EEG source imaging enhances the decoding of complex right-hand motor imagery tasks
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GOAL: Sensorimotor-based brain-computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device. METHODS: We extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation. RESULTS: We report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method. CONCLUSION: ESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks. SIGNIFICANCE: This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.
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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 [24]
For motor imagery, electrophysiological source imaging enhances the capability of decoding subjects' intent relative to directly using sensor space signals.
“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 [74]
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- Authors
- Edelman, B. J., Baxter, B., & He, B.
- Affiliations
- University of Minnesota (US)
- Corresponding author
- He, Bin
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2016-01
- Volume
- 63
- Issue
- 1
- Pages
- 4-14
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- PMC
- PMC4716869
- OpenAlex
- W2197723530
- Cited by
- 370
- References
- 69
- Citations by year
- 2026: 19 · 2025: 30 · 2024: 45 · 2023: 40 · 2022: 27 · 2021: 38 · 2020: 29 · 2019: 39 · 2018: 52 · 2017: 38 · 2016: 12 · 2015: 1
- FWCI
- 18.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (CBET-1264782, IGERT DGE-1069104)
- National Institutes of Health (R01 EB00643)
- 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
- EEG source localization, brain-computer interface, motor imagery, wrist flexion and extension, pronation and supination
- MeSH terms
- Adolescent, Adult, Brain Mapping, Electroencephalography, Female, Hand, Humans, Imagination, Male, Young Adult, Brain-Computer Interfaces
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