Inter-muscular coherence features to classify upper limb simple tasks

E. Colamarino, J. Toppi, V. De Seta, F. Cincotti, F. Pichiorri, M. Masciullo, D. Mattia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The application of Hybrid Brain-Computer Interfaces (BCI) for post-stroke hand motor rehabilitation requires the investigation of new electromyographic (EMG) features, potentially able to identify pathological synergies to be discouraged. Inter-muscular coherence (IMC) is gaining attention as a descriptor of the mechanisms behind abnormal motor control in stroke patients. With the ultimate goal to exploit IMC features to control BCIs, this work aims at (a) characterizing finger extension and grasping tasks by IMC features, (b) assessing IMC feature performance in classifying different conditions. Classification results (accuracy equal to 0.81 ± 0.19) pave the way for IMC feature application in hybrid BCI control.

Original languageEnglish
Title of host publication2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
PublisherIEEE Computer Society
Pages57-60
Number of pages4
ISBN (Electronic)9781728143378
DOIs
Publication statusPublished - May 4 2021
Event10th International IEEE/EMBS Conference on Neural Engineering, NER 2021 - Virtual, Online, Italy
Duration: May 4 2021May 6 2021

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2021-May
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Country/TerritoryItaly
CityVirtual, Online
Period5/4/215/6/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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