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Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning

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Ahmadi_2021_J._Neural_Eng._18_026011.pdfPublished version3.63 MBAdobe PDFView/Open
Title: Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning
Authors: Ahmadi, N
Constandinou, TG
Bouganis, C-S
Item Type: Journal Article
Abstract: Objective. Brain–machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs. Approach. We propose entire spiking activity (ESA)—an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique—as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks. Main results. Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long-term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data. Significance. Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.
Issue Date: 1-Apr-2021
Date of Acceptance: 21-Jan-2021
URI: http://hdl.handle.net/10044/1/87386
DOI: 10.1088/1741-2552/abde8a
ISSN: 1741-2552
Publisher: IOP Publishing
Start Page: 1
End Page: 23
Journal / Book Title: Journal of Neural Engineering
Volume: 18
Issue: 2
Copyright Statement: © 2021 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Neurosciences
Engineering
Neurosciences & Neurology
brain-machine interface
neural decoding
entire spiking activity
deep learning
quasi-recurrent neural network
brain-machine interface
deep learning
entire spiking activity
neural decoding
quasi-recurrent neural network
Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Neurosciences
Engineering
Neurosciences & Neurology
brain-machine interface
neural decoding
entire spiking activity
deep learning
quasi-recurrent neural network
0903 Biomedical Engineering
1103 Clinical Sciences
1109 Neurosciences
Biomedical Engineering
Publication Status: Submitted
Open Access location: https://iopscience.iop.org/article/10.1088/1741-2552/abde8a/pdf
Article Number: ARTN 026011
Online Publication Date: 2021-02-26
Appears in Collections:Electrical and Electronic Engineering



This item is licensed under a Creative Commons License Creative Commons