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

arXiv: https://arxiv.org/abs/1901.00708 · HTML: https://mlsanity.com/paper/1901.00708/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling](https://mlsanity.com/paper/2512.12461/) (2025)
- [ViF-SD2E: A Robust Weakly-Supervised Method for Neural Decoding](https://mlsanity.com/paper/2112.01261/) (2021)
- [Leveraging Recurrent Neural Networks for Predicting Motor Movements from Primate Motor Cortex Neural Recordings](https://mlsanity.com/paper/2410.22283/) (2024)
- [Deep Learning for real-time neural decoding of grasp](https://mlsanity.com/paper/2311.01061/) (2023)
- [An Algorithm Board in Neural Decoding](https://mlsanity.com/paper/2502.12536/) (2025)
- [REALM: Retrospective Encoder Alignment for LFP Modeling](https://mlsanity.com/paper/2605.14867/) (2026)

## Abstract (arXiv)

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.