The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Motor imagery classification by means of source analysis for brain–computer interface applications
Open accessAbstract
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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“For motor imagery, a paradigm for brain computer interface (BCI) (71, 72), ESI enhances the capability of decoding subjects’ intent, relative to directly using sensor space signals (73, 74).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [73]
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A review of classification algorithms for EEG-based brain–computer interfaces
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665
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Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation
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LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., & He, B. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/4/046003 · Cited by 583
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Metadata
- Authors
- Qin, L., Ding, L., & He, B.
- Affiliations
- University of Minnesota (US)
- 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
- 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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