Classification of auditory attention focuses during speech perception

Passive brain-computer interfaces (BCIs) covertly decode the cognitive and emotional states of users by using neurophysiological signals. An important issue for passive BCIs is to monitor the attentional state of the brain. Previous studies mainly focus on the classification of attention levels, i.e. high vs. low levels, but few has investigated the classification of attention focuses during speech perception. In this paper, we tried to use electroencephalography (EEG) to recognize the subject's attention focuses on either call sign or number when listening to a short sentence. Fifteen subjects participated in this study, and they were required to focus on either call sign or number for each listening task. A new algorithm was proposed to classify the EEG patterns of different attention focuses, which combined common spatial pattern (CSP), short-time Fourier transformation (STFT) and discriminative canonical pattern matching (DCPM). As a result, the accuracy reached an average of 78.38% with a peak of 93.93% for single trial classification. The results of this study demonstrate the proposed algorithm is effective to classify the auditory attention focuses during speech perception.