Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain–computer interface
Article
Article Title | Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain–computer interface |
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ERA Journal ID | 5039 |
Article Category | Article |
Authors | Siuly, Siuly (Author), Li, Yan (Author) and Wen, Peng (Paul) (Author) |
Journal Title | Computer Methods and Programs in Biomedicine |
Journal Citation | 113 (3), pp. 767-780 |
Number of Pages | 14 |
Year | 2014 |
Publisher | Elsevier |
Place of Publication | Ireland |
ISSN | 0169-2607 |
1872-7565 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cmpb.2013.12.020 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0169260713004100 |
Abstract | Motor imagery (MI) tasks classification provides an important basis for designing brain computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested. |
Keywords | brain computer interface (BCI); electroencephalogram (EEG); motor imagery; cross-correlation; logistic regression; feature extraction |
ANZSRC Field of Research 2020 | 400399. Biomedical engineering not elsewhere classified |
460103. Applications in life sciences | |
461399. Theory of computation not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Faculty of Health, Engineering and Sciences |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q25wv/modified-cc-lr-algorithm-with-three-diverse-feature-sets-for-motor-imagery-tasks-classification-in-eeg-based-brain-computer-interface
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