| dc.description.abstract | Owing to the rapid developments in brain wave acquisition technologies, brain computer interface (BCI) has drawn great attention in recent years. Scientists are trying to develop BCI technologies for neuromuscular paralyzed patients to communicate with external environments through their intentions. Nevertheless, the key issue of achieving correct intention detection is the requisite of abundant brain wave data for classifier training. Especially, precise labeling of brain wave data in different conditions is important. Therefore, this study intended to combine EEG data with subject’s limb postures, in order to obtain training data for BCI from subject’s daily life movement recordings. In this research, we mounted two inertial movement units (IMU) on subject’s left and right wrists. The EEG electrodes were attached on C1, C2, C3, and C4 positions, according to international 10-20 EEG system. Subjects were asked to extend and flex their left and right elbows individually, and timing of subject’s posture data was wirelessly transmitted for EEG labeling. EEG data were segmented into epochs from -4s to 2s anchored to subject’s movement onsets. Labeled EEG data were used to train convolutional neural network for BCI detections. Our system has achieved acceptable detection rates by exploring the connections between subject’s movements and EEG changes. | en_US |