# Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network

## TL;DR

This study introduces an LSTM-based decoder that significantly improves the accuracy of brain-machine interfaces using local field potentials, outperforming traditional Kalman filter decoders with spike signals.

## Contribution

The paper presents a novel LSTM decoder for LFP-based BMIs, demonstrating superior decoding performance over standard Kalman filter methods with spike signals.

## Key findings

- LFP-driven LSTM decoder outperforms KF decoders with spike signals.
- LFP-based BMIs can achieve high decoding accuracy with LSTM.
- LSTM decoder offers robustness and low power advantages.

## Abstract

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.