a reference or baseline signal for the analysisThe subject was asked to relax for ten seconds.The subject was alerted for the following task by a ‘+’ sign cue for 5 seconds.The word ‘Reach’ as a cue was shown for 5 seconds, indicating the subject to reach the object.Then an image of a bottle was shown to the subject, indicating to grasp the bottle placed in front of the table. This cue was presented for five secondsAn upward arrow cue (↑) was shown for next 5 seconds indicating to the subject to lift the bottle to the chin level.This was followed by a leftwards arrow cue (←) for five seconds; subject was then instructed to move the bottle towards left direction using the left hand.Following this, a down-wards arrow cue (↓) was shown for 5 seconds indicating to the subject to place the object back down on the tableA blank screen was shown as an indication of the end of the experiment trialThe same steps were repeated for right-hand right direction movement task as well.The tasks were carried out using both hands for the two movement directions in trials defined as right-hand leftward direction (RHLD), right-hand rightward direction (RHRD), and left-hand leftward direction (LHLD), and left-hand rightward direction (LHRD). However, only LHLD and RHRD have been considered for the analysis.The study included the acquisition of EEG data by placing EEG sensors on the subject’s scalp. For clinically relevant data, we used a 32-electrode commercially available device  positioned on the scalp according to the 10–20 international system with a sampling rate of 500Hz.Considering commercial and limited electrode platforms, we also employed a 14-electrode device (EMOTIV EPOC+) with a sampling rate of 128 Hz. EEG signals were recorded for 10 subjects using a 32- electrode device. For each subject, four trials were performed per task. So, a total of 40 trials were performed for 10 subjects. Due to the loss of data packets, EEG data of 12 trials were discarded. Using the 14-electrode device, EEG signals were recorded for 20 subjects. In this, 10 trials were performed for 6 subjects, 5 trials for 12 subjects, 13 trials for 1 subject, and 2 trials for 1 subject have been carried out and EEG signals were recorded. Out of 175 trials, 163 were used for the data analysis, and 12 trials were rejected due to the loss of signal packets during the recording.X1, X2……., Xn, n number of components in this case total number of channels have considered n = 32 number of components. signal X:Data analysis was performed using custom scripts written in Python incorporating the ‘mne’ package functions . To obtaA represents mixing matrix with size n×n and s was the vector of independent components. The mixing effect, after computing the matrix generated the independent componentsk−learning rate which depended on k; g (.)–a nonlinear function. g(y) logistic function.To obtain the independent components in this study, we used the infomax algorithm based on general optimisation principle for neural networks and other processing systems , 94. TheFurther, data was segmented into non-overlapping epochs of 2s for the timelines of 0s - 10s (Relax Task), 10s-15s (premovement task), and 30s -35s (Left hand left direction movement / Right hand right direction movement). Consequently, the segmented data was used to estimate the spectral features. The spectral estimations of each rhythm were quantified using multitaper power spectral density (PSD) estimation , 96. TheFor temporal cortical potential components , The MRCx and σx values were mean and standard deviation of x (channel 1) and the μy and σy were the mean and standard deviation of y (channel 2) respectively. The goal of the correlation analysis was to identify the optimal electrode position for the relax, premovement and movement tasks. Further, the correlated channels were compared to test the significance of correlated channels for each task using multiple t-test statistics. Statistical significance was determined using the Holm-Sidak method, with alpha = 0.05. Each row (correlated pair combination of electrode) was analysed individually, without assuming a consistent SD.Correlation analysis was carried out to determine the best electrode positions for the analysis of MRCPs. The data from channels  were selected and used for correlation analysis. The relation for electrode positions in different regions of cortical regions from MRCPs was analysed using Pearson correlation coefficient. For each electrode channel and N scalar observations (time points), the correlation coefficient was computed.2 analysis. The value of χ2 was estimated using the formula,O was the observed frequency count and FE was expected frequency count of dependent features, the confidence interval chosen was 0.05. Similar analysis was performed to find the association between frequency band and tasks .The association between frequency band oscillations in the three tasks and the subject’s gender was tested using χTo better understand the relationship between changes in PSD for central electrode regions across different frequency bands for all 28 trials, PSD for tasks  was compared. A two-way repeated measure of ANOVA with Tukey’s multiple comparison posthoc test was performed on the average of each feature extracted from the relax, pre-movement and grasped movement conditions. Similarly, we compared the frequency band features for the task’s male and female subjects for all the trials, to identify the statistical significance of the features discriminating among the male and female subjects for each task using two-way ANOVA analysis. All the statistical analysis was performed using GraphPad Prism .Gini’, splitter: ‘best’), SVM model Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: NoReviewer #2: No**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: NoReviewer #2: No**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. 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Reviewer #1: 1- The title is too long, and can be truncated to: Exploring EEG spectral and temporal dynamics underlying a hand grasp movement2- The abstract should be revised for grammatical, structural and typing errors, such as:The data acquisition was done for 163 trails of 30 ...Instead, say: The EEG data was acquired from 30 participants, where each performed 163 trials of a hand grasping movement.3- The introduction should be grammatically re-structured, too many faults.ex: Neural correlates to voluntary grasped movement .. should be: Neural correlates of4- In the methods: mean age +/- std ?Amrita Vishwa Vidyapeetham: If this is the name of the university, please write 'university'.In this study, a 32-electrode and a 14-electrode recording systems were employed: which one exactly ? and why using 2 systems instead of either one ?total of 173 trials were recorded for each task: for each participant ?Every subject participated in two sessions of 4 trials and every trial was separated by two minutes of resting: 4 trials or 173 ?what is is total duration of the experiment ?what is the total duration of each trial ?in line 174: the ”, for what ?The grasp should be done in how many seconds ? how the participant knew the start of a new trial ? hwo does the system knew the end of a trial and the beginning of a new trial ?2.3. Data Acquisition: here you say 32 electrodes!line 188: is it the raw EEG data ?line 224: what is the length of each sample ? what are these samples (features) ? only filtered EEG data ? spectral ? in which EEG bands ?PLEASE re-write the methods section as many details are not present.PLEASE re-write the discussion section for structural errorsReviewer #2: This paper presents the analysis and classification of EEG signal recorded during grasp movement. The work consisted of an experimental task were healthy participant moved any of the arms in a synchronized experiment. One critical problem with this study is the lack of novelty, many aspects are presented in previous literature. Other problem is with analyses which are very poor and require significant improvement.Some comments1) What is the motivation to carry out this research? What is the scientific problem you are addressing?2) Methods section: 1st paragraph: were the 173 trials recorded for each participant or for all of them? What are the tasks?3) Experimental Design & Procedure: as I understand, each subject participant in two session and four trials were recorded per session.4) In general, the description of the experimental task. For instance, “Experimental Design & Procedure:” did not presented anything about the use of both hand and two directions, but this information is subsequently presented in “Trial structure:”. Authors should improve the description of the experiment. In consequence, it seem that only two trials for hand and direction are recorded.5) In the 1st paragraph of “Methods” you said that “In this study, a 32-electrode and a 14-electrode recording systems were employed.”, but then in “2.3. Data Acquisition:” you said that “we used a 32-electrode commercially available device”. This is highly confusing.6) In many places there are not space between words and references, while in other places there are. Or the reference is after the end point of a sentence. Please review.7) “The relative band power was estimated based on the brain activity bands for left hand movement and right-hand movement tasks.”: this seems to be a critical step that required further explanations. Its is not possible to replicate the procedure in its current state8) More details and/or results about the use of ICA to remove artefactual EMG components embedded in the EEG should be given.9) The data analysis descriptions is very plain and this is an important aspect of the research. In its current state is it not possible to replicate the procedures therein.10) In the preprocessing of the data, why did you apply two filtering steps? I do not see the need for that since at the end only the BPF has an impact on the signals11) The methods do not show how the MRCP are extracted from the preprocessing EEG signals.12) It is not clear what you did in “2.5. Statistical measures:”. This needs significant rewording. In consequence, it is nor clear how the statistical analysis were done.13) There are organization problems. One the is that you call figure 1, then figure 5.14) “The dataset contained 270 samples has a sampling rate of 128 Hz and 56 samples 225 dataset has a sampling rate of 500 Hz.” Why are there two sampling frequencies?15) I am not sure there you have MRCP since you are removing all frequency content below 1Hz.16) What time windows of the trial were used to classify? What are the number of classes/categories? What is the reason to employ several classifiers? What is the dimension of the input vector? There are several open questions in the machine learning analysis.17) Why to decode movement since in real BCI setting with final users (patients) they are not able to move or at best they only have residual movements 18) The number of trials is very limited for each participant19) It seems you are combining data from all participants. This is not a common strategy in the analysis of EEG signals, and they are usually done subject specific.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Yes: Bilal AlchalabiReviewer #1: Reviewer #2: Nohttps://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  27 May 2022We thank the editor and the reviewers for their constructive and valuable comments, and we have used the same to address changes in the manuscript. We have included additions also to our supplementary material. Reviewer 1: 1. The title is too long, and can be truncated to: Exploring EEG spectral and temporal dynamics underlying a hand grasp movementThe title has been modified to "Exploring EEG spectral and temporal dynamics underlying a hand grasp movement", as suggested by the reviewer. Thank you for the suggestion. 2. The abstract should be revised for grammatical, structural, and typing errors, such as:The data acquisition was done for 163 trails of 30. Instead, say: The EEG data was acquired from 30 participants, where each performed 163 trials of a hand grasping movement.Thank you for the comment. The abstract has been revised, and the manuscript was checked for grammatical and language errors and typos. The abstract has been slightly modified (line number 16 to 38 on page number 2) to reflect these adaptations. 3. The introduction should be grammatically re-structured, too many faults.ex: Neural correlates to voluntary grasped movement. should be: Neural correlates ofThank you. Agreeing with the reviewer, we have revised the manuscript. As suggested by the reviewer, the statement on page number 4, line 49 have been rewritten as "Neural correlates of voluntary grasped movement are relevant in the development of modern prosthetics, and towards Brain Computer Interface (BCI) research ." 4. In the methods: mean age +/- std?Thirty healthy volunteers aged 18 to 30 years (mean age = 22.32 ± 1.92 years) participated in this study. This has been added to the methods section on page 9, line number 177) 5. Amrita Vishwa Vidyapeetham: If this is the name of the university, please write 'university'.As per Indian legal records, the university is called "Amrita Vishwa Vidyapeetham", where "Vishwa Vidyapeetham" is the Sanskrit term for university. However, for practical reasons, as indicated by the reviewer we have used the term "University". The corrections can be seen on page number 9 at line number 179 in the manuscript. 6. In this study, 32-electrode and 14-electrode recording systems were employed: which one exactly? and why using 2 systems instead of either one? In this study both 32 -electrode medical graded system and 14 electrode consumer graded systems have been used. The consumer graded 14 electrode device is equipped with Frontal, temporal, parietal and Occipital sensors  and the medically graded 32 electrode device is equipped with 32 sensors from the same regions including central region . Based on the study of comparison of medical and consumer EEG systems by Ratti et al., 2017 consumer systems were prone to artifacts in frontal regions, medical devices offer clear advantages in data quality, reliability, and depth analysis over consumer systems.Since the study is more focused in clinically aspects and application of neuro prosthesis at consumer level, it was crucial to look at the accuracy from obtained features using both systems and to map neural activity that were not restricted to frontal and temporal regions only.Though 32-electrode can provide a greater dataset compared to the 14-electrode, the 32-electrode device was more expensive . As researchers based in a developing country like India, we also are focused on using and implementing how low-cost devices can be used reliably. With this as a possible alternative for recordings outside lab settings, we have checked whether 14-electrode device provided similar or reliable accuracy like that of a 32-electrode device. The results suggest that features from central electrode regions provide reliable accuracy for premovement and movement tasks, compared to frontal electrode regions. The clear discrimination of MRCP pattern for movement initiation was observed from the central region electrodes. In the present manuscript, introduction section on page 7, line number 135 to 140 and on page 8 line 156 to 166 we have now modified the text with description. We thank the reviewer for the comment. 7. total of 173 trials were recorded for each task: for each participant? Every subject participated in two sessions of 4 trials and every trial was separated by two minutes of resting: 4 trials or 173?In this study, we have recorded 175 trials from 30 subjects. Off these 30 subjects, 10 subjects were recorded using 32-electrode system. For each subject, four trials per task were carried out. 40 trials were performed with 10 subjects. Due to the loss of data packets, EEG data of 12 trials were discarded. For 20 subjects, the 14-electrode system was used. With that 14-electrode device, 10 trials were performed for 6 subjects, 5 trials for 12 subjects, 13 trials for 1 subject, and 2 trials for 1 subject have been carried out and EEG signals were recorded. The recording for each task was taken in two separate sessions, for LHLD, RHRD tasksWe have included this information in the present draft on page 11, line number 233. We thank the reviewer for the comment. 8. what is total duration of the experiment? what is the total duration of each trialEach recording employed in this study was for 45 seconds and considered as a trial of the experiment. We have reedited the methods in the manuscript  as read below. A cue-based paradigm  was employed where subjects were presented with visual cues using a slide presentation of the duration of 45 seconds. we have recorded each trial as an experiment. EEG signals were recorded for 10 subjects using a 32- electrode device. For each subject, four trials were performed per task. So, a total of 40 trials were performed for 10 subjects. Due to the loss of data packets, EEG data of 12 trials were discarded. Using the 14-electrode device, EEG signals were recorded for 20 subjects. In this, 10 trials were performed for 6 subjects, 5 trials for 12 subjects, 13 trials for 1 subject, and 2 trials for 1 subject have been carried out and EEG signals were recorded. Out of 175 trials, 163 were used for the data analysis, and 12 trials were rejected due to the loss of signal packets during the recording.9. in line 174: the", for what?Thank you for pointing out a typo, we have edited the manuscript 10. The grasp should be done in how many seconds? how the participant knew the start of a new trial? How does the system knew the end of a trial and the beginning of a new trial?The protocol has been recorded for 45 seconds duration with different cues, The Relax cue was show for 10 seconds, alert cue was shown for 5 seconds, reach cue was shown for 5 seconds, grasp cue was shown for 5 seconds, movement cues were shown for 5 seconds and stop cue with blank screen shown for 5 seconds. The next trial was started after 3 minutes break. The blank screen cue presentation indicates the beginning and end of the trials. This has been incorporated in the Methods sections, Experimental Paradigm on page 10, Line numbers 205-220 clarified with different steps of the protocol in the manuscript. 11. Data Acquisition: here you say 32 electrodes!We have now edited the methods to clarify this point as well. As indicated previously, in this study, we have recorded from two different set of experimental devices with frontal and central electrode sensors and have analyzed data separately. The results suggest that features from central electrode regions have more accuracy prediction rate for premovement and movement tasks, compared to frontal electrode regions. The clear discrimination of MRCP pattern for movement initiation was observed from the central region electrodes. For clinically relevant data, we used a 32-electrode commercially available device  positioned on the scalp according to the 10–20 international system with a sampling rate of 500Hz. Considering commercial and limited electrode platforms, we also employed a 14-electrode device (EMOTIV EPOC+) with a sampling rate of 128 Hz. EEG signals were recorded for 10 subjects using a 32- electrode device. For each subject, four trials were performed per task. So, a total of 40 trials were performed for 10 subjects. Due to the loss of data packets, EEG data of 12 trials were discarded. Using the 14-electrode device, EEG signals were recorded for 20 subjects. In this, 10 trials were performed for 6 subjects, 5 trials for 12 subjects, 13 trials for 1 subject, and 2 trials for 1 subject have been carried out and EEG signals were recorded. Out of 175 trials, 163 were used for the data analysis, and 12 trials were rejected due to the loss of signal packets during the recording. Thank you for the question. 12. line 188: is it the raw EEG data?We refer to the raw data acquired from the device was detrended to minimize the drifts and further band pass filtered 1- 60 Hz range. This now reads edited as line 246 on page 12. Here is the edited text from the manuscript. To obtain the spectral components initially the data was high pass filtered (1Hz) to minimise the drifts [88]and the reference (mean) was subtracted, further the data was detrended [89] and bandpass filtered using an FIR filter  of order 20 within the range, 1Hz - 60 Hz and notch filter was applied to remove line noise in the range of 50 Hz. 13. line 224: what is the length of each sample? what are these samples (features)? only filtered EEG data? spectral? in which EEG bands?We have used two different datasets from two different devices, the first dataset with 56 samples  with a binary class of premovement & movement consists of 74 columns which are features , alpha band, beta band, and gamma-band). The second dataset with 270 samples  consists of 67 columns which are features.The features are readiness potential peak and spectral oscillations of alpha, beta and gamma band obtained after preprocessing the raw EEG data. 14. PLEASE re-write the methods section as many details are not present. Thank you for the comment. We have rewritten the methods section as suggested. We have edited the “Experimental Design & Procedure” completely restructuring it as Experimental Paradigm. The present draft in the manuscript from the line number 187 to 230 has been re-addressed with stepwise procedure to perform the experiment and has included number of subjects considered for data acquisition and number of trails recorded in total from all the subjects. We have also modified the trial structure part as a step-by-step method for ease of clarity and understanding (from line number185 to 225 at page number 11)In the methods section the offline processing data was added and indicates our methods for power spectral analysis and movement related cortical potentials. . The statistical analysis was also re-structured and elaborated (page number 14 line numbers 293- 332 in the manuscript). 15. PLEASE re-write the discussion section for structural errorsWe apologize for the issues in the previous draft and thank the reviewer for the comment. We have avoided structural issues (page 21 at line numbers 457 -513) and redrafted the discussion section.Reviewer 2:1. What is the motivation to carry out this research? What is the scientific problem you are addressing?We thank the reviewer for this question. Our motivation for this study is now part of the introduction in the present manuscript on page 4, line number 43 -48 and on page 8, line number 159-166   frequency bands and readiness potentials for grasped-movement. Patients with amyotrophic lateral sclerosis (ALS) or spinal cord injury (SCI) are reported to have significant loss of voluntary motor control and extensive dysfunction of upper and lower limbs [1–4]. Rehabilitation using robotic or functional electrical stimulation could facilitate novel therapeutic strategies for such conditions. Integration of signals could also be a potential goal for Brain-Computer Interfaces (BCI), allowing the transfer of control information from a prosthesis onto the brain tissue [5–7]. Neural correlates of voluntary grasped movement are relevant in the development of modern prosthetics and towards Brain-Computer Interface (BCI) research [8–10]. Insights from electroencephalography (EEG) recordings and their underlying neurophysiological processes involved in motor tasks can help decode movement and its functional interpretation. EEG signal components related to movement allow noninvasive measurements, making them suitable for natural BMIs . In addition, these correlates are measurable in paralyzed patients . In this study, we explored the EEG dynamics by employing a novel protocol to understand movement intention and execution during grasped movement tasks performed in the left and right directions. Exploring the activity space underlying grasping controlled by parieto-frontal circuits [86] and central regions, this paper also attempts to relate the variations in movement initiation and grasped movement by developing classification models based on features of temporal and spectral correlations specifically with a combination of α, β, and lower �2. Methods section: 1st paragraph: were the 173 trials recorded for each participant or for all of them? What are the tasksWe thank the reviewer for the question. We have redrafted methods to reflect the trials, questions, and procedure. In this study, 175 trials recorded for all the participants., The tasks included left hand grasped left direction movement (LHLD), a right hand grasped right direction movement (RHRD), and a premovement. These tasks followed timelines based on the cueing paradigm and were performed by subjects for 45 seconds for each trial.As per the reviewer’s suggestions, we have reedited the methods and have included step by step experimental paradigm in the manuscript on page 10, line number 187-225. The experimental paradigm was conducted as outlined in the following steps. 1. All trials commenced with a relaxation phase (blank screen), considered as a reference or baseline signal for the analysis2. The subject was asked to relax for ten seconds.3. The subject was alerted for the following task by a ‘+’ sign cue for 5 seconds. 4. The word ‘Reach’ as a cue was shown for 5 seconds, indicating the subject to reach the object. 5. Then an image of a bottle was shown to the subject, indicating to grasp the bottle placed in front of the table. This cue was presented for five seconds 6. An upward arrow cue (↑) was shown for next 5 seconds indicating to the subject to lift the bottle to the chin level. 7. This was followed by a leftwards arrow cue (←) for five seconds; subject was then instructed to move the bottle towards left direction using the left hand.8. Following this, a down-wards arrow cue (↓) was shown for 5 seconds indicating to the subject to place the object back down on the table9. A blank screen was shown as an indication of the end of the experiment trialThe same steps were repeated for right-hand right direction movement task as well. The tasks were carried out using both hands for the two movement directions in trials defined as right-hand leftward direction (RHLD), right-hand rightward direction (RHRD), and left-hand leftward direction (LHLD), and left-hand rightward direction (LHRD). However, only LHLD and RHRD have been considered for the analysis.The same steps were repeated for right hand right direction of movement 3. Experimental Design & Procedure: as I understand, each subject participant in two sessions and four trials were recorded per session.In this study, 175 trials recorded from 30 subjects. Out of these 30 subjects, 10 subjects were using 32-electrode system. For each subject, four trials were performed per task. So, a total of 40 trials were performed for 10 subjects. Due to the loss of data packets, EEG data of 12 trials were discarded. Yes, each participant has participated in two sessions to acquire the two separate directions of movement left-hand left direction movement and right-hand right direction movement, the recording for each task was taken in two separate sessions, . This has been incorporated in the manuscript methods section on page number 11 from line number 233 can be read as below paragraph.EEG signals were recorded for 10 subjects using a 32- electrode device. For each subject, four trials were performed per task. So, a total of 40 trials were performed for 10 subjects. Due to the loss of data packets, EEG data of 12 trials were discarded. Using the 14-electrode device, EEG signals were recorded for 20 subjects. In this, 10 trials were performed for 6 subjects, 5 trials for 12 subjects, 13 trials for 1 subject, and 2 trials for 1 subject have been carried out and EEG signals were recorded. Out of 175 trials, 163 were used for the data analysis, and 12 trials were rejected due to the loss of signal packets during the recording4. In general, the description of the experimental task. For instance, "Experimental Design & Procedure:" did not present anything about the use of both hands and two directions, but this information is subsequently presented in "Trial structure:". The authors should improve the description of the experiment. In consequence, it seems that only two trials for hand and direction are recorded.Thank you for the suggestions, we have restructured the experimental design & procedure into a single structure called experimental paradigm and have included in the manuscript on page number 10-11 lines 187 – 225.5. In the 1st paragraph of "Methods" you said that "In this study, a 32-electrode and a 14-electrode recording systems were employed.", but then in "2.3. Data Acquisition:" you said that "we used a 32-electrode commercially available device". This is highly confusing.This was an important choice for us and we thank the reviewer for raising the question. Both 32 -electrode medical graded system and 14 electrode consumer graded systems have been used in this study. The consumer grade 14 electrode device was equipped with Frontal, temporal, parietal and Occipital sensors  and the medical grade 32 electrode device was equipped with 32 sensors from the same regions including central region . Based on the study of comparison of medical and consumer EEG systems by Ratti et al., 2017 consumer systems were prone to artifacts in frontal regions, medical devices offer clear advantages in data quality, reliability, and depth analysis over consumer systems.Since the study was focused on interpreting signal aspects for evaluating pre and movement information at consumer level, it was helpful to look at the accuracy of obtained features using both systems and in order to map neural activity that was not related to frontal and temporal regions.The 32-electrode could yield more data compared to the 14-electrode and yet, the 32-electrode device was more expensive . Apart from this paper, it was crucial to evaluate the applicability of low-cost devices especially in off laboratory settings. With this as an additional reason, we employed the 14-electrode device and tested if results were reliable as performed on a 32-electrode. Our results suggest that features from central electrode regions yielded better accuracy for premovement and movement tasks, compared to frontal electrode regions. The discrimination of MRCP patterns for movement initiation was observed from the central region electrodes. In the present manuscript, introduction section on page 7, line number 135 to 140 and on page 8 line 156 to 166 we have now modified the text with description. 6. In many places there are not space between words and references, while in other places there are. Or the reference is after the end point of a sentence. Please review.Thank you for pointing out this issue. We have corrected the same in the present draft of the manuscript .7. "The relative band power was estimated based on the brain activity bands for left hand movement and right-hand movement tasks.": this seems to be a critical step that required further explanations. It is not possible to replicate the procedure in its current stateWe thank the reviewer for pointing out our short sightedness in this regard. Taking the reviewer’s suggestion, the methods section has been modified and details have been added (line number 277 on page 13). 8. More details and/or results about the use of ICA to remove artefactual EMG components embedded in the EEG should be given.ICA analysis was performed to remove any artifacts related to EMG, EOG, from the components. AS per the author's knowledge, the authors haven't observed most of the EMG artefactual components in the data, but mostly in the data the first component has seen EOG artifacts which have been removed during the pre-processing stage. In this study Infomax ICA algorithm was used from python mne package to do ICA analysis. The detailed description of ICA method has been edited in the manuscript on page 12 lines 251- 271. 9. The data analysis descriptions is very plain and this is an important aspect of the research. In its current state is it not possible to replicate the procedures therein.Thank you for the comment. The manuscript has been modified to reflect changes in the methods section (per say line number 244 on page 12). The present manuscript has a section on data analysis (as listed under Offline processing of EEG data).10. In the pre-processing of the data, why did you apply two filtering steps? I do not see the need for that since at the end only the BPF has an impact on the signals. We thank the reviewer for the comment. We have revised the methods section to address the filtering aspects (page numbers 12 & 13 at line numbers 247 & 286). In our study, we have used high pass filter above 1Hz to minimise the drifts. Spectral oscillations in the frequency range 1Hz to 60 Hz were estimated. To obtain MRCPs, the EEG data was high pass filtered for the 0.1Hz to 5 Hz range. 11. The methods do not show how the MRCP are extracted from the pre-processing EEG signals.Thank you for the comment. We have now edited the methods to include on extracting MRCPs. . 12. It is not clear what you did in "2.5. Statistical measures:". This needs significant rewording. In consequence, it is nor clear how the statistical analysis were done.We agree with this comment. Thank you. In the present draft of the manuscript, we have rewritten the methods section (page 14 line numbers 294 to 329). 13. There are organization problems. One the is that you call figure 1, then figure 5.Thank you for pointing out that oversight from our side. We have modified the manuscript. 14. "The dataset contained 270 samples has a sampling rate of 128 Hz and 56 samples dataset has a sampling rate of 500 Hz." Why are there two sampling frequencies?Two different datasets have been used for the analysis obtained from medically graded acquisition device which had 500 Hz sampling rate and other one was a consumer grade device at 128Hz sampling rate. The dataset obtained from consumer grade device contains 270 samples  and the second dataset obtained from medical graded device contains 56 samples/trials (28 premovement/ 28 movement) 15. I am not sure there you have MRCP since you are removing all frequency content below 1Hz.We thank the reviewer for this comment. The methods section has been modified to show analysis for spectral components and for MRCPs separately (line number 285 on page 13). To obtain the MRCPs, a bandpass filter of 0.1 Hz – 5 Hz was exployed. High pass filter 1Hz was used to obtain the spectral components. 16. What time windows of the trial were used to classify? What are the number of classes/categories? What is the reason to employ several classifiers? What is the dimension of the input vector? There are several open questions in the machine learning analysis.We thank the reviewer for the comment. In this paper, time windows of 10s(premovement), and 30s (Left hand grasped movement / right hand grasped movement) of the trial were used in the classification. We chose binary class labels ,  and have used two datasets from 2 devices; with the first dataset dimension is 170 * 67 and the second dataset dimension is 56 * 74. We have reported this in our re-edited methods section, which has a Machine learning subsection . 17. Why to decode movement since in real BCI setting with final users (patients) they are not able to move or at best they only have residual movements Our expectation here was that we targeted the classification for future tools and prostheses. We have started an ongoing study to allow BCI based transfer of control using robotic devices and within such contexts both peri-movement and movement were crucial. Sensory and Cognitive functions of the brain are only minimally affected by amyotrophic lateral sclerosis (ALS) or spinal cord injury (SCI). Rehab with robotic interfaces is also a potential goal for Brain Computer Interfaces (BCI) leading to a possible control or transfer of control signals from prosthesis to actual brain tissue. We included some aspects of this into our introduction section (page number 4 from line 43-59). 18. The number of trials is very limited for each participantWe agree that the number of trials (175) were limited. This was also due to restrictions imposed by COVID. We have however considered all trials together from the subjects as inter subject modelling generalizing the problem using different datasets from various electrode positions for classification with binary class labels. We do hope to extend this study with other BCI components and with our 256-electrode device as we limp back into some form of normalcy after the COVID lockdowns. The number of trials and the subjects has been mentioned in the present manuscript at methods section on page 11 from line numbers 227 – 242.19. It seems you are combining data from all participants. This is not a common strategy in the analysis of EEG signals, and they are usually done subject specific.Thank you for the comment. For the relevance of commonality across same tasks, we have looked into inter-subject modelling approaches of EEG using different datasets obtained from 2 different EEG devices for machine learning purpose., the analysis and feature extraction of EEG signal for each trial has been done individually and the average responses were represented in the figures . Although it was a risk, we found data from central electrodes allowed reliable discrimination across subjects performing the tasks recorded using both devices.We are happyAttachmentBodda2022_Reviewer_comments_answers_secondversion.docxSubmitted filename: Click here for additional data file. 9 Jun 2022Exploring EEG spectral and temporal dynamics underlying a hand grasp movementPONE-D-21-37461R1Dear Dr. Diwakar,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Mukesh Dhamala, Ph. D.Academic EditorPLOS ONE 13 Jun 2022PONE-D-21-37461R1 Exploring EEG spectral and temporal dynamics underlying a hand grasp movement Dear Dr. Diwakar:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Mukesh Dhamala Academic EditorPLOS ONE


To evaluate the effectiveness of Tobacco Cessation on Prescription (TCP) compared to standard treatment in socioeconomically disadvantaged areas in Swedish primary healthcare (PHC).A pragmatic cluster randomized controlled trial, where randomization was conducted at the PHC center level using a computer-generated random allocation sequence.18 PHC centers in socioeconomically disadvantaged areas in Stockholm.250 adult daily tobacco users  with Swedish social security numbers and permanent resident permits, fluent in Swedish or Arabic, of which 140 responded to the follow-up at 6 months and 139 to the follow-up at 12 months. No blinding was applied.TCP  compared to standard treatment.The primary outcome was self-reported 7-day abstinence at 6 months and the secondary outcomes included self-reported 7-day abstinence at 12 months and 3-month continued abstinence at 6 and 12 months follow-up.PHC centers were randomized to the intervention group (n = 8) and control group (n = 10). At the PHC centers, 250 patients  were recruited. There was a statistically significant effect of TCP compared to standard treatment for the outcomes 7-day abstinence at 6 months  and 3-month continued abstinence at 6  and 12 months follow-up .TCP may be effective in achieving abstinence from tobacco use compared to standard treatment in the given setting but due to several limitations, resulting in high attrition rates and a low statistical power in the study, more research is needed to evaluate this.ISRCTN 11498135. Tobacco use is responsible for approximately 8% of the total disease burden in Sweden . AlthougTo tackle the harmful effects of tobacco use and related inequalities in health, clinical guidelines for tobacco cessation have been developed , 4. TheyPrimary healthcare (PHC) has an important role in providing tobacco cessation treatment since it has the main responsibility for health promotion in the Swedish healthcare system  and the In Sweden and in many other countries, prescription approaches are used in PHC to promote physical activity among patients . A similIn previous qualitative studies we have explored patients’ and PHC providers’ experiences of TCP , 14, 15,A two-armed pragmatic cluster randomized controlled trial was chosen as the study design. Randomization was conducted at the PHC center level, meaning that participating PHC centers were randomly allocated to intervention or control conditions. At the PHC centers, all patients in the intervention arm were offered TCP and all patients in the control arm were offered standard treatment. Patient characteristics were measured at baseline and outcomes at 6 and 12 months after the intervention. The trial has been approved by the Regional Ethical Review Board in Stockholm . It is presented according to the CONSORT Statement with relevant extensions  to ensurEligible PHC centers included PHC centers in socioeconomically disadvantaged areas in Stockholm. Eligible patients included daily tobacco users over 18 years of age with Swedish social security numbers and permanent residence permits, fluent in Swedish or Arabic with willingness to participate in the study. Daily tobacco use was defined as use of cigarettes, snus or other tobacco products every day for the past year. Ongoing treatment for tobacco cessation and cognitive impairment affecting voluntariness to participate were applied as exclusion criteria.PHC centers were sampled based on a socioeconomic index considering the income, educational level, ethnicity and health status of the population in a PHC center’s catchment area . ManagerPatients were recruited by one to three appointed staff members employed at each of the participating PHC centers. Most appointed staff members were nurses, specialized in diabetes or lung diseases. Other staff members could also refer patients to the responsible employees. Eligible patients were identified through a short screening questionnaire and invited to participate. Further information about the study was administered by responsible staff at the participating PHC centers and written informed consent was sought from all participants. Patients were offered a 100 SEK gift certificate as an incentive to participate.TCP included 1) tobacco cessation counseling for ≥10 minutes, 2) a written prescription for individualized tobacco cessation treatment and 3) follow-up on ≥1 occasion. The TCP prescription form included options for further counseling , pharmacotherapy , other measures for tobacco cessation , follow-up (by telephone or revisit) and support for self-management (questions for self-reflection and reference to mobile applications and websites). The prescription form was available on paper and electronically as a PDF-file. The form could be used as a basis for, or summary, of the counseling or as a written agreement. It was recommended that the patients in the study were given a copy of the filled out prescription form but the PHC providers were free to use the prescription form as they preferred. PHC providers responsible for the treatment of patients in the intervention group received 3.5 hours of training in tobacco cessation and TCP. A manual summarizing the training was also distributed.Standard treatment was defined as treatment for tobacco cessation according to current practice at the PHC centers. Responsible staff members were free to offer whatever cessation treatment they wanted as long as it was documented in a study-specific protocol for later comparison with TCP. The minimum treatment for the control group was 5 minutes of counseling. The control group did not have access to the TCP prescription form so the main difference between the treatment arms was how the counseling was administered (with or without a prescription form). PHC providers responsible for the treatment of patients in the control group received the same training as providers in the intervention group, excluding 30 minutes of information about TCP. A manual identical to the one developed for the intervention group but excluding all information about TCP, was also distributed to providers in the control group.As recommended by experts, the primary outcome was measured in self-reported point prevalence of 7-day abstinence  at 6 months follow-up . The secThe following changes to outcomes were made to what was stated in the published study protocol. Quit attempts and daily tobacco use were only reported for cigarettes since 97% of the study participants were daily smokers and snus and cigarettes were not considered appropriate to merge in the analysis of these outcomes. Daily cigarette use was only reported for non-quitters to evaluate the effect of reduced use separately from the effect of cessation. Since there was a difference in health-related quality of life between the treatment groups at baseline, this outcome was reported as change instead of absolute numbers at follow-up.Data on sociodemographic characteristics, tobacco use, cessation, health status and health-related quality of life was collected through paper-based questionnaires. The questions were based on the Swedish Public Health Survey 2014 , a queste.g. if new or additional PHC providers needed training to participate in the study. The latter occurred at approximately half of the PHC centers.The measurements were conducted at baseline and at 6 and 12 months after the intervention. In the follow-up questionnaires, questions about tobacco cessation support received were added. Responsible staff administered the baseline questionnaires and documented the cessation treatment in the electronic medical record as usual and in study-specific documentation protocols to enable a structured comparison of the interventions. They received written information and training in the documentation procedures before their involvement in the study. The researchers visited the PHC centers every three to four months to collect data and consent forms and to ensure that the study procedures were followed or adapted if needed, The 6- and 12 month follow-up questionnaires were sent to all participants via mail, regardless of their response to the 6-month follow-up. If the follow-up questionnaires were not returned within ten days, a reminder with a new questionnaire was sent via mail. If this was not returned within ten days, additional reminders were sent via mail, e-mail, and Short Message Service (SMS) with the option to answer the questionnaire in a telephone interview. All follow-up questionnaires and reminders were sent by the researchers.Data on the characteristics of participating PHC centers were collected by the researchers through informal interviews with staff members at baseline, including data on number of listed patients and employees at the PHC centers.The sample size was calculated by the researchers based on the primary outcome, assuming a 7% point prevalence of 7-day abstinence at 6 months follow-up in the control group, a significance level of 5% and 80% power to detect a relative risk of 2.0 when comparing the treatment groups, leading to an unadjusted sample size of 300 participants per arm. Assuming an intra-cluster coefficient (ICC) of 0.01  the sampDue to recruitment challenges, the PHC centers were randomized in two sets. In the first set, PHC centers were paired based on their socioeconomic index and allocation to treatment/control was done per pair using the uniform random number generator built into Excel. Allocation was done with a 1:1 ratio. In the second set, the randomization was not paired based on socioeconomic index, the ratio 4:6 between the intervention and control group was used due to a higher number of PHC centers dropping out in the control arm. The randomizations were conducted and communicated to the PHC centers by the researchers. Cluster randomization was employed at the PHC center level to avoid contamination of the trial conditions. This meant that all patients recruited from a particular PHC center were allocated to receive the same treatment.No blinding was applied. However, patients were only informed that they would be randomly allocated to receive different types of tobacco cessation support when they were recruited to participate. Thus, they were not informed about their treatment allocation or what the trial conditions entailed until after the study. This was done to avoid attrition and preconceptions regarding the effectiveness of treatment . PHC proDescriptive statistics of baseline characteristics at the individual and cluster level are presented separately for the treatment groups. Categorical variables are reported as proportions and continuous variables as mean values with standard deviations (SDs). All descriptive statistics were analyzed in IBM SPSS Statistics 25.Associations between treatment and outcomes at 6 and 12 months follow-up were analyzed in multiple regression models. Logistic regression models were used for binary outcomes, including point prevalence of 7-day abstinence. The results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). Associations between treatment and continuous/count outcomes, including cigarettes per day among non-quitters and change in health-related quality of life, were analyzed in multiple linear and Poisson regression models. All analyses were conducted according to the intention-to-treat principle , meaninge.g. no response to intention to quit), data was defined as missing. No sensitivity analysis with imputed values was needed since none of the variables in the models had more than 5% missing values, as specified in the study protocol [Before data was analyzed, logical assumptions were made to handle drop-out in responses to individual questionnaire items. For example, multiple answers to highest education achieved was interpreted as the highest education reported. Where logical assumptions could not be made  and were more often privately operated compared to included PHC centers. After randomization was completed, six PHC centers dropped out of the study due to staff turnover and organizational changes. These PHC centers had a higher socioeconomic index  and were more often publicly operated compared to those who continued in the study. Patients were recruited from February 2016 to August 2018. Patient recruitment took markedly longer than expected and had to be interrupted before the estimated sample size could be obtained. In total, 311 patients were screened for eligibility and 250 were included in the study. Patients who did not meet the eligibility criteria, who declined to participate or were missing signed consent forms and/or baseline data were excluded from the study. Excluded patients were of similar age as included patients but more often male. The majority of patients screened (68%) were in the intervention group. The participation rate was also higher in the intervention group compared to the control group (89% and 63% respectively). In addition, most of the participants (75%) were recruited from the PHC centers in the intervention group. Patients were followed-up from August 2016 to September 2019. In total, 140 patients (56%) responded to the follow-up at 6 months and 139 patients (56%) responded to the follow-up at 12 months. The response rate was higher in the intervention group compared to the control group (58% compared to 50% at 6 months and 59% compared to 45% at 12 months follow-up). Patients lost to follow-up were somewhat younger, more often male, employed and foreign born compared to respondents. Further details on the PHC center and participant flow are presented in PHC centers in the intervention group had fewer patients listed, more employees, lower socioeconomic index (more affluent) and were more often privately operated compared to PHC centers in the control group. For further details, see Patients generally had similar characteristics in the treatment groups but patients in the intervention group were more often female, Swedish born, had more previous quit attempts, previous experience of pharmacotherapy and lower prevalence of chronic disease compared to the control group. Patient demographics at baseline are presented in Almost all patients responded to the follow-up in Swedish (94%) and by paper-based questionnaire (94%) as opposed to in a telephone interview. In total, 38 of 108 patients in the intervention group and 4 of 31 patients in the control group reported total abstinence from tobacco in the 7 days preceding follow-up at 6 months after the intervention. When adjusted for significant covariates, the odds ratio for the primary outcome was 5.4. The adjusted odds ratio for 3-month continued abstinence was 6.4 at 6 months and 7.8 at 12 months follow-up. These associations were statistically significant. There were no statistically significant differences between the treatment groups in the other outcomes. Further details on all outcomes are presented in Sensitivity analysis was conducted on the primary outcome, assuming that all non-respondents had continued their tobacco use at 6 months follow-up. The sensitivity analysis produced lower odds ratios  with statistically significant associations.Exploratory analysis of the cessation treatment provided showed that the mean (SD) number of counseling sessions was 2.6 (1.8) in the intervention group and 2.4 (1.5) in the control group (p = 0.924). Furthermore, the mean (SD) number of counseling minutes was 105 (56) in the intervention group compared to 89 (51) in the control group (p = 0.889). These differences were not statistically significant. The content of the counseling was also similar in the treatment groups but there was a statistically significant difference in the proportion of patients in the intervention group that received counseling on pharmacotherapy compared to the control group . A higher proportion of patients in the intervention group also used prescription drugs for tobacco cessation (varenicline or bupropion)  and considered the support they had received as sufficient  at 6 months follow-up. However, these associations were not statistically significant, nor was the difference in use of nicotine replacement therapy at 6 months follow-up .To the best of the authors’ knowledge, this is the first study to evaluate the effectiveness of TCP in Swedish PHC. When adjusted for significant covariates at baseline, the odds of achieving 7-day abstinence was 5.4 times higher among patients receiving TCP compared to standard treatment at 6 months follow-up. The adjusted odds ratio for 3-month continued abstinence was 6.4 at 6 months follow-up and 7.8 at 12 months follow-up. These associations were statistically significant. However, the sensitivity analysis of the primary outcome yielded a lower odds ratio, indicating that 7-day abstinence at 6 months may have been overestimated. A previous systematic review in the field reported relative risks around 1.2 to 2.5 for effective tobacco cessation treatments , while tThe attrition rate was relatively high, particularly among more disadvantaged participants. It was higher in this study compared to other tobacco cessation studies in Sweden , 28 but Difficulties with recruiting and retaining PHC centers and patients could also be explained by high staff turnover, workload, and organizational changes in PHC as these were the main barriers for PHC centers to engage in the study. PHC centers with one staff member responsible for tobacco cessation were particularly vulnerable to this. Lone responsibility to work with tobacco cessation has previously been emphasized as a barrier to continuity, negatively affecting access to cessation treatment , 15. It Another limitation in the study was that the PHC providers were aware of the treatment allocation. This may have had a negative effect on PHC providers’ motivation to recruit patients and offer cessation support in the control arm, potentially contributing to lower recruitment and abstinence rates among patients in this arm. Still, individual study participants were not informed about the treatment options in the study arms until after the study, reducing the likelihood of biased psychological or physical responses to treatment among patients .An additional limitation of the study was the reduction of the statistical models to enable convergence in the iterative estimation procedures. Insignificant covariates were removed from the models which could be argued to be data driven and decreasing the inference from the models. This limitation in evidence has been acknowledge by careful presentation, interpretation, and discussion of the results.The effect sizes in this study were larger than expected and yielded statistically significant and clinically relevant treatment effects for several outcomes. A possible explanation for this may be the low statistical power in the study. Another explanation could be increased self-efficacy among PHC providers to work with tobacco cessation and increased involvement in the treatment among patients with TCP, as reported in a previous study exploring PHC providers’ perceived barriers and facilitators of implementing TCP . HoweverA strength of the study was the pragmatic approach applied, reflecting real world conditions, and providing high external validity of the findings . This maThe results suggest that TCP may be effective in achieving abstinence from tobacco use compared to standard treatment in socioeconomically disadvantaged areas in Swedish PHC but due to several limitations, resulting in high attrition rates and a low statistical power in the study, more research is needed to evaluate this.S1 File(DOCX)Click here for additional data file.S2 File(PDF)Click here for additional data file.S3 File(PDF)Click here for additional data file.S4 File(DOCX)Click here for additional data file.S5 File(DOCX)Click here for additional data file.


Guideline-based therapy of cardiac arrhythmias is important for many physicians from the beginning of their training. Practical training of the required skills to treat cardiac arrhythmias is useful for acquiring these skills but does not seem sufficient for skill retention. The aim of this study was to compare different retention methods for skills required to treat cardiac arrhythmias with respect to the performance of these skills in an assessment.Seventy-one final-year medical students participated in a newly designed workshop to train synchronized cardioversion (SC) and transcutaneous cardiac pacing (TCP) skills in 2020. All participants completed an objective structured clinical examination (OSCE 1) one week after the training. Afterwards, the participants were stratified and randomized into three groups. Nine weeks later, one group received a standard operating procedure (SOP) for the skills, one group participated in a second workshop (SW), and one group received no further intervention (control). Ten weeks after the first training, all groups participated in OSCE 2.p < 0.001) lower score in OSCE 2  than in OSCE 1. Students in the SOP-group achieved on average the same result in OSCE 2 as in OSCE 1 . Students who completed a second skills training (SW-group) scored not significantly higher in OSCE 2 compared to OSCE 1 . The OSCE 2 scores in groups SOP and SW were neither significantly different nor statistically equivalent.The average score of all students in OSCE 1 was 15.6 ± 0.8 points with no significant differences between the three groups. Students in the control group reached a significantly (Partial loss of SC and TCP skills acquired in a workshop can be prevented after 10 weeks by reading an SOP as well as by a second workshop one week before the second assessment. Refreshing practical skills with an SOP could provide an effective and inexpensive method for skills retention compared to repeating a training. Further studies need to show whether this effect also exists for other skills and how frequently an SOP should be re-read for appropriate long-term retention of complex skills. Some skills medical students acquire during undergraduate medical education are not frequently needed for patient management, even during postgraduate education . NeverthAcquired skills deteriorate quickly if routine training cannot be offered. Performance in neonatal resuscitation, for instance, reached only about 50% of the baseline 12 weeks after the initial training . DeterioIn hospitals, many procedures are regulated by so called standard operating procedures (SOPs), short, but detailed and concisely written instructions that describe a specific, relevant activity to guide its uniform performance . It has In this study, we established a workshop to teach medical interns two infrequently needed but important skills, SC and TCP. A few weeks later, some interns received reading material about these skills and other interns practiced these skills in a second workshop. Ten weeks after the first workshop, these skills were tested in an OSCE. Our research questions were: 1) Does performance of these skills deteriorate 10 weeks after a workshop? 2) Does reading a standard operating procedure (SOP) about these skills or participating in a second workshop one week prior to the OSCE lead to better retention of these skills?We carried out a prospective, stratified, controlled study with 71 interns  in anesthesiology to investigate the influence of different interventions on the retention of specific, infrequently needed skills. Five participants dropped out for various reasons . In total, 66 students finished the study, and their data could be included in the analysis. The study was conducted in 2020 – from January until October – and was performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Ludwig-Maximilians-University, Munich. It confirmed the innocuousness of the study with consented, anonymized, and voluntary participation (19–802). All participants provided informed written consent for participation in this study.We instructed the 66 participants in a 90-minute workshop to treat “patients” with unstable tachycardia with synchronized cardioversion (SC) or unstable bradycardia with transcutaneous cardiac pacing (TCP). The participants were divided into small groups of three to six students Fig. . They prTo reach an even distribution of male and female students as well as a similar mean OSCE 1 score in the three groups for the second part of our study, we used the following approach. According to their individual OSCE 1 scores, the interns were allocated in principle randomly to one of the three groups. The first half of the participants were assigned by simple randomization and the latter half were assigned to groups by hand such that the number of female participants and average OSCE 1 score were approximately the same across all three groups . The control group (18 interns) received no further intervention, the SOP-group (24 interns) was asked to read a standard operating procedure (SOP) for SC and TCP, and the SW-group (24 students) participated in a second workshop. The interns of the SOP-group were sent a standard operating procedure (SOP) with detailed written instructions, four pages including pictures and stepwise approach on how to perform SC and TCP, 9 weeks after the workshop via e-mail. They were asked to prepare with the SOP for OSCE 2 one week later. The participants of the SW-group were invited to participate in a second workshop 9 weeks after the first training. The second workshop was identical to the one before. Ten weeks after the initial workshop, all interns participated in OSCE 2, where they had to demonstrate an SC and to perform a TCP again. OSCE 2 was identical to OSCE 1 . The three raters of the second OSCE were “blinded” with respect to the participants’ group. After OSCE 2, individual feedback on their performance was given to all participants.We considered both hypotheses related to the change between week 1 and week 10 and hypotheses related to the comparison of the three groups. The primary hypotheses that we aimed to confirm were: 1) The score will change between week 1 and week 10 in the control group. 2) The score will not substantially change between week 1 and week 10 in the SOP-group and the SW-group, respectively, formalized as equivalence testing, where a change of less than 1 point was considered ignorable. The secondary hypotheses to be confirmed referring to the comparisons between the three groups were: 1) The change between week 1 and week 10 will not be substantially different for the SOP- and SW-groups . 2) The change between week 1 and week 10 will be different for the SOP- and SW-group, respectively, and the control group.n = 22 is required for both SOP and SW groups, while n = 17 are needed for the control group. The group sizes were thus chosen as n = 18 (control group) and n = 24 (SOP and SW groups) to account for 5% dropout.For sample size calculation it was assumed that all within-group average scores are normally distributed . Furtherd was calculated for effect size when t-tests were performed.All analyses were performed with R (version 4.0.3). All statistical tests were performed based on 95%-confidence intervals (CI) for the mean changes between scores at week 1 and week 10 or for the between-group differences of mean changes. CIs were derived assuming normality of the endpoints. The null hypothesis was rejected if 0 was not in the CI . Additionally, the average and standard deviation were computed in the different groups. Cohen’s n = 42). The ratio of women to men within the groups was: 11 to 7 (control group), 15 to 9 (SOP-group), and 16 to 8 (SW-group). The distribution of the achieved point scores of the three groups in the two OSCEs (week 1 and week 10) is shown in the form of histograms  score in OSCE 2  than in OSCE 1 , resulting in an average difference of -2.0 . This effect was very large (Cohen’s d = 1.3). The SOP-group reached the same average score in OSCE 2  and OSCE 1 , i.e. the average difference was 0 . For the SW-group, the average difference between the score in OSCE 2  and OSCE 1  was + 0.4 , thus confirming that the score did not substantially decrease between week 1 and week 10.Of the 66 participants, almost two thirds were female n = 4. The ratp < 0.001) different in the SOP-group  and the SW-group , thus confirming the utility of both, the SOP and the second workshop for skills retention. Both differences had a very large effect . The difference between the SW-group and the SOP-group  in OSCE 2 (week 10) was not significant, but the CI was not included in . Therefore, a superiority of the second workshop over reading an SOP could neither be established nor excluded based on the available data.Compared to the control group, the change of the score was significantly . It has been described that the management of tachyarrhythmias can be learned quickly with skills training . The treTo maintain acquired skills, several methods have been described. Most of them are time and personnel-consuming. The best-studied method for skills retention is to repeat the skills training , 27–29. Since in our study reading an SOP on SP and TCP led to nearly the same maintenance of skills in interns 10 weeks after the initial workshop as repeating the workshop one week before the second assessment, reading an SOP provides an effective and inexpensive way for the retention of these skills. The costs for one workshop to train six participants amount to approximately 400 euros, while SOPs are usually part of a hospital’s quality management system , 34. SOPOur study has several limitations. Student participation was voluntary and could have led to inclusion of very motivated and skilled participants. We were not able to carry out a pre-test of our assessment because SC and TCP are not taught in the regular undergraduate curriculum at our medical school. This could be circumvented in future studies by carrying out a pre-test with physicians who perform these skills regularly. For the second OSCE (week 10), we had to use three different examiners for organizational reasons like in regular OSCEs. This could have led to some variability in the results despite standardized instructions how to use the checklist, but did not, which underscores the quality of the checklist. As it is usual for OSCEs to have different examiners, high quality checklists are the most important feature for OSCE scoring and rater training is a requirement for quality control . AnotherSynchronized cardioversion (SC) and transcutaneous pacing (TCP) skills were sufficiently acquired by final-year medical students in a workshop. A loss of these skills could be completely prevented by either a repeated training or by reading an SOP. In this context, the SOP is a cost-effective and resource-friendly method for skills retention. Whether reading an SOP is effective for retention of other complex skills needs to be investigated in future studies.