Comparisons between motor area EEG and all-channels EEG for two algorithms in motor imagery task classification
Article
Article Title | Comparisons between motor area EEG and all-channels EEG for two algorithms in motor imagery task classification |
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ERA Journal ID | 40719 |
Article Category | Article |
Authors | Li, Yan (Author) and Wen, Peng (Paul) (Author) |
Journal Title | Biomedical Engineering: Applications, Basis and Communications |
Journal Citation | 26 (3), pp. 1-10 |
Number of Pages | 10 |
Year | 2014 |
Place of Publication | Singapore |
ISSN | 1016-2372 |
Digital Object Identifier (DOI) | https://doi.org/10.4015/S1016237214500409 |
Web Address (URL) | http://www.worldscientific.com/doi/abs/10.4015/S1016237214500409 |
Abstract | This article reports on a comparative study to identify electroencephalography (EEG) signals during motor imagery (MI) for motor area EEG and all-channels EEG in the brain–computer interface (BCI) application. In this paper, we present two algorithms: CC-LS-SVM and CC-LR for MI tasks classfication. The CC-LS-SVM algorithm combines the |
Keywords | EEG; brain-computer interface; cross-correlation; electroencephalogram; least square support vector machine; logistic regression; motor imagery |
ANZSRC Field of Research 2020 | 320602. Medical biotechnology diagnostics (incl. biosensors) |
320222. Radiology and organ imaging | |
400399. Biomedical engineering not elsewhere classified | |
Public Notes | © 2014 National Taiwan University. Accepted version deposited in accordance with the copyright policy of the publisher. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q22z6/comparisons-between-motor-area-eeg-and-all-channels-eeg-for-two-algorithms-in-motor-imagery-task-classification
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