Usama, NNiazi, IKDremstrup, KJochumsen, M2023-06-132023-06-132021-09-18Sensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 21(18), 6274-. doi: 10.3390/s211862741424-82201424-8220https://hdl.handle.net/10292/16261Error-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.https://creativecommons.org/licenses/by/4.0/brain–computer interfacecalibrationclassifier interpretationerror-related potentialsneurorehabilitationstroke46 Information and Computing Sciences40 Engineering4003 Biomedical EngineeringRehabilitationNeurosciencesBioengineeringStrokeAssistive Technology0301 Analytical Chemistry0502 Environmental Science and Management0602 Ecology0805 Distributed Computing0906 Electrical and Electronic EngineeringAnalytical Chemistry3103 Ecology4008 Electrical engineering4009 Electronics, sensors and digital hardware4104 Environmental management4606 Distributed computing and systems softwareBrainBrain-Computer InterfacesElectroencephalographyHumansNeural Networks, ComputerReproducibility of ResultsStrokeBrainHumansElectroencephalographyReproducibility of ResultsStrokeBrain-Computer InterfacesNeural Networks, ComputerBrainBrain-Computer InterfacesElectroencephalographyHumansNeural Networks, ComputerReproducibility of ResultsStrokeJournal ArticleOpenAccess10.3390/s21186274