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A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection
Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signa...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411431/ https://www.ncbi.nlm.nih.gov/pubmed/28512396 http://dx.doi.org/10.3389/fnins.2017.00226 |
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author | Bhattacharyya, Saugat Konar, Amit Tibarewala, D. N. Hayashibe, Mitsuhiro |
author_facet | Bhattacharyya, Saugat Konar, Amit Tibarewala, D. N. Hayashibe, Mitsuhiro |
author_sort | Bhattacharyya, Saugat |
collection | PubMed |
description | Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signals which occur when the participant observes an erroneous feedback from the system. ErrP holds significance in such closed-loop system, as BCI is prone to error and we need an effective method of systematic error detection as feedback for correction. In this paper, we have proposed a novel scheme for online detection of error feedback directly from the EEG signal in a transferable environment (i.e., across sessions and across subjects). For this purpose, we have used a P300-speller dataset available on a BCI competition website. The task involves the subject to select a letter of a word which is followed by a feedback period. The feedback period displays the letter selected and, if the selection is wrong, the subject perceives it by the generation of ErrP signal. Our proposed system is designed to detect ErrP present in the EEG from new independent datasets, not involved in its training. Thus, the decoder is trained using EEG features of 16 subjects for single-trial classification and tested on 10 independent subjects. The decoder designed for this task is an ensemble of linear discriminant analysis, quadratic discriminant analysis, and logistic regression classifier. The performance of the decoder is evaluated using accuracy, F1-score, and Area Under the Curve metric and the results obtained is 73.97, 83.53, and 73.18%, respectively. |
format | Online Article Text |
id | pubmed-5411431 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-54114312017-05-16 A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection Bhattacharyya, Saugat Konar, Amit Tibarewala, D. N. Hayashibe, Mitsuhiro Front Neurosci Neuroscience Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signals which occur when the participant observes an erroneous feedback from the system. ErrP holds significance in such closed-loop system, as BCI is prone to error and we need an effective method of systematic error detection as feedback for correction. In this paper, we have proposed a novel scheme for online detection of error feedback directly from the EEG signal in a transferable environment (i.e., across sessions and across subjects). For this purpose, we have used a P300-speller dataset available on a BCI competition website. The task involves the subject to select a letter of a word which is followed by a feedback period. The feedback period displays the letter selected and, if the selection is wrong, the subject perceives it by the generation of ErrP signal. Our proposed system is designed to detect ErrP present in the EEG from new independent datasets, not involved in its training. Thus, the decoder is trained using EEG features of 16 subjects for single-trial classification and tested on 10 independent subjects. The decoder designed for this task is an ensemble of linear discriminant analysis, quadratic discriminant analysis, and logistic regression classifier. The performance of the decoder is evaluated using accuracy, F1-score, and Area Under the Curve metric and the results obtained is 73.97, 83.53, and 73.18%, respectively. Frontiers Media S.A. 2017-05-02 /pmc/articles/PMC5411431/ /pubmed/28512396 http://dx.doi.org/10.3389/fnins.2017.00226 Text en Copyright © 2017 Bhattacharyya, Konar, Tibarewala and Hayashibe. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Bhattacharyya, Saugat Konar, Amit Tibarewala, D. N. Hayashibe, Mitsuhiro A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection |
title | A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection |
title_full | A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection |
title_fullStr | A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection |
title_full_unstemmed | A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection |
title_short | A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection |
title_sort | generic transferable eeg decoder for online detection of error potential in target selection |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411431/ https://www.ncbi.nlm.nih.gov/pubmed/28512396 http://dx.doi.org/10.3389/fnins.2017.00226 |
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