Siuly, Siuly and Li, Yan 
ORCID: https://orcid.org/0000-0002-4694-4926 and Wen, Peng (Paul) ORCID: https://orcid.org/0000-0003-0939-9145
(2014)
Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain computer interface.
Computer Methods and Programs in Biomedicine, 113 (3).
pp. 767-780.
ISSN 0169-2607
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.
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | © 2014 Elsevier Ireland Ltd. Permanent restricted access to published version due to publisher copyright policy. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Date Deposited: | 23 Apr 2014 21:32 |
| Last Modified: | 06 Feb 2018 01:07 |
| Uncontrolled Keywords: | brain computer interface (BCI); electroencephalogram (EEG); motor imagery; cross-correlation; logistic regression; feature extraction |
| Fields of Research (2008): | 09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified 08 Information and Computing Sciences > 0803 Computer Software > 080301 Bioinformatics Software 08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity |
| Fields of Research (2020): | 40 ENGINEERING > 4003 Biomedical engineering > 400399 Biomedical engineering not elsewhere classified 46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460103 Applications in life sciences 46 INFORMATION AND COMPUTING SCIENCES > 4613 Theory of computation > 461399 Theory of computation not elsewhere classified |
| Socio-Economic Objectives (2008): | C Society > 92 Health > 9202 Health and Support Services > 920203 Diagnostic Methods |
| Identification Number or DOI: | https://doi.org/10.1016/j.cmpb.2013.12.020 |
| URI: | http://eprints.usq.edu.au/id/eprint/25067 |
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