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As an inherent feedback mechanism in humans, Error-related Potentials (ErrPs) signals have received increasing attention for their potential to improve the accuracy and efficiency of Brain-computer interface (BCI) systems. However, the successful detection of ErrPs signals in a single trial is a great challenge due to the low signal-to-noise ratio (SNR) and the high individual differences. In this study, we propose a novel method that combines convolutional layers and transformer encoder modules to classify ErrPs from electroencephalogram (EEG) signals.

The raw data from all electrode channels were chosen as the inputs of the proposed method and two different dataset partition ways, which include within-session and cross-subject, were used to test the proposed model. The dataset used for evaluation is the ‘Monitoring error-related potentials’ dataset of the BNCI Horizon …