Abstract
Brain-computer interface (BCI) is a direct communication pathway between brain and external devices, and BCI-based prosthetic devices are promising to provide new rehabilitation options for people with motor disabilities. Electrocorticography (ECoG) signals contain rich information correlated with motor activities, and have great potential in hand gesture decoding. However, most existing decoders use long time windows, thus ignore the temporal dynamics within the period. In this study, we propose to use recurrent neural networks (RNNs) to exploit the temporal information in ECoG signals for robust hand gesture decoding. With RNN's high nonlinearity modeling ability, our method can effectively capture the temporal information in ECoG time series for robust gesture recognition. In the experiments, we decode three hand gestures using ECoG signals of two participants, and achieve an accuracy of 90%. Specially, we investigate the possibility of recognizing the gestures in a time interval as short as possible after motion onsets. Our method rapidly recognizes gestures within 0.5 s after motion onsets with an accuracy of about 80%. Experimental results also indicate that the temporal dynamics is especially informative for effective and rapid decoding of hand gestures.
Highlights
Brain-computer interface (BCI) is a direct communication pathway between brain and external devices (Wolpaw et al, 2002)
We test the recurrent neural networks (RNNs) model with different settings to select the optimal parameters for gesture decoding
We proposed a RNN-based method to exploit the temporal information in ECoG signals for rapid and robust gesture recognition
Summary
Brain-computer interface (BCI) is a direct communication pathway between brain and external devices (Wolpaw et al, 2002). BCI systems do not depend on peripheral nerves and muscles, and have great potential to provide new rehabilitation options to patients with motor disabilities (Daly and Wolpaw, 2008), toward the big vision of cyborg intelligence (Wu et al, 2013, 2016; Yu et al, 2016). Electrocorticography (ECoG)-based BCI systems, i.e., the semi-invasive BCIs, have better long-term stability than invasive BCIs (Pilcher and Rusyniak, 1993), neural spikes (Qian et al, 2018; Xing et al, 2018) have high temporal resolution, and contains richer information than traditional non-invasive BCIs, such as EEG (Blankertz et al, 2004; Sun et al, 2016), have been considered as an ideal option for applications such as neural prosthesis control (Leuthardt et al, 2004; Schalk et al, 2008).
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