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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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Cites
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,324
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Analysis of Discrete Ill-Posed Problems by Means of the L-Curve
Hansen, P. C. (1992) · SIAM Review · doi:10.1137/1034115 · Cited by 3,903
A Fast Fixed-Point Algorithm for Independent Component Analysis
Hyvärinen, A., & Erkki Oja, A. (1997) · Neural Computation · doi:10.1162/neco.1997.9.7.1483 · Cited by 3,373
Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture
Jutten, C., & Hérault, J. (1991) · Signal Processing · doi:10.1016/0165-1684(91)90079-x · Cited by 2,550
Dale, A. M., & Sereno, M. I. (1993) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1993.5.2.162 · Cited by 2,003
A spelling device for the paralysed
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., & Flor, H. (1999) · Nature · doi:10.1038/18581 · Cited by 1,577
An EEG-based brain-computer interface for cursor control
Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(91)90040-b · Cited by 1,117
An on-line transformation of EEG scalp potentials into orthogonal source derivations
Hjorth, B. (1975) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(75)90056-5 · Cited by 1,014
Spatial filter selection for EEG-based communication
McFarland, D. J., McCane, L. M., David, S. V., & Wolpaw, J. R. (1997) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(97)00022-2 · Cited by 937
Research works printed but not linked: 4
- He B, 1999, Crit. Rev. Biomed. Eng., 27, 149
- He B, 2004, Modeling and Imaging of Bioelectric Activity-Principles and Applications
- He B, 2004, Neural Eng.
- Osman A Robert A, 2001, Cognit. Neurosci. Ann. Meeting
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McFarland, D. J., Anderson, C. W., Muller, K.-R., Schlogl, A., & Krusienski, D. J. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875637 · Cited by 204
Lotte, F. (2014) · Springer eBooks · doi:10.1007/978-1-4471-6584-2_7 · Cited by 190
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Ding, L., Worrell, G. A., Lagerlund, T. D., & He, B. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.042 · Cited by 183
Self-citationShim, M., Hwang, H., Kim, D.-W., Lee, S., & Im, C. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.05.007 · Cited by 181
Vallabhaneni, A., Wang, T., & He, B. (2007) · Springer eBooks · doi:10.1007/0-306-48610-5_3 · Cited by 188
Self-citationWu, W., Gao, X., Hong, B., & Gao, S. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.919125 · Cited by 180
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Edelman, B. J., Zhang, S., Schalk, G., Brunner, P., Müller-Putz, G., Guan, C., & He, B. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3449790 · Cited by 212
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Makeig, S., Kothe, C. A., Mullen, T. R., Bigdely-Shamlo, N., Zhang, Z., & Kreutz-Delgado, K. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2185009 · Cited by 164
Luo, T.-J., Zhou, C., & Chao, F. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2365-1 · Cited by 162
Qin, L., & He, B. (2005) · Journal of Neural Engineering · doi:10.1088/1741-2560/2/4/001 · Cited by 154
Self-citationSadiq, M. T., Yu, X., Yuan, Z., Fan, Z., Rehman, A. U., Li, G., & Xiao, G. (2019) · IEEE Access · doi:10.1109/access.2019.2939623 · Cited by 153
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Zhang, H., Chin, Z. Y., Ang, K. K., Guan, C., & Wang, C. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2084099 · Cited by 75
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Noirhomme, Q., Kitney, R. I., & Macq, B. M. M. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.913986 · Cited by 75
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Zhou, B., Wu, X., Lv, Z., Zhang, L., & Guo, X. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162657 · Cited by 68
Kamousi, B., Amini, A. N., & He, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/002 · Cited by 68
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Ding, L., Wilke, C. T., Xu, B., Xu, X., van Drongelen, W., Kohrman, M. H., & He, B. (2007) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e318038fd52 · Cited by 46
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Alazrai, R., Alwanni, H., Baslan, Y., Alnuman, N., & Daoud, M. I. (2017) · Sensors · doi:10.3390/s17091937 · Cited by 46
Belkacem, A. N., Nishio, S., Suzuki, T., Ishiguro, H., & Hirata, M. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2837003 · Cited by 45
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Baxter, B., Edelman, B. J., Sohrabpour, A., & He, B. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00691 · Cited by 41
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Deng, J., Yao, J., & Dewald, J. P. A. (2005) · Journal of Neural Engineering · doi:10.1088/1741-2560/2/4/009 · Cited by 38
Besserve, M., Martinerie, J. M., & Garnero, L. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.056 · Cited by 37
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Wronkiewicz, M., Larson, E. B., & Lee, A. K. C. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/4/046027 · Cited by 36
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EEG Source Imaging of Movement Decoding: The State of the Art and Future Directions
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Baxter, B., Edelman, B. J., Nesbitt, N., & He, B. (2016) · Brain stimulation · doi:10.1016/j.brs.2016.07.003 · Cited by 35
Self-citationWang, K., Wang, Z., Guo, Y., He, F., Qi, H., Xu, M., & Ming, D. (2017) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-017-0307-1 · Cited by 36
Feasibility of approaches combining sensor and source features in brain–computer interface
Ahn, M., Hong, J. H., & Jun, S. C. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.11.002 · Cited by 33
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Forenzo, D., Zhu, H., Shanahan, J., Lim, J., & He, B. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae145 · Cited by 36
Self-citationKhan, Y. U., & Sepulveda, F. (2010) · IET Signal Processing · doi:10.1049/iet-spr.2008.0235 · Cited by 32
Deshpande, G., Rangaprakash, D., Oeding, L., Cichocki, A., & Hu, X. P. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00246 · Cited by 29
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Delisle-Rodríguez, D., Villa-Parra, A. C., Bastos-Filho, T., López-Delis, A., Frizera, A., Krishnan, S., & Rocón, E. (2017) · Sensors · doi:10.3390/s17122725 · Cited by 29
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Grandchamp, R., Braboszcz, C., Makeig, S., & Arnaud Delorme, A. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347540 · Cited by 25
Afrakhteh, S., & Mosavi, M. R. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819045-6.00002-9 · Cited by 27
STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging
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Rembado, I., Castagnola, E., Turella, L., Ius, T., Budai, R., Ansaldo, A., Angotzi, G. N., de Bertoldi, F., Ricci, D., Škrap, M., et al. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065716500520 · Cited by 22
Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury
Athanasiou, A., Terzopoulos, N., Pandria, N., Xygonakis, I., Foroglou, N., Polyzoidis, K. S., & Bamidis, P. D. (2018) · Neural Plasticity · doi:10.1155/2018/9354207 · Cited by 21
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Georgieva, P., da Silva, F. M. T. P., Milanova, M. G., & Kasabov, N. K. (2013) · Springer eBooks · doi:10.1007/978-3-642-30574-0_46 · Cited by 20
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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