The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface
Abstract
A brain-computer interface(BCI) based on motor imagery (MI) translates the subject's motor intention into a control signal through classifying the electroencephalogram (EEG) patterns of different imagination tasks, e.g. hand and foot movements. Characteristic EEG spatial patterns make MI tasks substantially discriminable. Multi-channel EEGs are usually necessary for spatial pattern identification and therefore MI-based BCI is still in the stage of laboratory demonstration, to some extent, due to the need for constanly troublesome recording preparation. This paper presents a method for channel reduction in MI-based BCI. Common spatial pattern (CSP) method was employed to analyze spatial patterns of imagined hand and foot movements. Significant channels were selelcted by searching the maximunms of spatial pattern vectors in scalp mappings. A classification algorithm was developed by means of combining linear discriminat analysis towards even-related desynchronization (ERD) and readiness potential (RP). The classification accuracies with four optimal channels were 93.45% and 91.88% for two subjects.
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Citations
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
Cited by
A review of channel selection algorithms for EEG signal processing
Alotaiby, T., El-Samie, F. E. A., Alshebeili, S. A., & Ahmad, I. (2015) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-015-0251-9 · Cited by 454
An end-to-end deep learning approach to MI-EEG signal classification for BCIs
Dose, H., Møller, J. S., Iversen, H. K., & Puthusserypady, S. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.08.031 · Cited by 382
Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting
Lu, H., Eng, H.-L., Guan, C., Plataniotis, K. N., & Venetsanopoulos, A. N. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2082540 · Cited by 309
Decoding Covert Speech From EEG-A Comprehensive Review
Panachakel, J. T., & Ramakrishnan, A. G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.642251 · Cited by 156
Kwon, J., Shin, J., & Im, C.-H. (2020) · PLOS ONE · doi:10.1371/journal.pone.0230491 · Cited by 53
Metadata
- Authors
- Wang, Y., Gao, S., & Gao, X.
- Venue
- 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference
- Publisher
- IEEE
- Published
- 2005
- Volume
- 2005
- Pages
- 5392-5395
- OpenAlex
- W2038120455
- Cited by
- 379
- References
- 1
- FWCI
- 4.86
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, Y., Gao, S., & Gao, X. (2005). Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. https://doi.org/10.1109/iembs.2005.1615701
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