Cargando…
Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network
Error-related potentials (ErrPs) have been proposed as a means for improving brain–computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant w...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8472485/ https://www.ncbi.nlm.nih.gov/pubmed/34577481 http://dx.doi.org/10.3390/s21186274 |
_version_ | 1784574740772421632 |
---|---|
author | Usama, Nayab Niazi, Imran Khan Dremstrup, Kim Jochumsen, Mads |
author_facet | Usama, Nayab Niazi, Imran Khan Dremstrup, Kim Jochumsen, Mads |
author_sort | Usama, Nayab |
collection | PubMed |
description | Error-related potentials (ErrPs) have been proposed as a means for improving brain–computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test–retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on two separate days where they attempted to perform a movement after which they received feedback (error/correct) while continuous EEG was recorded. The EEG was divided into epochs: ErrPs and NonErrPs. The epochs were classified with a multi-layer perceptron ANN based on temporal features or the entire epoch. Additionally, the features were classified with shrinkage LDA. The features were waveforms of the ErrPs and NonErrPs from the sensorimotor cortex to improve the explainability and interpretation of the output of the classifiers. Three calibration schemes were tested: within-day, between-day, and across-participant. Using within-day calibration, 90% of the data were correctly classified with the entire epoch as input to the ANN; it decreased to 86% and 69% when using temporal features as input to ANN and LDA, respectively. There was poor test–retest reliability between the two days, and the other calibration schemes led to accuracies in the range of 63–72% with LDA performing the best. There was no association between the individuals’ impairment level and classification accuracies. The results show that ErrPs can be classified in individuals with stroke, but that user- and session-specific calibration is needed for optimal ErrP decoding with this approach. The use of ErrP/NonErrP waveform features makes it possible to have a physiological meaningful interpretation of the output of the classifiers. The results may have implications for labelling data continuously in BCIs for stroke rehabilitation and thus potentially improve the BCI performance. |
format | Online Article Text |
id | pubmed-8472485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84724852021-09-28 Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network Usama, Nayab Niazi, Imran Khan Dremstrup, Kim Jochumsen, Mads Sensors (Basel) Article Error-related potentials (ErrPs) have been proposed as a means for improving brain–computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test–retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on two separate days where they attempted to perform a movement after which they received feedback (error/correct) while continuous EEG was recorded. The EEG was divided into epochs: ErrPs and NonErrPs. The epochs were classified with a multi-layer perceptron ANN based on temporal features or the entire epoch. Additionally, the features were classified with shrinkage LDA. The features were waveforms of the ErrPs and NonErrPs from the sensorimotor cortex to improve the explainability and interpretation of the output of the classifiers. Three calibration schemes were tested: within-day, between-day, and across-participant. Using within-day calibration, 90% of the data were correctly classified with the entire epoch as input to the ANN; it decreased to 86% and 69% when using temporal features as input to ANN and LDA, respectively. There was poor test–retest reliability between the two days, and the other calibration schemes led to accuracies in the range of 63–72% with LDA performing the best. There was no association between the individuals’ impairment level and classification accuracies. The results show that ErrPs can be classified in individuals with stroke, but that user- and session-specific calibration is needed for optimal ErrP decoding with this approach. The use of ErrP/NonErrP waveform features makes it possible to have a physiological meaningful interpretation of the output of the classifiers. The results may have implications for labelling data continuously in BCIs for stroke rehabilitation and thus potentially improve the BCI performance. MDPI 2021-09-18 /pmc/articles/PMC8472485/ /pubmed/34577481 http://dx.doi.org/10.3390/s21186274 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Usama, Nayab Niazi, Imran Khan Dremstrup, Kim Jochumsen, Mads Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network |
title | Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network |
title_full | Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network |
title_fullStr | Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network |
title_full_unstemmed | Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network |
title_short | Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network |
title_sort | detection of error-related potentials in stroke patients from eeg using an artificial neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8472485/ https://www.ncbi.nlm.nih.gov/pubmed/34577481 http://dx.doi.org/10.3390/s21186274 |
work_keys_str_mv | AT usamanayab detectionoferrorrelatedpotentialsinstrokepatientsfromeegusinganartificialneuralnetwork AT niaziimrankhan detectionoferrorrelatedpotentialsinstrokepatientsfromeegusinganartificialneuralnetwork AT dremstrupkim detectionoferrorrelatedpotentialsinstrokepatientsfromeegusinganartificialneuralnetwork AT jochumsenmads detectionoferrorrelatedpotentialsinstrokepatientsfromeegusinganartificialneuralnetwork |