|Abstract: ||心電圖、腦電波、肌電波等是臨床上重要的診斷儀器，傳統溼式 Ag/AgCl電|
結合腦波電極設置在 10-20 EEG System之 C3、Cz、C4、F3、F4位置的腦波，每隔 8
為分析腦波的區間，透過小波轉換 (wavelet transform) 的方式取出此腦波區間的
(Event RelatedDesynchronization/Event RelatedSynchronization, ERD/ERS)，並將五個通
(Convolution Neural Networks, CNN)及長短期記憶神經網路(Long Short-Term Memory,
LSTM)進行分析，達到 CNN 80% 及 LSTM 89% 準確率，並以此架構找出動作與腦波相
對應的連結。;Electrocardiogram (ECG), electroencephalogram(EEG), electromyography(EMG) are important diagnostic instruments in clinic. Although the Ag/AgCl electrode is quite stable in signal reception, it causes user discomfortable in the experiments, especially for using Electrolyte gel to reduce the impedance between the electrode and the skin. The use of wet type electrolyte may cause stimulation to the user′s skin, and even produce allergic reaction, so we use dry electrodes for experiments. Since it is pointed out in medical research that the specific bandwidth in EEG has a correlation between brain activity and motor performance, in this study we propose to develop a brain wave analysis system close to daily movements by integrating brain wave signal processing and IMU system to provide sufficient and marked brain wave training data. In this experiment, IMU is used as the marking tool, and angles change between different actions are used as the time mark of brain wave. The purpose of this experiment is to improve the accuracy of brain computer interface (BCI) by combining IMU and brain wave system. In this research, we mounted two IMU on subject’s left and right arm. The EEG electrodes were attached on C3,Cz,C4,F3, and F4 positions, according to international 10-20 EEG system. Subjects were asked to do specified motion between 8sec, and timing of subject’s posture data was wirelessly transmitted for EEG labeling. EEG data were segmented into epochs from -2sec anchored to subject’s movement onsets. Labeled EEG data were extracted Event RelatedDesynchronization/Event RelatedSynchronization (ERD/ERS) by wavelet transform, and we combine the five channels (C3,Cz,C4,F3, and F4) time-frequency relationship as two-dimension image. Using this image as Convolution Neural Networks(CNN) and Long Short-Term Memory(LSTM) input attained CNN 80% and LSTM 89%, to exploring the connections between subject’s movement and brain wave.