Reconstructing Degree of Forearm Rotation from Imagined movements for BCI-based Robot Hand Control

Brain-computer interface (BCI) is an important tool for rehabilitation and control of an external device (e.g., robot arm or home appliances). Fully reconstruction of upper limb movement from brain signals is one of the critical issues for intuitive BCI. However, decoding of forearm rotation from imagined movements using electroencephalography (EEG) is difficult to decode degree of rotation accurately. In this paper, we reconstructed imagined forearm rotation from low- frequency (0.3-3 Hz) of EEG signals. We selected 20 EEG channel on motor cortex for analysis. Ten healthy subjects participated in our experiment. The subjects performed actual and imagined forearm rotation to reach different targets. We trained a reconstruction decoder which used the EEG signals measured from actual movements and the kinematic information only. Additionally, we applied a long short-term memory (LSTM) network to enhance decoding performances. As a result, we achieved the high correlation performance (Average: 0.67) to decode imagined forearm rotation angle. This result has demonstrated that the reconstruction decoder which is trained by the EEG data from actual movement has effective to decode robustly for the imagined forearm rotation angle.