Rapid Decoding of Hand Gestures in Electrocorticography Using Recurrent Neural Networks.

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 experi

SUBMITTER: Pan G 

PROVIDER: S-EPMC6119703 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature