Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning

Robustness and decoding accuracy remain major challenges in the clinical translation of intracortical brain-machine interface (BMI) systems. In this work, we show that a signal/decoder co-design methodology (exploiting the synergism between the input signal and decoding algorithm within the design development process) can be used to yield robust and accurate BMI decoding performance. Specifically, through applying this process, we propose the combination of using entire spiking activity (ESA) as the input signal and quasi-recurrent neural network (QRNN) based deep learning as the decoding algorithm. We evaluated the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of a non-human primate. Our proposed method yielded consistently higher decoding performance than any other methods previously reported across long-term recording sessions. Its high decoding performance could sustain, even when spikes were removed from the raw signals. Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.