The proposed method combining multiple-channel features from temporal, spectral, and spatial domain has better classification performance than any individual feature alone. The average AUC reaches 0.7818 with combined features, while 0.7270, 0.6376, 0.7330 with single temporal, spectral, and spatial features respectively. The receiver operating characteristic (ROC) curve and area under ROC curve (AUC) further indicate the superior performance of the combination of features over any single features in error detection. The total classification accuracy with combination of features is 76.7 %.
In order to evaluate the generalization ability across subjects, the data from 16 participants were used for training and the rest for testing. 26 participants (13 males, mean age = 28.8 ± 5.4, range 20–37) took part in the study, who engaged in a P300 speller task spelling cued words from a 36-character matrix. This study proposed a new approach of fusing multiple-channel features from temporal, spectral, and spatial domains through two times of dimensionality reduction based on neural network. It has been demonstrated viable to extract error potential from electroencephalography recordings.
However, errors existing in BCI systems affect their performance greatly, which in turn confines the development and application of BCI technology. Is an assistive technology that conveys users’ intentions by decoding various brain activities and translating them into control commands, without the need of verbal instructions and/or physical interactions.