SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in Brain–Computer Interfaces

To allow motor-disabled people for communication, Brain–Computer Interfaces (BCIs) are being developed. Such a communication does not depend on the brain's normal output pathways of peripheral nerves and muscles, but is based on analysis of recorded brain activity. In this paper, we compare the performance of power features and phase locking values (PLVs) computed from broadband and narrowband filtered EEG signals for discriminating 3 mental tasks in the framework of a BCI. EEG signals were recorded from 5 subjects while performing the 3 mental tasks left- and right-hand movement imagination and word generation. To reduce the total amount of features, the most discriminative features were selected in a 2-step feature selection procedure by SVM-based recursive feature elimination.Significance tests demonstrated that band power features were more discriminative when they were computed in the narrower frequency band 8–12 Hz. In case of PLV features, the discrimination of mental tasks was significantly better when they were computed from the broader 8–30 Hz frequency band, as compared to the narrower bands 8–12, 13–18 and 19–30 Hz.