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...

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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. 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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. 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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 ...

Measuring performance is crucial for any research or clinical application involving brain computer interfaces. CBLE helps evaluate the effectiveness of a system for any particular user. CBLE can be used to predict the user's P300 Speller accuracy from just three to eight characters of data.To begin, install the CBLE performance estimation graphical user interface. Open MATLAB and change the current directory to the graphical user interface folder. Click on the Apps tab, select My apps, and choose CBLE Performance Estimation.In the dropdown menu, click Select dataset format and choose the desired option. Next, click the Select input folder button to choose the directory for the EEG dataset. In the number of participants text box, enter the number of participants for the estimation.If using Brain Invader data, specify the sampling rate of the dataset. Choose a decimation value to downs sample the data set to approximately 20 hertz. Specify the time window for the classification in milliseconds.Then define the shift window for CBLE in milliseconds. Once done, click the Set parameters button to set analysis parameters. To split the data set, select the number of targets for the training set size.Click the Split the dataset button to divide the dataset into training and test sets. For Braininvaders, click the train a model button to apply linear regression using equation two on the training dataset. Next, click Predict Accuracy to apply the trained classifier model to the test feature set and predict the accuracies using equation one.Select the maximum target number X for test set consideration and press find X target accuracy. Then click the find vCBLE button to get the vCBLE for all targets. Click the Calculate RMSE button to calculate the RMSE between both predictions based on vCBLE with BCI accuracy and X target accuracy with BCI accuracy.Now click accuracy versus vCBLE to observe the relationship between total accuracy and total vCBLE for all participants. Click RMSE of BCI and vCBLE to display the RMSE curve of BCI accuracy and vCBLE. For predicting the accuracy of an individual participant in Sub ID, enter the subject ID.Then select a target number N and click Predict to get the predicted accuracy of the test participant.Open the CBLE Performance Estimation graphical user interface. In the dropdown menu, click Select dataset format and choose the BCI2000 option. Click the Select input folder button to choose the directory for the EEG dataset.Enter the number of participants for the estimation. Choose a decimation value and specify the original in CBLE window. Then choose character number X.Next, in the ID length field, enter the subject ID length from the dataset files.In the channel ID field, indicate the total number of channels or specific channel numbers for analysis. After checking the data format, specify training and testing file names. From the BCI2000 dataset, check the test file.Then in the test file number fields, enter the test file number. Now click Run, and wait until all the parameters from the checklist are ticked. Then click Accuracy versus vCBLE to observe the relationship between total accuracy and total vCBLE for all participants.Finally, click RMSE of BCI and vCBLE to display the RMSE curve of BCI accuracy and vCBLE. A strong negative correlation was observed between BCI accuracy plotted against vCBLE for the braininvaders dataset. The RMSE of vCBLE plotted against different test dataset sizes showed that vCBLE performs better than BCI accuracy.vCBLE is capable of predicting BCI accuracy using only seven characters. vCBLE prediction models indicated that 10 individuals were required to build the regression model for the relationship between vCBLE and accuracy for a particular experimental paradigm. The vCBLE model showed better performance for the Michigan dataset with same day training and testing datasets.The mean RMSE calculated over three days for vCBLE and accuracy models using Michigan data showed that the vCBLE fit had a lower RMSE when the test included less than six characters. The RMSE of vCBLE accuracy drops by only 0.025 from three characters to the optimal number, suggesting little gain beyond three characters for the small test set. In this work, we use simple linear regression, But CBLE can be computed with any time dependent combination of feature extraction, feature selection, and classifier.We have used CBLE to reduce the data required for our test sets. Other labs have used it to investigate the variable latency between stimuli and the related brain responses.

p300-based-brain-computer-interface-speller-performance-estimation

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Neuroscience

416 Görüntüleme.  Kansas State University. Performansın ölçülmesi, beyin bilgisayar arayüzlerini içeren herhangi bir araştırma veya klinik uygulama için çok önemlidir. CBLE, herhangi bir kullanıcı için bir sistemin etkinliğini değerlendirmeye yardımcı olur. CBLE, kullanıcının P300 Speller doğruluğunu yalnızca üç ila sekiz karakterlik verilerden tahmin etmek için kullanılabilir.Başlamak için, CBLE performans tahmini grafik kullanıcı arayüzünü yükleyin. MATLAB'ı açın ve geçerli dizini grafi...