  Local field potential (LFP) has gained increasing interest as an alternative
input signal for brain-machine interfaces (BMIs) due to its informative
features, long-term stability, and low frequency content. However, despite
these interesting properties, LFP-based BMIs have been reported to yield low
decoding performances compared to spike-based BMIs. In this paper, we propose a
new decoder based on long short-term memory (LSTM) network which aims to
improve the decoding performance of LFP-based BMIs. We compare offline decoding
performance of the proposed LSTM decoder to a commonly used Kalman filter (KF)
decoder on hand kinematics prediction tasks from multichannel LFPs. We also
benchmark the performance of LFP-driven LSTM decoder against KF decoder driven
by two types of spike signals: single-unit activity (SUA) and multi-unit
activity (MUA). Our results show that LFP-driven LSTM decoder achieves
significantly better decoding performance than LFP-, SUA-, and MUA-driven KF
decoders. This suggests that LFPs coupled with LSTM decoder could provide high
decoding performance, robust, and low power BMIs.
