The data used for representative results were collected from the work supported by the National Institute of Child Health and Human Development (NICHD), the National Institutes of Health (NIH) under Grant R21HD054697, and the National Institute on Disability and Rehabilitation Research (NIDRR) in the Department of Education under Grant H133G090005 and Award Number H133P090008. The rest of the work was funded in part by the National Science Foundation (NSF) under award #1910526. Findings and opinions within this work do not necessarily reflect the positions of NICHD, NIH, NIDRR or NSF.

The &#34;CBLE Performance Estimation&#34; GUI was applied to two datasets: &#34;BrainInvaders&#34; dataset and Michigan dataset. For the &#34;BrainInvaders&#34; dataset, the data collection was approved by the Ethical Committee of the University of Grenoble Alpes20. Michigan data were collected under the University of Michigan Institutional Review Board approval19. Data were analyzed under Kansas State University exempt protocol 7516. If collecting new data, follow the user&#39;s IRB-approved process for collecting informed consent. Here, the proposed protocol is evaluated using offline analysis of previously-recorded, de-identified data and therefore did not require additional informed consent.
The graphical user interface (GUI) included in this article is proficient in managing two distinct dataset formats. The first format is associated with the BCI2000 software, while the second format is referred to as the &#34;BrainInvaders&#34; dataset. In order to utilize the &#34;Brain Invaders&#34; format, data must be pre-processed as described in step 1 of the protocol section. However, when dealing with the &#34;BCI2000&#34; dataset format, step 1 can be omitted.
1. Data preparation
<ol>
	<li>BrainInvaders only: Generate the input data file in &#34;.mat&#34; file format that can be used with the &#34;CBLE performance Estimation&#34; graphical user interface (GUI). For a sample script, refer to Supplementary Coding File 2. 
	NOTE: Each data file consists of a two-dimensional matrix comprised of rows that represent observations recorded at distinct time samples. The matrix columns numbered 2 to 17 are recordings derived from 16 EEG electrodes. The first column of the matrix denotes the timestamp of each observation, while column 18 encompasses information related to experimental events. In column 19, there are mostly zeros, but when a non-Target (or Target) flash starts, the numbers change to one (or two) at that specific time. A detailed description may be found in the reference20.</li>
</ol>
2. Downloading and installing the GUI package
<ol>
	<li>Download and install the &#34;CBLE Performance Estimation&#34; GUI.</li>
</ol>
3. Storing the dataset in a subfolder of the GUI location
<ol>
	<li>Ensure that the dataset folder remains within the same directory as the GUI.</li>
	<li>For instance, create a new folder and place the &#34;CBLE Performance Estimation&#34; GUI inside it. Keep all the datasets in a subfolder within &#34;CBLE GUI&#34; named &#34;Dataset.&#34;</li>
</ol>
4. Opening the installed GUI
<ol>
	<li>Open MATLAB (see Table of Materials), change the current directory to the folder where the GUI is placed, click on APPS tab, and select MY APPS.</li>
	<li>Under the &#34;MY APPS&#34; tab, select CBLE Performance Estimation.</li>
</ol>
5. Choosing the dataset format
<ol>
	<li>Select a dataset format from the dropdown Select dataset format.</li>
</ol>
6. Loading the EEG data file
<ol>
	<li>Click on the Select input folder button to choose the directory where the dataset is located.</li>
	<li>Observe the count of data files present in that selected folder. 
	NOTE: In the &#34;Brain Invaders&#34; format, each participant is represented by a single data file. Therefore, the total number of data files indicates the number of participants in the study. However, this is not the case for the &#34;BCI2000&#34; format, as each participant may have multiple train and test files.</li>
</ol>
7. Setting the parameters
<ol>
	<li>Type the number of participants that the user intends to use for the estimation process in the &#34;No. of participants&#34; text box.</li>
	<li>BrainInvaders only: Specify the sampling rate of the dataset. 
	NOTE: BCI2000 files include the sample rate.</li>
	<li>Choose a decimation value to downsample the dataset to approximately 20 Hz in order to improve classification performance21. For example, if the sampling frequency is 256 Hz, then select a decimation value of 13.</li>
	<li>Specify the time window for the classification in milliseconds. 
	NOTE: The recommended initial window size is specified, allowing the starting point to vary from 0 to 100 ms and the ending point from 700 to 800 ms. However, it is important to avoid making the window size excessively large to prevent overlapping with another P300 event.</li>
	<li>Define the shift window for CBLE in milliseconds. 
	NOTE: The &#39;shift window&#39; refers to the expanded time range that the CBLE&#8239;method searches to find the P3 response.&#8239; The classifier will be applied to the epoch starting at the first element of the shift window.&#8239; The classifier is then sequentially applied to epochs starting one sample later, until the epoch extends outside the shift window.&#8239; Thus, the shift window must be larger than the original window; empirically, values less than 100 ms from each side perform well.&#8239; In any case, the margin should be held to less than half the ISI.</li>
	<li>BC2000 only: Enter the length of the subject ID indicated in the dataset files within the &#34;ID length&#34; field. 
	NOTE: The GUI expects the first sub_len characters of the filenames to encode the subject ID.</li>
	<li>BCI2000 only: In the &#34;Channel ID&#34; field, indicate either the total number of channels or specify the specific channel numbers to be used for the analysis.</li>
	<li>Click on the Set parameters button to set all the parameters required for the analysis.</li>
</ol>
8. BrainInvaders only: Splitting the dataset into training and test set
<ol>
	<li>Select a number of targets that represents the size of the training set. The remaining portion of the dataset will be considered as the test dataset. 
	NOTE: To ensure proper training of the model, it is essential to have a sufficiently large training sample. The recommended minimum training sample size is 20, though this may vary depending on the overall dataset size. If regression errors occur during training sessions, it is advisable to increase the training sample size.</li>
	<li>Press the &#34;Split the dataset&#34; button to divide the dataset into the training and test sets. 
	&#8203;NOTE: Each participant will have an equal amount of training data. However, the number of test data may not be equal for all participants due to the possibility of multiple attempts during the task. Consequently, the total number of targets or flashes presented may vary from person to person.</li>
</ol>
9. Training a model with the training dataset
NOTE: Step 9.1 is applicable for &#34;Brain Invaders&#34; format, and step 9.2 is applicable for &#34;BCI2000&#34; format.
<ol>
	<li>BrainInvaders only: Click on the Train a model button to apply linear regression on the training dataset using Equation 2 for training a classifier model.</li>
	<li>BCI2000 only: Indicate training and testing filenames along with their data format (.dat) to distinguish the training and testing files from all files. Then, click on the Train a model button to apply linear regression on the training dataset.</li>
</ol>
10. Predicting the accuracy of the test set
<ol>
	<li>Click on Predict accuracy to apply the trained classifier model to the test feature set and predict&#160;accuracy&#160;using Equation 1.</li>
</ol>
11. Getting X-target accuracies
<ol>
	<li>Select a maximum target number, X, to consider in the test set.</li>
	<li>BCI2000 only: Select a test file number if the user has multiple test files.</li>
	<li>Press Find X target accuracy.</li>
</ol>
12. Calculating vCBLE
<ol>
	<li>Click on the Find vCBLE button to get the vCBLE for all targets.</li>
</ol>
13. Calculating the Root mean square error (RMSE) of BCI accuracy and vCBLE
<ol>
	<li>Click on the Calculate RMSE button to calculate the RMSE between both predictions based on vCBLE with BCI accuracy, and X-target accuracy with BCI accuracy.</li>
</ol>
14. Visualizing the analysis results
<ol>
	<li>Click on the Accuracy vs vCBLE button to observe the relation between total accuracy and total vCBLE for all participants.</li>
	<li>Press on the RMSE of BCI &#38; vCBLE button to show the RMSE curve of BCI accuracy and vCBLE.</li>
</ol>
15. Predicting the performance of an individual participant
<ol>
	<li>To predict the accuracy of an individual participant, place the subject ID in Sub ID. 
	NOTE: Here, the dataset of all participants, excluding the test participant, will be used to train a linear regression model. The vCBLE scores of all other participants and their corresponding test accuracies will be utilized as predictors and labels, respectively, for the classifier.</li>
	<li>Select a target number, n. The prediction will be made based on the accuracy of n-testing characters.</li>
	<li>Click on the Predict button to get the predicted accuracy of the test participant.</li>
</ol>

<ol>
	<li>Rezeika, A., Benda, M., Stawicki, P., Gembler, F., Saboor, A., Volosyak, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain-Computer+Interface+spellers:+A+review.">Brain-Computer Interface spellers: A review.</a> Brain Science. 8 (4), 57 (2018).</li><li>Gannouni, S., Aledaily, A., Belwafi, K., Aboalsamh, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Emotion+detection+using+electroencephalography+signals+and+a+zero-time+windowing-based+epoch+estimation+and+relevant+electrode+identification.">Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification.</a> Scientific Reports. 11 (1), 7071 (2021).</li><li>Daly, J. J., Wolpaw, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain-computer+interfaces+in+neurological+rehabilitation.">Brain-computer interfaces in neurological rehabilitation.</a> Lancet Neurology. 7 (11), 1032-1043 (2008).</li><li>Birbaumer, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Breaking+the+silence:+brain-computer+interfaces+(BCI)+for+communication+and+motor+control.">Breaking the silence: brain-computer interfaces (BCI) for communication and motor control.</a> Psychophysiology. 43 (6), 517-532 (2006).</li><li>Riccio, A., Simione, L., Schettini, F., Pizzimenti, A., Inghilleri, M., Belardinelli, M. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Attention+and+P300-based+BCI+performance+in+people+with+amyotrophic+lateral+sclerosis.">Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis.</a> Frontiers in Human Neuroscience. 7, 732 (2013).</li><li>Finke, A., Lenhardt, A., Ritter, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+MindGame:+a+P300-based+brain-computer+interface+game.">The MindGame: a P300-based brain-computer interface game.</a> Neural Network. 22 (9), 1329-1333 (2009).</li><li>Farwell, L. A., Donchin, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Talking+off+the+top+of+your+head:+toward+a+mental+prosthesis+utilizing+event-related+brain+potentials.+Electroencephalogr.">Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr.</a> Clinical Neurophysiology. 70 (6), 510-523 (1988).</li><li>Li, Q., Lu, Z., Gao, N., Yang, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimizing+the+performance+of+the+visual+P300-speller+through+active+mental+tasks+based+on+color+distinction+and+modulation+of+task+difficulty.">Optimizing the performance of the visual P300-speller through active mental tasks based on color distinction and modulation of task difficulty.</a> Frontiers in Human Neuroscience. 13, 130 (2019).</li><li>McFarland, D. J., Sarnacki, W. A., Townsend, G., Vaughan, T., Wolpaw, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+P300-based+brain-computer+interface+(BCI):+effects+of+stimulus+rate.">The P300-based brain-computer interface (BCI): effects of stimulus rate.</a> Clinical Neurophysiology. 122 (4), 731-737 (2011).</li><li>Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+classification+techniques+for+the+P300+Speller.">A comparison of classification techniques for the P300 Speller.</a> Journal of Neural Engineering. 3 (4), 299-305 (2006).</li><li>Sellers, E. W., Donchin, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+P300-based+brain-computer+interface:+initial+tests+by+ALS+patients.">A P300-based brain-computer interface: initial tests by ALS patients.</a> Clinical Neurophysiology. 117 (3), 538-548 (2006).</li><li>Donchin, E., Spencer, K. M., Wijesinghe, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+mental+prosthesis:+assessing+the+speed+of+a+P300-based+brain-computer+interface.">The mental prosthesis: assessing the speed of a P300-based brain-computer interface.</a> IEEE Transactions on Rehabilitation Engineering. 8 (2), 174-179 (2000).</li><li>H&#246;hne, J., Schreuder, M., Blankertz, B., Tangermann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+9-class+auditory+ERP+paradigm+driving+a+predictive+text+entry+system.">A novel 9-class auditory ERP paradigm driving a predictive text entry system.</a> Frontiers in Neuroscience. 5, 99 (2011).</li><li>Acqualagna, L., Treder, M. S., Blankertz, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chroma+Speller:+Isotropic+visual+stimuli+for+truly+gaze-independent+spelling.">Chroma Speller: Isotropic visual stimuli for truly gaze-independent spelling.</a> , (2013).</li><li>Townsend, G., LaPallo, B. K., Boulay, C. B., Krusienski, D. J., Frye, G. E., Hauser, C. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+P300-based+brain-computer+interface+stimulus+presentation+paradigm:+moving+beyond+rows+and+columns.">A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.</a> Clinical Neurophysiology. 121 (7), 1109-1120 (2010).</li><li>Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+many+people+are+able+to+control+a+P300-based+brain-computer+interface+(BCI).">How many people are able to control a P300-based brain-computer interface (BCI).</a> Neuroscience Letters. 462 (1), 94-98 (2009).</li><li>Mowla, M. R., Gonzalez-Morales, J. D., Rico-Martinez, J., Ulichnie, D. A., Thompson, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+classification+techniques+to+predict+Brain-computer+interfaces+accuracy+using+classifier-based+latency+estimation.">A comparison of classification techniques to predict Brain-computer interfaces accuracy using classifier-based latency estimation.</a> Brain Science. 10 (10), 734 (2020).</li><li>Thompson, D. E., Warschausky, S., Huggins, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Classifier-based+latency+estimation:+a+novel+way+to+estimate+and+predict+BCI+accuracy.">Classifier-based latency estimation: a novel way to estimate and predict BCI accuracy.</a> Journal of Neural Engineering. 10 (1), 016006 (2012).</li><li>Thompson, D. E., Gruis, K. L., Huggins, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+plug-and-play+brain-computer+interface+to+operate+commercial+assistive+technology.">A plug-and-play brain-computer interface to operate commercial assistive technology.</a> Disability and Rehabilitation: Assistive Technology. 9 (2), 144-150 (2014).</li><li>Korczowski, L., Ostaschenko, E., Andreev, A., Cattan, G., Coelho Rodrigues, P. L., Gautheret, V., Congedo, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain+Invaders+calibration-less+P300-based+BCI+using+dry+EEG+electrodes+Dataset+(bi2014a)+[Data+set].">Brain Invaders calibration-less P300-based BCI using dry EEG electrodes Dataset (bi2014a) [Data set].</a> Zenodo. , (2019).</li><li>Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M., Wolpaw, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+classification+techniques+for+the+P300+Speller.">A comparison of classification techniques for the P300 Speller.</a> Journal of Neural Engineering. 3 (4), 299-305 (2006).</li></ol>

All authors declare they do not have any conflicts of interest.

This article outlined a method for estimating BCI accuracy using a small P300 dataset. Here, the current protocol was developed based on the &#34;bi2014a&#34; dataset, although the efficacy of the protocol was confirmed on two different datasets. To successfully implement this technique, it is crucial to establish certain variables, such as the epoch window for the original data, the window for time shifting, the down-sampling ratio, and the size of both the training and testing datasets. These variables are determined b...

Brain-computer interfaces (BCIs) are a noninvasive technology that allows individuals to communicate through machines directly without regard for physical limitations imposed by the body. BCI can be utilized as an assistive device operated directly by the brain. BCI uses the brain activity of a user to determine if the user intends to choose a certain key (letter, number, or symbol) displayed on the screen1. In a typical computer system, a user physically presses the intended key on a keyboard. However, in a BCI system with a visual display, the user needs to focus on the desired key. Then, BCI will select the intended key by analyzing the measured brain signals1. The activity of the brain can be measured using various techniques. Though there are competing BCIs technologies, electroencephalogram (EEG) is considered a leading technique due to its noninvasive nature, high temporal resolution, reliability, and relatively low cost2.
Applications of BCI include communication, device control, and also entertainment3,4,5,6. One of the most active BCI application areas is the P300 speller, which was introduced by Farwell and Donchin7. The P300 is an event-related potential (ERP) produced in response to the recognition of a rare but relevant stimulus8. When a person recognizes their target stimulus, they automatically produce a P300. The P300 is an effective signal for a BCI because it conveys the participant&#39;s recognition of the target event without requiring an outward response9.
The P300 BCI has attracted researchers from computer science, electrical engineering, psychology, human factors, and various other disciplines. Advances have been made in signal processing, classification algorithms, user interfaces, stimulation schemes, and many other areas10,11,12,13,14,15. However, regardless of the research area, the common thread in all of this research is the necessity of measuring the BCI system performance. This task typically requires the generation of a test data set. This necessity is not limited to research; eventual clinical application as an assistive technology will likely require individual validation sets for each end user to ensure the system can generate reliable communication.
Despite the considerable research applied toward the P300 BCI, the systems are still quite slow. While the majority of people are able to use a P300 BCI16, most P300 Spellers produce text on the order of 1-5 characters per minute. Unfortunately, this slow speed means that generating test data sets requires substantial time and effort for participants, experimenters, and eventual end users. Measuring BCI system accuracy is a binomial parameter estimation problem, and many characters of data are necessary for a good estimate.
To estimate the presence or absence of the P300 ERP, most classifiers use a binary classification model, which involves assigning a binary label (e.g., &#34;presence&#34; or &#34;absence&#34;) to each trial or epoch of EEG data. The general equation used by most classifiers can be expressed as:
<img alt="Equation 1" src="/files/ftp_upload/64959/64959eq01.jpg" style="margin: auto;" />
where <img alt="Equation 2" src="/files/ftp_upload/64959/64959eq02.jpg" style="margin: auto;" /> is called the classifier&#39;s score, which represents the probability of the P300 response being present, x is the feature vector extracted from the EEG signal, and b is a bias term17. The function f is a decision function that maps the input data to the output label, and is learned from a set of labeled training data using a supervised learning algorithm17. During training, the classifier is trained on a labeled dataset of EEG signals, where each signal is labeled as either having a P300 response or not. The weight vector and bias term are optimized to minimize the error between the predicted output of the classifier and the true label of the EEG signal. Once the classifier is trained, it can be used to predict the presence of the P300 response in new EEG signals.
Different classifiers can use different decision functions, such as linear discriminant analysis (LDA), stepwise linear discriminant analysis (SWLDA), least squares (LS), logistic regression, support vector machines (SVM), or neural networks (NNs). The least squares classifier is a linear classifier that minimizes the sum of squared errors between the predicted class labels and the true class labels. This classifier predicts the class label of a new test sample using the following equation:
<img alt="Equation 3" src="/files/ftp_upload/64959/64959eq03.jpg" style="margin: auto;" />&#160; &#160; (1)
where the sign function returns +1 if the product is positive and -1 if it is negative, and the weight vector <img alt="Equation 4" src="/files/ftp_upload/64959/64959eq04.jpg" style="margin: auto;" /> is obtained from the feature set of the training data, (x) and class labels (y) using the below equation:
<img alt="Equation 5" src="/files/ftp_upload/64959/64959eq05.jpg" style="margin: auto;" />&#160; &#160; (2)
In earlier research, we argued that Classifier-Based Latency Estimation (CBLE) can be used to estimate BCI accuracy17,18,19. CBLE is a strategy for evaluating latency variation by exploiting the classifier&#39;s temporal sensitivity18. While the conventional approach to P300 classification involves using a single time window that is synchronized with each stimulus presentation, the CBLE method involves creating multiple time-shifted copies of the post-stimulus epochs. Then it detects the time shift that results in the maximum score in order to estimate the latency of the P300 response17,18. Here, this work presents a protocol that estimates BCI performance from a small dataset using CBLE. As a representative analysis, the number of characters is varied to make predictions of the overall performance of an individual. For both example datasets, the root mean square error (RMSE) for vCBLE and actual BCI accuracy were computed. The results indicate that the RMSE from vCBLE predictions, using its fitted data, was consistently lower than the accuracy derived from 1 to 7 tested characters.
We developed a Graphical User Interface (GUI) called &#34;CBLE Performance Estimation&#34; for the implementation of the proposed methodology. The example code is also provided (Supplementary Coding File 1) that operates on the MATLAB platform. The example code performs all of the steps applied in the GUI, but the steps are provided to assist the reader with adapting to a new dataset. This code employs a publicly available dataset &#34;Brain Invaders calibration-less P300-based BCI using dry EEG electrodes Dataset (bi2014a)&#34; to evaluate the proposed method20. Participants played up to three game sessions of Brain Invaders, each session having 9 levels of the game. The data collection continued until all levels were completed or the participant lost all control over the BCI system. The Brain Invaders interface included 36 symbols that flashed in 12 groups of six aliens. According to the Brain Invaders P300 paradigm, a repetition was created by 12 flashes, one for each group. Out of these 12 flashes, two flashes contained the Target symbol (known as Target flashes), while the remaining 10 flashes did not contain the Target symbol (known as non-Target flashes). More information on this paradigm can be found in the original reference20.
The CBLE approach was also implemented on a Michigan dataset, which contained data from 40 participants18,19. Here, the data of eight participants had to be discarded because their tasks were incomplete. The whole study required three visits from each participant. On the first day, each participant typed a 19-character training sentence, followed by three 23-character testing sentences on Days 1, 2, and 3. In this example, the keyboard included 36 characters which were grouped into six rows and six columns. Each row or column was flashed for 31.25 milliseconds with an interval of 125 milliseconds between flashes. Between characters, a 3.5 s pause was provided.
Figure 1 shows the block diagram of the proposed method. The detailed procedure is described in the protocol section.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>MATLAB 2021</td>
			<td>Matlab</td>
			<td>N/A</td>
			<td>Any recent MATLAB version can be used.</td>
		</tr>
	</tbody>
</table>

p300-based brain-computer interface speller performance estimation with classifier-based latency estimation

Performance estimation is a necessary step in the development and validation of Brain-Computer Interface (BCI) systems. Unfortunately, even modern BCI systems are slow, making collecting sufficient data for validation a time-consuming task for end users and experimenters alike. Yet without sufficient data, the random variation in performance can lead to false inferences about how well a BCI is working for a particular user. For example, P300 spellers commonly operate around 1-5 characters per minute. To estimate accuracy with a 5% resolution requires 20 characters (4-20 min). Despite this time investment, the confidence bounds for accuracy from 20 characters can be as much as &plusmn;23% depending on observed accuracy. A previously published method, Classifier-Based Latency Estimation (CBLE), was shown to be highly correlated with BCI accuracy. This work presents a protocol for using CBLE to predict a user's P300 speller accuracy from relatively few characters (~3-8) of typing data. The resulting confidence bounds are tighter than those produced by traditional methods. The method can thus be used to estimate BCI performance more quickly and/or more accurately.

The proposed protocol has been tested on two different datasets: &#34;BrainInvaders&#34; and the Michigan dataset. These datasets are already introduced briefly in the Introduction section. The parameters used for this two datasets are mentioned in Table 1. Figures 2-4 depict the findings obtained using the &#34;BrainInvaders&#34; dataset, whereas Figures 5-7 demonstrate the res...

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P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

This article presents a method for estimating same-day P300 speller Brain-Computer Interface (BCI) accuracy using a small testing dataset.

Performance estimation is a necessary step in the development and validation of Brain-Computer Interface (BCI) systems. Unfortunately, even modern BCI ...

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447 Views.  Kansas State University. This article presents a method for estimating same-day P300 speller Brain-Computer Interface (BCI) accuracy using a small testing dataset. 

Video: P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow to brain thereby preventing delivery of oxygen and nutrients. About half of the stroke survivors are left with some degree of disability. Innovative methodologies for restorative neurorehabilitation are urgently required to reduce long-term disability. The ability of the nervous system to reorganize its structure, function and connections as a response to intrinsic or extrinsic stimuli is called neuroplasticity. Neuroplasticity is involved in post-stroke functional disturbances, but also in rehabilitation. Beneficial neuroplastic changes may be facilitated with non-invasive electrotherapy, such as neuromuscular electrical stimulation (NMES) and sensory electrical stimulation (SES). NMES involves coordinated electrical stimulation of motor nerves and muscles to activate them with continuous short pulses of electrical current while SES involves stimulation of sensory nerves with electrical current resulting in sensations that vary from barely perceivable to highly unpleasant. Here, active cortical participation in rehabilitation procedures may be facilitated by driving the non-invasive electrotherapy with biosignals (electromyogram (EMG), electroencephalogram (EEG), electrooculogram (EOG)) that represent simultaneous active perception and volitional effort. To achieve this in a resource-poor setting, e.g., in low- and middle-income countries, we present a low-cost human-machine-interface (HMI) by leveraging recent advances in off-the-shelf video game sensor technology. In this paper, we discuss the open-source software interface that integrates low-cost off-the-shelf sensors for visual-auditory biofeedback with non-invasive electrotherapy to assist postural control during balance rehabilitation. We demonstrate the proof-of-concept on healthy volunteers.

A novel low-cost human-machine interface for interactive post-stroke balance rehabilitation system is presented in this article. The system integrates off-the-shelf low-cost sensors towards volitionally driven electrotherapy paradigm. The proof-of-concept software interface is demonstrated on healthy volunteers.

A stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow to brain thereby preventing ...

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat model of ischemic stroke, differences in quantitative T1 and T2 MRI relaxation times (qT1 and qT2) between the ischemic lesion (delineated by low diffusion) and the contralateral non-ischemic hemisphere increase with time from stroke onset. The time dependency of MRI relaxation time differences is heuristically described by a linear function and thus provides a simple estimate of stroke onset time. Additionally, the volumes of abnormal qT1 and qT2 within the ischemic lesion increase linearly with time providing a complementary method for stroke timing. A (semi)automated computer routine based on the quantified diffusion coefficient is presented to delineate acute ischemic stroke tissue in rat ischemia. This routine also determines hemispheric differences in qT1 and qT2 relaxation times and the location and volume of abnormal qT1 and qT2 voxels within the lesion. Uncertainties associated with onset time estimates of qT1 and qT2 MRI data vary from &plusmn; 25 min to &plusmn; 47 min for the first 5 hours of stroke. The most accurate onset time estimates can be obtained by quantifying the volume of overlapping abnormal qT1 and qT2 lesion volumes, termed 'Voverlap' (&plusmn; 25 min) or by quantifying hemispheric differences in qT2 relaxation times only (&plusmn; 28 min). Overall, qT2 derived parameters outperform those from qT1. The current MRI protocol is tested in the hyperacute phase of a permanent focal ischemia model, which may not be applicable to transient focal brain ischemia.

A protocol for stroke onset time estimation in a rat model of stroke exploiting quantitative magnetic resonance imaging (qMRI) parameters is described. The procedure exploits diffusion MRI for delineation of the acute stroke lesion and quantitative T1 and T2 (qT1 and qT2) relaxation times for timing of stroke.

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat ...

Stereological Estimation of Dopaminergic Neuron Number in the Mouse Substantia Nigra Using the Optical Fractionator and Standard Microscopy Equipment

In pre-clinical Parkinson&#39;s disease research, analysis of the nigrostriatal tract, including quantification of dopaminergic neuron loss within the substantia nigra, is essential. To estimate the total dopaminergic neuron number, unbiased stereology using the optical fractionator method is currently considered the gold standard. Because the theory behind the optical fractionator method is complex and because stereology is difficult to achieve without specialized equipment, several commercially available complete stereology systems that include the necessary software do exist, purely for cell counting reasons. Since purchasing a specialized stereology setup is not always feasible, for many reasons, this report describes a method for the stereological estimation of dopaminergic neuronal cell counts using standard microscopy equipment, including a light microscope, a motorized object table (x, y, z plane) with imaging software, and a computer for analysis. A step-by-step explanation is given on how to perform stereological quantification using the optical fractionator method, and pre-programmed files for the calculation of estimated cell counts are provided. To assess the accuracy of this method, a comparison to data obtained from a commercially available stereology apparatus was performed. Comparable cell numbers were found using this protocol and the stereology device, thus demonstrating the precision of this protocol for unbiased stereology.

This work presents a step-by-step protocol for the unbiased stereological estimation of dopaminergic neuronal cell numbers in the mouse substantia nigra using standard microscopy equipment (i.e., a light microscope, a motorized object table (x, y, z plane), and public domain software for digital image analysis.

In pre-clinical Parkinson&#39;s disease research, analysis of the nigrostriatal tract, including quantification of dopaminergic neuron loss within the ...

CMAP Scan MUNE (MScan) - A Novel Motor Unit Number Estimation (MUNE) Method

Like other methods for motor unit number estimation (MUNE), compound muscle action potential (CMAP) scan MUNE (MScan) is a non-invasive electrophysiologic method to estimate the number of functioning motor units in a muscle. MUNE is an important tool for the assessment of neuropathies and neuronopathies. Unlike most MUNE methods in use, MScan assesses all the motor units in a muscle, by fitting a model to a detailed stimulus-response curve, or CMAP scan. It thereby avoids the bias inherent in all MUNE methods based on extrapolating from a small sample of units. Like &#39;Bayesian MUNE,&#39;&#160;MScan analysis works by fitting a model, made up of motor units with different amplitudes, thresholds, and threshold variabilities, but the fitting method is quite different, and completed within five minutes, rather than several hours. The MScan off-line analysis works in two stages: first, a preliminary model is generated based on the slope and variance of the points in the scan, and second, this model is then refined by adjusting all the parameters to improve the fit between the original scan and scans generated by the model.
This new method has been tested for reproducibility and recording time on 22 amyotrophic lateral sclerosis (ALS) patients and 20 healthy controls, with each test repeated twice by two blinded physicians. MScan showed excellent intra- and inter-rater reproducibility with ICC values of &#62;0.98 and a coefficient of variation averaging 12.3 &#177; 1.6%. There was no difference in the intra-rater reproducibility between the two observers. Average recording time was 6.27 &#177; 0.27 min.
This protocol describes how to record a CMAP scan and how to use the MScan software to derive an estimate of the number and sizes of the functioning motor units. MScan is a fast, convenient, and reproducible method, which may be helpful in diagnoses and monitoring disease progression in neuromuscular disorders.

CMAP Scan MUNE MScan - A Novel Motor Unit Number Estimation MUNE Method

This protocol describes a new method to estimate the number of functioning motor units in a muscle, by fitting a model to a detailed stimulus-response curve of the compound muscle action potential. It is quick and easy to perform and analyze and has excellent reproducibility.

Like other methods for motor unit number estimation (MUNE), compound muscle action potential (CMAP) scan MUNE (MScan) is a non-invasive electrophysiologic ...

A Human Blood-Brain Interface Model to Study Barrier Crossings by Pathogens or Medicines and Their Interactions with the Brain

The early screening of nervous system medicines on a pertinent and reliable in cellulo BBB model for their penetration and their interaction with the barrier and the brain parenchyma is still an unmet need. To fill this gap, we designed a 2D in cellulo model, the BBB-Minibrain, by combining a polyester porous membrane culture insert human BBB model with a Minibrain formed by a tri-culture of human brain cells (neurons, astrocytes and microglial cells). The BBB-Minibrain allowed us to test the transport of a neuroprotective drug candidate (e.g., Neurovita), through the BBB, to determine the specific targeting of this molecule to neurons and to show that the neuroprotective property of the drug was preserved after the drug had crossed the BBB. We have also demonstrated that BBB-Minibrain constitutes an interesting model to detect the passage of virus particles across the endothelial cells barrier and to monitor the infection of the Minibrain by neuroinvasive virus particles. The BBB-Minibrain is a reliable system, easy to handle for researcher trained in cell culture technology and predictive of the brain cells phenotypes after treatment or insult. The interest of such in cellulo testing would be twofold: introducing derisking steps early in the drug development on the one hand and reducing the use of animal testing on the other hand.

Here we present a protocol describing the setting of an in cellulo BBB (Blood brain barrier)-Minibrain polyester porous membrane culture insert system in order to evaluate the transport of biomolecules or infectious agents across a human BBB and their physiological impact on the neighboring brain cells.

The early screening of nervous system medicines on a pertinent and reliable in cellulo BBB model for their penetration and their interaction with the ...

Stereological Estimation of Cholinergic Fiber Length in the Nucleus Basalis of Meynert of the Mouse Brain

The length of cholinergic or other neuronal axons in various brain regions are often correlated with the specific function of the region. Stereology is a useful method to quantify neuronal profiles of various brain structures. Here we provide a software-based stereology protocol to estimate the total length of cholinergic fibers in the nucleus basalis of Meynert (NBM) of the basal forebrain. The method uses a space ball probe for length estimates. The cholinergic fibers are visualized by choline acetyltransferase (ChAT) immunostaining with the horseradish peroxidase-diaminobenzidine (HRP-DAB) detection system. The staining protocol is also valid for fiber and cell number estimation in various brain regions using stereology software. The stereology protocol can be used for estimation of any linear profiles such as cholinoceptive fibers, dopaminergic/catecholaminergic fibers, serotonergic fibers, astrocyte processes, or even vascular profiles.

Neuronal fiber length within a three-dimensional structure of a brain region is a reliable parameter to quantify specific neuronal structural integrity or degeneration. This article details a stereological quantification method to measure cholinergic fiber length within the nucleus basalis of Meynert in mice as an example.

The length of cholinergic or other neuronal axons in various brain regions are often correlated with the specific function of the region. Stereology is a ...

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Many reports in the last 15 years have assessed changes in the auditory brainstem response (ABR) waveform after insults such as noise exposure. Common changes include reductions in the peak 1 amplitude and the relative latencies of the later peaks, as well as increased central gain, which is reflected by a relative increase in the amplitudes of the later peaks compared to the amplitude of peak 1. Many experimenters identify the peaks and troughs visually to assess their relative heights and latencies, which is a laborious process when the waveforms are collected in 5 dB increments throughout the hearing range for each frequency and condition. This paper describes free routines that may be executed in the open-source platform R with the RStudio interface to semi-automate the measurements of the peaks and troughs of auditory brainstem response (ABR) waveforms. The routines identify the amplitudes and latencies of peaks and troughs, display these on a generated waveform for inspection, collate and annotate the results into a spreadsheet for statistical analysis, and generate averaged waveforms for figures. In cases when the automated process misidentifies the ABR waveform, there is an additional tool to assist in correction. The goal is to reduce the time and effort needed to analyze the ABR waveform so that more researchers will include these analyses in the future.

This article describes the semi-automated measurement of the amplitudes and latencies of the first five peaks and troughs in the auditory brainstem response waveform. An additional routine compiles and annotates the data into a spreadsheet for experimenter analysis. These free computer routines are executed using the open-source statistical package R.

Many reports in the last 15 years have assessed changes in the auditory brainstem response (ABR) waveform after insults such as noise exposure. Common ...

Restoring Post-Stroke Motor Function by Motor Imagery Brain-Computer Interface

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

The rehabilitation effect of patients with moderate or severe upper limb motor dysfunction after stroke is poor, which has been the focus of research owing to the difficulties encountered. Brain-computer interface (BCI) represents a hot frontier technology in brain neuroscience research. It refers to the direct conversion of the sensory perception, imagery, cognition, and thinking of users or subjects into actions, without reliance on peripheral nerves or muscles, to establish direct communication and control channels between the brain and external devices. Motor imagery brain-computer interface (MI-BCI) is the most common clinical application of rehabilitation as a non-invasive means of rehabilitation. Previous clinical studies have confirmed that MI-BCI positively improves motor dysfunction in patients after stroke. However, there is a lack of clinical operation demonstration. To that end, this study describes in detail the treatment of MI-BCI for patients with moderate and severe upper limb dysfunction after stroke and shows the intervention effect of MI-BCI through clinical function evaluation and brain function evaluation results, thereby providing ideas and references for clinical rehabilitation application and mechanism research.

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

The purpose of this study is to provide an important reference for the standard clinical operation of motor imagery brain-computer interface (MI-BCI) for upper limb motor dysfunction after stroke.

The rehabilitation effect of patients with moderate or severe upper limb motor dysfunction after stroke is poor, which has been the focus of research ...