| Summary: | 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 <inline-formula> <tex-math notation="LaTeX">$73.39~\pm ~6.35$ </tex-math></inline-formula>%. The binary classification accuracies achieved <inline-formula> <tex-math notation="LaTeX">$80.24~\pm ~6.25$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$82.62~\pm ~7.82$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$86.28~\pm ~5.50$ </tex-math></inline-formula>% 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 <inline-formula> <tex-math notation="LaTeX">$86.28~\pm ~5.50$ </tex-math></inline-formula>%, <inline-formula> <tex-math notation="LaTeX">$75.67~\pm ~7.18$ </tex-math></inline-formula>%, and <inline-formula> <tex-math notation="LaTeX">$77.79~\pm ~5.65$ </tex-math></inline-formula>%, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.
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