Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network

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aut.relation.issue18
aut.relation.journalSensors
aut.relation.startpage6274
aut.relation.volume21
dc.contributor.authorUsama, N
dc.contributor.authorNiazi, IK
dc.contributor.authorDremstrup, K
dc.contributor.authorJochumsen, M
dc.date.accessioned2023-06-13T04:25:58Z
dc.date.available2023-06-13T04:25:58Z
dc.date.issued2021-09-18
dc.description.abstractError-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.
dc.identifier.citationSensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 21(18), 6274-. doi: 10.3390/s21186274
dc.identifier.doi10.3390/s21186274
dc.identifier.issn1424-8220
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/10292/16261
dc.languageeng
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/1424-8220/21/18/6274
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectbrain–computer interface
dc.subjectcalibration
dc.subjectclassifier interpretation
dc.subjecterror-related potentials
dc.subjectneurorehabilitation
dc.subjectstroke
dc.subject46 Information and Computing Sciences
dc.subject40 Engineering
dc.subject4003 Biomedical Engineering
dc.subjectRehabilitation
dc.subjectNeurosciences
dc.subjectBioengineering
dc.subjectStroke
dc.subjectAssistive Technology
dc.subject0301 Analytical Chemistry
dc.subject0502 Environmental Science and Management
dc.subject0602 Ecology
dc.subject0805 Distributed Computing
dc.subject0906 Electrical and Electronic Engineering
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.subject.meshBrain
dc.subject.meshBrain-Computer Interfaces
dc.subject.meshElectroencephalography
dc.subject.meshHumans
dc.subject.meshNeural Networks, Computer
dc.subject.meshReproducibility of Results
dc.subject.meshStroke
dc.subject.meshBrain
dc.subject.meshHumans
dc.subject.meshElectroencephalography
dc.subject.meshReproducibility of Results
dc.subject.meshStroke
dc.subject.meshBrain-Computer Interfaces
dc.subject.meshNeural Networks, Computer
dc.subject.meshBrain
dc.subject.meshBrain-Computer Interfaces
dc.subject.meshElectroencephalography
dc.subject.meshHumans
dc.subject.meshNeural Networks, Computer
dc.subject.meshReproducibility of Results
dc.subject.meshStroke
dc.titleDetection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network
dc.typeJournal Article
pubs.elements-id508408
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