The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis rates have been relatively high.In this study,we applied brain-c...The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis 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.展开更多
基金supported by the Science and Technology Commission of Shanghai Municipality(STCSM)Research Fund(21JC1405300)to Fan Minthe National Key Research and Development Program of China(2018YFC0831102)sponsored by the Shanghai Key Research Laboratory of NSAI。
文摘The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis 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.
