Application of brain-computer-interface in awareness detection using machine learning methods

The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests, however, the mis-diagnosis rates have been relatively high. In this study, we applied brain-computer interface (BCI) to awareness detection with a passive auditory stimulation paradigm. 12 subjects with normal hearing were invited to collect electroencephalogram (EEG) based on a BCI communication system, in which EEG signals are transmitted wirelessly. After necessary preprocessing, RBF-SVM and EEGNet were used for algorithm realization and analysis. For a single subject, RBF-SVM can distinguish his (her) name stimuli awareness with classification accuracies ranging from 60–95%. EEGNet was used to learn all subjects' data and improved accuracy to 78.04% for characteristics finding and model generalization. Moreover, we completed the supplementary analysis work from the time domain and time-frequency domain. This study applied BCI communication to human awareness detection, proposed a passive auditory paradigm, and proved the effectiveness, which could be an inspiration for brain, mental or physical diseases diagnosis and detection.