Cited by 239EEG and Brain-Computer InterfacesMuscle activation and electromyography studiesBlind Source Separation Techniques

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Motor imagery classification by means of source analysis for brain–computer interface applications

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Qin, Lei, Ding, Lei, He, Bin·2004·Journal of Neural Engineering

DOI 10.1088/1741-2560/1/3/002PMC PMC1945182

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Abstract

We report a pilot study of performing classification of motor imagery for brain-computer interface applications, by means of source analysis of scalp-recorded EEGs. Independent component analysis (ICA) was used as a spatio-temporal filter extracting signal components relevant to left or right motor imagery (MI) tasks. Source analysis methods including equivalent dipole analysis and cortical current density imaging were applied to reconstruct equivalent neural sources corresponding to MI, and classification was performed based on the inverse solutions. The classification was considered correct if the equivalent source was found over the motor cortex in the corresponding hemisphere. A classification rate of about 80% was achieved in the human subject studied using both the equivalent dipole analysis and the cortical current density imaging analysis. The present promising results suggest that the source analysis approach could manifest a clearer picture on the cortical activity, and thus facilitate the classification of MI tasks from scalp EEGs.

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Authors: Qin, L., Ding, L., & He, B.
Affiliations: University of Minnesota (US)
ORCID: Qin, Lei
Ding, Lei
He, Bin
Venue: Journal of Neural Engineering
Publisher: IOP Publishing
Published: 2004-09-01
Volume: 1
Issue: 3
Pages: 135-141
ISSN: 1741-2552 (ISSN-L)
1741-2560
DOI: 10.1088/1741-2560/1/3/002
PMC: PMC1945182
OpenAlex: W2102176429
Cited by: 239
References: 31
Citations by year: 2026: 2 · 2025: 5 · 2024: 7 · 2023: 8 · 2022: 4 · 2021: 9 · 2020: 12 · 2019: 13 · 2018: 18 · 2017: 13 · 2016: 15 · 2015: 16 · 2014: 13 · 2013: 17 · 2012: 14
FWCI: 4.97
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Journal of Neural Engineering (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Science Foundation
University of Pennsylvania
National Institutes of Health
National Institute of Biomedical Imaging and Bioengineering (R01EB000178)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
Keywords: motor imagery classification, brain-computer interface, source analysis, scalp EEG, independent component analysis, motor cortex, hemispheric lateralization, inverse problems
MeSH terms: Algorithms, Brain Mapping, Communication Devices for People with Disabilities, Diagnosis, Computer-Assisted, Electroencephalography, Humans, Imagination, Models, Neurological, Motor Cortex, Pattern Recognition, Automated, User-Computer Interface, Evoked Potentials, Motor, Principal Component Analysis

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Motor imagery classification by means of source analysis for brain–computer interface applications

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