  Brain-machine interfaces (BMIs) are promising for motor rehabilitation and
mobility augmentation. High-accuracy and low-power algorithms are required to
achieve implantable BMI systems. In this paper, we propose a novel spiking
neural network (SNN) decoder for implantable BMI regression tasks. The SNN is
trained with enhanced spatio-temporal backpropagation to fully leverage its
ability in handling temporal problems. The proposed SNN decoder achieves the
same level of correlation coefficient as the state-of-the-art ANN decoder in
offline finger velocity decoding tasks, while it requires only 6.8% of the
computation operations and 9.4% of the memory access.
