In recent years, diagnostic studies of brain disorders based on auditory event-related potentials (AERP) have become a hot topic. Research showed that AERP might help to detect patient consciousness, especially using the subjects' own name (SON). In this study, we conducted a preliminary analysis of the brain response to Chinese name stimuli. Twelve subjects participated in this study. SONs were used as target stimuli for each trial. The names used for non-target stimuli were divided into three Chinese character names condition (3CC) and two Chinese characters names condition (2CC). Thus, each subject was required to be in active (silent counting) and passive mode (without counting) with four conditions [(passive, active) × (3CC, 2CC)]. We analyzed the spatio-temporal features for each condition, and we used SVM for target vs. non-target classification. The results showed that the passive mode under 3CC conditions showed a similar brain response to the active mode, and when 3CC was used as a non-target stimulus, the brain response induced by the target stimulus would have a better interaction than 2CC. We believe that the passive mode 3CC may be a good paradigm to replace the active mode which might need more attention from subjects. The results of this study can provide certain guidelines for the selection and optimization of the paradigm of auditory event-related potentials based on name stimulation.

Recent years, researches on diagnosis of anesthesia status and postoperative recovery based on electroencephalogram (EEG) signals have become increasingly popular. Bispectral index (BIS) is the earliest and most effective indicator for monitoring the depth of clinical anesthesia, and the waveform of intraoperative EEG can be displayed in real time. Excessive anesthesia and other conditions will cause burst suppression (BS) of intraoperative EEG, which will prolong the operation time and ICU stay time, and increase the incidence of postoperative delirium. This study analyzes the changes of intraoperative BIS monitoring on real-time EEG waveform and spectrogram, the results showed that BIS can predict the occurrence of EEG BS and then reduce the operation time and the incidence of postoperative delirium through rational use of intraoperative drugs.

Recent years, researches on diagnosis of anesthesia status and postoperative recovery based on electroencephalogram (EEG) signals have become increasingly popular. Bispectral index (BIS) is the earliest and most effective indicator for monitoring the depth of clinical anesthesia, and the waveform of intraoperative EEG can be displayed in real time. Excessive anesthesia and other conditions will cause burst suppression (BS) of intraoperative EEG, which will prolong the operation time and ICU stay time, and increase the incidence of postoperative delirium. This study analyzes the changes of intraoperative BIS monitoring on real-time EEG waveform and spectrogram, the results showed that BIS can predict the occurrence of EEG BS and then reduce the operation time and the incidence of postoperative delirium through rational use of intraoperative drugs.