| dc.description.abstract | Brain Computer Interface(BCI) is provided as a bridge for disabled people to communicate with the world. Motor imagery(MI) is an important part of the BCI. MI and action observation (AO) have been considered as effective ways for motor learning. In complex learning tasks, AO is considered a more effective method to train brain motor cortex, compared to MI. In our study, we constructed a human mirror neuron system (hMNS) as pre-trained task for suject’s MI training. The hMNS provided subjects reference images for MI, and tried to guide the movements of the left/right hand in different angles under virtual reality(VR) environment. Our EEG experiment contained two parts. In the first part, subjects were requested to performed a hMNS task by viewing AO videos, and EEG data were collected to train a pre-trained model for the subsequent MI task. In the second part, MI task was given to subjects and the MI classification was performed using the pre-trained hMNS deep learning model obtained from the experiment of the first part. In the MI task, instead of view hand motions, an arrow indicator was used to indicate the direction for MI. The purpose of the aforementioned two-step EEG experiment is trying to build a new MI training process based on a hMNS pre-training approach. A thirteen-channel dry-electrode wireless EEG system was used to measure EEG signals from electrode positions at Fp1, Fp2, F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, and O2 according to the international 10-20 montage system. The EEG data were real-time filtered into ten frequency bands as features. The covariance matrixes obtained from the features of ten frequency bands were calculated and projected into the Riemann space. The mean of the projected values in Riemann were calculated and the tangent space mapping(TSM) method was used to transfer EEG data to the Euclidean space for prediction and classification. The results showed that the subjects, who participated in pre-trained by hMNS task, had significant improvements in the following MI tasks. In the future, it is expected to enhance the application of BCI in various fields. | en_US |