Decoding Coordinated Directions of Bimanual Movements From EEG Signals

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 $73.39~\pm ~6.35$ %. The binary classification accuracies achieved $80.24~\pm ~6.25$ , $82.62~\pm ~7.82$ , and $86.28~\pm ~5.50$ % 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 $86.28~\pm ~5.50$ %, $75.67~\pm ~7.18$ %, and $77.79~\pm ~5.65$ %, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.

Keywords

• Brain–computer interface (BCI)
• coordinated directions
• deep learning
• electroencephalogram (EEG)
• task-oriented bimanual movement