뇌파 신호 기반 BCI 연구에서 데이터 연속성의 영향 = Impact of Data Continuity in EEG Signal-based BCI Research

다국어 초록 (Multilingual Abstract)

This study conducted a comparative experiment on the continuity of time series data and the classification performance of artificial intelligence models. In BCI research using EEG signals, the performance of behavior and thought classification improved as the continuity of the data decreased. In particular, LSTM achieved a high performance of 0.8728 on data with low continuity, and DNN showed a performance of 0.9178 when continuity was not considered. This suggests that data without continuity may perform better. Additionally, data without continuity showed better performance in task classification. These results suggest that BCI research based on EEG signals can perform better by showing various data characteristics through shuffling rather than considering data continuity.