Decoding Coordinated Directions of Bimanual Movements From EEG Signals
Mingming Zhang, Junde Wu, Jongbin Song, Ruiqi Fu, Rui Ma, Yichuan Jiang, Yi-Feng Chen
Abstract
Bimanual coordination is common in human daily life, whereas current research focused mainly on decoding unimanual movement from electroencephalogram (EEG) signals. Here we developed a brain-computer interface (BCI) paradigm of task-oriented bimanual movements to decode coordinated directions from movement-related cortical potentials (MRCPs) of EEG. Eight healthy subjects participated in the target-reaching task, including (1) performing leftward, midward, and rightward bimanual movements, and (2) performing leftward and rightward unimanual movements. A combined deep learning model of convolution neural network and bidirectional long short-term memory network was proposed to classify movement directions from EEG. Results showed that the average peak classification accuracy for three coordinated directions of bimanual movements reached 73.39 ± 6.35 %. The binary classification accuracies achieved 80.24 ± 6.25 , 82.62 ± 7.82 , and 86.28 ± 5.50 % for leftward versus midward, rightward versus midward and leftward versus rightward, respectively. We also compared the binary classification (leftward versus rightward) of bimanual, left-hand, and right-hand movements, and accuracies achieved 86.28 ± 5.50 %, 75.67 ± 7.18 %, and 77.79 ± 5.65 %, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.