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- W3108223469 abstract "Error-related potentials (ErrP) is elicited when an individual makes or perceives an error while conducting or observing a task. Many researchers are striving to improve performance of brain-computer interfaces (BCIs) by detecting ErrP in brain signals of single trial. However, it is still difficult to attain high performance of ErrP detections, which is needed by those who attempt to use ErrP. In this paper, we propose a novel approach to detecting ErrP. It computes a coefficient of determination for each time point after error onset according to brain signals of all trials in training set of an individual, constructs a curve of coefficients of determination (CCD) over time for each channel, obtains a time window for an individual by searching the peak of the mean of CCDs over his/her frontocentral scalp area, transforms the brain signals in the time window to feature vectors, and finally builds a classifier on the feature vectors for an individual. We compared this approach with such commonly used methods as xDAWN, PCA and ICA in a brain signal data set of eight individuals, which was acquired during the experiments of P300 Speller BCI comprising feedback, showing that our approach could achieve a better performance from a comprehensive perspective on accuracy, AUC and F1-score." @default.
- W3108223469 created "2020-12-07" @default.
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- W3108223469 date "2020-10-17" @default.
- W3108223469 modified "2023-09-27" @default.
- W3108223469 title "An approach to detecting ErrP elicited by feedback of P300 Speller BCI based on coefficients of determination" @default.
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- W3108223469 doi "https://doi.org/10.1109/cisp-bmei51763.2020.9263583" @default.
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