Improved Spike-based Brain-Machine Interface Using Bayesian Adaptive
Kernel Smoother and Deep Learning
Abstract
Multiunit activity (MUA) has been proposed to mitigate the robustness
issue faced by single-unit activity (SUA)-based brain-machine interfaces
(BMIs). Most MUA-based BMIs still employ a binning method for estimating
firing rates and linear decoder for decoding behavioural parameters. The
limitations of binning and linear decoder lead to suboptimal performance
of MUA-based BMIs. To address this issue, we propose a method which
consists of Bayesian adaptive kernel smoother (BAKS) as the firing rate
estimation algorithm and deep learning, particularly quasi-recurrent
neural network (QRNN), as the decoding algorithm. We evaluated the
proposed method for reconstructing (offline) hand kinematics from
intracortical neural data chronically recorded from the primary motor
cortex of two non-human primates. Extensive empirical results across
recording sessions and subjects showed that the proposed method
consistently outperforms other combinations of firing rate estimation
algorithm and decoding algorithm. Overall results suggest the
effectiveness of the proposed method for improving the decoding
performance of MUA-based BMIs.