Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery‐Based Brain–Machine Interfaces
Open accessAbstract
Motor imagery offers an excellent opportunity as a stimulus‐free paradigm for brain–machine interfaces. Conventional electroencephalography (EEG) for motor imagery requires a hair cap with multiple wired electrodes and messy gels, causing motion artifacts. Here, a wireless scalp electronic system with virtual reality for real‐time, continuous classification of motor imagery brain signals is introduced. This low‐profile, portable system integrates imperceptible microneedle electrodes and soft wireless circuits. Virtual reality addresses subject variance in detectable EEG response to motor imagery by providing clear, consistent visuals and instant biofeedback. The wearable soft system offers advantageous contact surface area and reduced electrode impedance density, resulting in significantly enhanced EEG signals and classification accuracy. The combination with convolutional neural network‐machine learning provides a real‐time, continuous motor imagery‐based brain–machine interface. With four human subjects, the scalp electronic system offers a high classification accuracy (93.22 ± 1.33% for four classes), allowing wireless, real‐time control of a virtual reality game.
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The most popular BMI approaches use EEG signals, the external manifestation of the brain's endogenous EM field, as the interface between brain and machine motor outputs, effectively implementing the same informational loop from EM field-encoded neuronal information to motor actions as proposed in the cemi field theory.
“The most popular BMI approaches utilize EEG signals-the external manifestation of the brain's endogenous EM field-as the interface between the brain and machine motor outputs [81], effectively implementing the same informational loop from EM field-encoded neuronal information to motor actions as is proposed in the cemi field theory.”
From The electromagnetic will (2021) · cites this paper as [81]
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- Authors
- Mahmood, M., Kwon, S., Kim, H., Kim, Y., Siriaraya, P., Choi, J., Otkhmezuri, B., Kang, K., Yu, K. J., Jang, Y. C., Ang, C. S., & Yeo, W.
- Affiliations
- Georgia Institute of Technology (US)
- University of Kent (GB)
- Yonsei University (KR)
- Corresponding author
- Yeo, Woon‐Hong
- ORCID
- Venue
- Advanced Science
- Publisher
- Wiley
- Published
- 2021-07-17
- Volume
- 8
- Issue
- 19
- Pages
- e2101129-e2101129
- ISSN
- 2198-3844 (ISSN-L)
- PMC
- PMC8498913
- OpenAlex
- W3167202027
- Cited by
- 78
- References
- 37
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 16 · 2023: 14 · 2022: 12 · 2021: 5
- FWCI
- 6.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Advanced Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Kent Academic Repository (University of Kent) (submittedVersion)
- Funders
- National Institutes of Health (NIH R21AG064309)
- National Research Foundation of Korea (NRF‐2018M3A7B4071109, NRF‐2019R1A2C2086085)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- motor imagery, brain-computer interface, soft electronics, microneedle electrodes, virtual reality, EEG classification, wearable EEG, biofeedback, convolutional neural networks, motion artifacts
- MeSH terms
- Virtual Reality, Brain, Electrodes, Electroencephalography, Humans, Scalp, User-Computer Interface, Brain-Computer Interfaces
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