mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Discriminating EEG spectral power related to mental imagery of closing and opening of hand
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-8174-1067
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
2019 (English)In: 2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), IEEE , 2019, p. 307-310Conference paper, Published paper (Refereed)
Abstract [en]

ElectroEncephaloGram (EEG) spectral power has been extensively used to classify Mental Imagery (MI) of movements involving different body parts. However, there is an increasing need to enable classification of MI of movements within the same limb. In this work, EEG spectral power was recorded in seven subjects while they performed MI of closing (grip) and opening (extension of fingers) the hand. The EEG data was analyzed and the feasibility of classifying MI of the two movements were investigated using two different classification algorithms, a linear regression and a Convolutional Neural Network (CNN). Results show that only the CNN is able to significantly classify MI of opening and closing of the hand with an average classification accuracy of 60.4%. This indicates the presence of higher-order non-linear discriminatory information and demonstrates the potential of using CNN in classifying MI of same-limb movements.

Place, publisher, year, edition, pages
IEEE , 2019. p. 307-310
Series
International IEEE EMBS Conference on Neural Engineering, ISSN 1948-3546
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-44330ISI: 000469933200077Scopus ID: 2-s2.0-85066765799ISBN: 978-1-5386-7921-0 (print)OAI: oai:DiVA.org:mdh-44330DiVA, id: diva2:1327854
Conference
9th IEEE/EMBS International Conference on Neural Engineering (NER), MAR 20-23, 2019, San Francisco, CA
Available from: 2019-06-20 Created: 2019-06-20 Last updated: 2019-06-20Bibliographically approved

Open Access in DiVA

No full text in DiVA

Scopus

Authority records BETA

Tidare, JonatanLeon, MiguelXiong, NingÅstrand, Elaine

Search in DiVA

By author/editor
Tidare, JonatanLeon, MiguelXiong, NingÅstrand, Elaine
By organisation
Embedded Systems
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 1 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf