Improvement of stability in long-term motor decoding forelimb movement with a sequence imputation of temporal-based spike patterns

Yun Ting Kuo, Shih Hung Yang, Chin Yu Chou, Hao Cheng Chang, Kuan Yu Chen, You Yin Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Instability of neural signals is a vital issue in intracortical brain machine interface (iBMI) systems which caused by missing neuron day by day. This study proposed mean-perturbation to impute missing neural spike train during rat forelimb movement. Our results showed that the proposed mean-perturbation for sequence imputation of missing neural spikes was used to enhance the long-term decoding performance.

Original languageEnglish
Title of host publicationBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
Subtitle of host publicationIntelligent Biomedical Systems for a Better Future, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages625-629
Number of pages5
ISBN (Electronic)9781665469173
DOIs
StatePublished - 2022
Event2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 - Taipei, Taiwan
Duration: 13 Oct 202215 Oct 2022

Publication series

NameBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings

Conference

Conference2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
Country/TerritoryTaiwan
CityTaipei
Period13/10/2215/10/22

Keywords

  • brain machine interface (BMI)
  • imputation
  • missing data

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