Decoding Coordinated Directions of Bimanual Movements From EEG Signals
| Autor: | Mingming Zhang, Junde Wu, Jongbin Song, Ruiqi Fu, Rui Ma, Yi-Chuan Jiang, Yi-Feng Chen |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 248-259 (2023) |
| Druh dokumentu: | article |
| ISSN: | 1558-0210 44114338 |
| DOI: | 10.1109/TNSRE.2022.3220884 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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