EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement
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Objective . In electroencephalography (EEG)-based brain–computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach . We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results . Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance . This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.
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ESI has yielded large improvements in offline neural decoding compared with traditional sensor techniques.
“Dramatic improvements in offline neural decoding have been observed when using ESI compared to traditional sensor techniques (24, 27)”
From Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019) · cites this paper as [27]
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A Neuroimaging Approach to Noninvasive Brain-Computer Interface Control
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Leung, J., Akter, M., & Chau, T. T. (2024) · Brain-Computer Interfaces · doi:10.1080/2326263x.2024.2400741
- Authors
- Shenoy Handiru, V., Vinod, A. P., & Guan, C.
- Affiliations
- Nanyang Technological University
- Nanyang Technological University (SG)
- Agency for Science, Technology and Research (SG)
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2017-05-18
- Volume
- 14
- Issue
- 4
- Pages
- 046008
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2615297171
- Cited by
- 36
- References
- 65
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 5 · 2022: 2 · 2021: 5 · 2020: 5 · 2019: 4 · 2018: 6 · 2017: 1
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- 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 › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- motor intention classification, Brodmann areas, slow cortical potentials, feature extraction, brain-computer interface, neurorehabilitation, hand kinematics
- MeSH terms
- Adult, Arm, Electroencephalography, Eye Movements, Factor Analysis, Statistical, Humans, Male, Motor Cortex, Movement, Photic Stimulation, Random Allocation, Brain Waves, Brain-Computer Interfaces
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