Single-Trial Classification of Different Movements on One Arm Based on ERD/ERS and Corticomuscular Coherence

Electroencephalography (EEG)-based brain-machine interface (BMI) is widely applied to control external devices like a wheel chair or a robotic arm, to restore motor function. EEG is useful to distinguish between left arm and right arm movements, however, it is difficult to classify the different movements on one arm. In this paper, a two-step single-trial classification method is proposed to recognize three movements (make a fist, hand extension and elbow flexion) of left and right arms: (1) distinguish between left arm and right arm movements by decoding event-related (de) synchronization (ERD/ERS) and (2) recognize the specific movement of this arm using corticomuscular coherence as features. Four healthy subjects are employed in a cue-based motor execution (ME) experiment. In Step one, ERD and post-movement ERS are found over the contralateral sensorimotor area; in Step two, for each movement, only the beta-band coherence between C3/C4 and the corresponding agonistic muscle is significant. The classification results show the best accuracy of Step one and Step two is 88.10% and 93.33%, respectively. This proposed method achieves a total accuracy of 82.22%. This study demonstrates that our method is effective to classify different movements on one arm, and provides the theoretic basis and technical support for the practical development of BMI-based motor restoration applications.