The Subspace Regularization Method Improves ErrP Detection by EEGNET in BCI Experiments

In this study, the subspace regularization method was applied on the Electroencephalographic (EEG) signal recorded during stimulation of the Error Potential (ErrP) in order to improve the detection of the latter.The ErrP is stimulated through the presentation of an erroneous event to the subject.The recorded signals were processed with the subspace regularization method to remove the background EEG not related to the erroneous event.Then, the ErrP and Non-ErrP epochs (both raw and processed with the proposed method) were classified using EEGNET, a Convolutional Neural Network considered golden standard for EEG classification.The results show that elaborating the signals with the proposed method highlight the typical characteristics of the ErrP epochs both in temporal and frequency domain.Moreover, the classification metrics evaluated, always increase if compared to not processed signal (i.e.maximum increase in accuracy, balanced accuracy and F1-score are of 7.7%, 10.1% and 11% respectively).These findings suggest that the subspace regularization method can improve the performance of ErrP-based Brain Computer Interfaces (BCI) and can be used also in real time application and for asynchronous classification of erroneous events.