Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network
File(s)
Accepted version
Author(s)
Ahmadi, Nur
Constandinou, Timothy G
Bouganis, Christos-Savvas
Type
Conference Paper
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.
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.
Date Issued
2019-03-20
Date Acceptance
2018-12-09
Citation
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER 2019), 2019, pp.415-419
Publisher
IEEE
Start Page
415
End Page
419
Journal / Book Title
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER 2019)
Copyright Statement
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
http://arxiv.org/abs/1901.00708v1
Source
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER 2019)
Subjects
q-bio.NC
q-bio.NC
Publication Status
Published
Start Date
2019-03-20
Finish Date
2019-03-23
Coverage Spatial
San Francisco, CA, USA
Date Publish Online
2019-05-20