Subject identification from electroencephalogram (EEG) signals during imagined speech
We investigate the potential of using electrical brainwave signals during imagined speech to identify which subject the signals originated from. Electroencephalogram (EEG) signals were recorded at the University of California, Irvine (UCI) from 6 volunteer subjects imagining speaking one of two syllables, /ba/ and /ku/, at different rhythms without performing any overt actions. In this work, we assess the degree of subject-to-subject variation and the feasibility of using imagined speech for subject identification. The EEG data are first preprocessed to reduce the effects of artifacts and noise, and autoregressive (AR) coefficients are extracted from each electrode's signal and concatenated for subject identification using a linear SVM classifier. The subjects were identifiable to a 99.76% accuracy, which indicates a clear potential for using imagined speech EEG data for biométrie identification due to its strong inter-subject variation. Furthermore, the subject identification appears to be tolerant to differing conditions such as different imagined syllables and rhythms (as it is expected that the subjects will not imagine speaking the syllables at exactly the same rhythms from trial to trial). The proposed approach was also tested on a publicly available database consisting of EEG signals corresponding to Visual Evoked Potentials (VEPs) to test the applicability of the proposed method on a larger number of subjects, and it was able to classify 120 subjects with 98.96% accuracy.