Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain computer interface

Siuly and Li, Yan ORCID: https://orcid.org/0000-0002-4694-4926 (2012) Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20 (4). pp. 526-538. ISSN 1534-4320


Abstract

Although brain–computer interface (BCI) techniques
have been developing quickly in recent decades, there still exist a number of unsolved problems, such as improvement of motor imagery (MI) signal classi


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited: 25 Oct 2012 05:18
Last Modified: 13 Jun 2016 05:06
Uncontrolled Keywords: brain–computer interface (BCI); cross-correlation technique; electroencephalogram (EEG); feature extraction; kernel logistic regression; least square support vector machine (LS-SVM); logistic regression; motor imagery (MI)
Fields of Research (2008): 08 Information and Computing Sciences > 0806 Information Systems > 080602 Computer-Human Interaction
10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors)
09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4608 Human-centred computing > 460806 Human-computer interaction
32 BIOMEDICAL AND CLINICAL SCIENCES > 3206 Medical biotechnology > 320602 Medical biotechnology diagnostics (incl. biosensors)
40 ENGINEERING > 4003 Biomedical engineering > 400399 Biomedical engineering not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology
Identification Number or DOI: https://doi.org/10.1109/TNSRE.2012.2184838
URI: http://eprints.usq.edu.au/id/eprint/21578

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