Investigating the effects of macaque primary motor cortex multi-unit activity binning period on behavioural decoding performance
Author(s)
Savolainen, Oscar W
Constandinou, Timothy G
Type
Conference Paper
Abstract
This paper investigates the relationship between Multi-Unit Activity (MUA) Binning Period (BP) and Brain-Computer Interface (BCI) decoding performance using Long-Short Term Memory decoders. The motivation is to determine whether lossy compression of MUA via increasing BP has any adverse consequences for BCI Behavioral Decoding Performance (BDP). The Neural data originates from intracortical recordings from Macaque Primary Motor cortex. The BDP is measured by the Pearson correlation r between the observed and predicted velocity of the subject's X- Y hand coordinates in reaching tasks. The results suggest a statistically significant but slight linear relationship between increasing MUA BP and decreasing BDP. For example, when using a 100 ms moving average window, increasing the BP by 10 ms on average reduces the BDP r by approximately 0.85%. This relationship may be due to the reduced number of training examples, or due to the loss of Behavioral information because of reduced MUA temporal resolution.
Date Issued
2021-05-06
Date Acceptance
2021-02-01
Citation
2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), 2021, pp.436-439
ISSN
1948-3546
Publisher
IEEE
Start Page
436
End Page
439
Journal / Book Title
2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)
Copyright Statement
© 2021 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://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000681358200088&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
10th International IEEE-EMBS Conference on Neural Engineering (NER)
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Theory & Methods
Engineering, Biomedical
Neurosciences
Computer Science
Engineering
Neurosciences & Neurology
Publication Status
Published
Start Date
2021-05-04
Finish Date
2021-05-06
Coverage Spatial
Prague, ELECTR NETWORK
Date Publish Online
2021-05-06