The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces
Abstract
Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.
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Cites
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
Brain-Computer Interfaces Based on Visual Evoked Potentials
Wang, Y., Gao, X., Hong, B., Jia, C., & Gao, S. (2008) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2008.923958 · Cited by 423
Self-citation
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Visual and Auditory Brain–Computer Interfaces
Gao, S., Wang, Y., Gao, X., & Hong, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2300164 · Cited by 449
Self-citationProgress in Brain Computer Interface: Challenges and Opportunities
Saha, S., Mamun, K. A., Ahmed, K., Mostafa, R., Naik, G. R., Darvishi, S., Khandoker, A. H., & Baumert, M. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.578875 · Cited by 365
An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals
Du, X., Ma, C., Zhang, G., Li, J., Lai, Y.-K., Zhao, G., Deng, X., Liu, Y.-J., & Wang, H. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3013711 · Cited by 357
Automagic: Standardized preprocessing of big EEG data
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Metadata
- Authors
- Chi, Y. M., Wang, Y.-T., Wang, Y., Maier, C., Jung, T.-P., & Cauwenberghs, G.
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2012-03
- Volume
- 20
- Issue
- 2
- Pages
- 228-235
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2142239365
- Cited by
- 297
- References
- 2
- FWCI
- 9.20
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chi, Y. M., Wang, Y.-T., Wang, Y., Maier, C., Jung, T.-P., & Cauwenberghs, G. (2012). Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/tnsre.2011.2174652
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