Version 1
: Received: 13 July 2023 / Approved: 14 July 2023 / Online: 17 July 2023 (02:32:36 CEST)
How to cite:
Chen, X.; Gupta, L. Analyses of Subsample Averaging of Single Trials for Event-Related Potential Classification. Preprints2023, 2023071022. https://doi.org/10.20944/preprints202307.1022.v1
Chen, X.; Gupta, L. Analyses of Subsample Averaging of Single Trials for Event-Related Potential Classification. Preprints 2023, 2023071022. https://doi.org/10.20944/preprints202307.1022.v1
Chen, X.; Gupta, L. Analyses of Subsample Averaging of Single Trials for Event-Related Potential Classification. Preprints2023, 2023071022. https://doi.org/10.20944/preprints202307.1022.v1
APA Style
Chen, X., & Gupta, L. (2023). Analyses of Subsample Averaging of Single Trials for Event-Related Potential Classification. Preprints. https://doi.org/10.20944/preprints202307.1022.v1
Chicago/Turabian Style
Chen, X. and Lalit Gupta. 2023 "Analyses of Subsample Averaging of Single Trials for Event-Related Potential Classification" Preprints. https://doi.org/10.20944/preprints202307.1022.v1
Abstract
Event-related potentials (ERPs) are estimated by averaging time-locked single trial electroencephalography (EEG) signals in response to specific events or stimuli. Classifying ERPs accurately is a challenge because (a) single trials have poor signal-to-noise-ratios (SNRs) and (b) it is difficult to collect large single trial ensembles to generate high SNR ERPs for classifier training and testing. The m-subsample averaging (m-SA) strategy which generates small-sample ERPs by repeated averaging of a small number of single trials drawn without replacement, has been proposed as a solution to the two problems. An ERP formed by averaging m single trials is referred to as an m-ERP where m is referred to as the averaging parameter. In this study, we conduct thorough analyses of m-SA and focus on issues not addressed in previous studies to better understand the beneficial properties of m-SA and to further support its application for ERP classification. Specifically, we (a) analyze the improvement in SNR as a function of m using the mean-root-mean-square SNR and visual analyses of m-ERP plots with confidence intervals, (b) analyze the improvement in interclass separation as a function of m, (c) determine how the SNR and interclass separation analyses can help to select the averaging parameter m, (d) determine the number of distinct m-ERPs that can be drawn from a single-trial ensemble, and (e) determine several probabilities related to the generation of distinct m-ERPs. Furthermore, an extensive set of experiments are designed to analyze the performance of support vector machine and convolution neural network classifiers employing m-SA with various combinations of the averaging parameters used for generating the training and test sets. The results confirm that ERPs can be classified accurately using small subsample averaging. Most importantly, it is concluded that m-SA can be deployed in practice to accurately classify ERPs in brain activity research and in clinical applications without having to collect a prohibitively large number of single trials.
Keywords
ERP classification; single trial averaging; interclass separation; convolution neural networks; support vector machines.
Subject
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
