2019/01/03 by Nur Ahmadi, Timothy G. Constandinou, Ahmadi, Nur +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Engineering · Neuroscience · #Advanced Memory and Neural Computing #EEG and Brain-Computer Interfaces #FOS: Biological sciences #Neurons and Cognition (q-bio.NC) #Neuroscience and Neural Engineering #q-bio.NC
paper · pdf · doi:10.48550/arxiv.1901.00708
Accepted for the 9th International IEEE EMBS Conference on Neural Engineering (NER 2019)
arxiv created 2019/01/03 · openalex publication_date 2019/01/03 · arxiv updated 2019/01/04 · openalex created_date 2022/07/30 · openalex updated_date 2026/07/28
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.