A new design of mental state classification for subject independent BCI systems

Joadder, Md. A. M. and Siuly, S. and Kabir, E. and Wang, H. and Zhang, Y. (2019) A new design of mental state classification for subject independent BCI systems. IRBM.

Abstract

Background: Brain Computer Interface (BCI) systems have been widely used to develop sustainable assistive technology for people suffering from neurological impairments. A major limitation of current BCI systems is that they are based on Subject-dependent (SD) concept. The SD based BCI system is time consuming and inconvenient for physical or mental disables people and also not suitable for limited computer resources. In order to overcome these problems, recently subject-independent (SI) based BCI concept has been introduced to identify mental states of motor disabled people but the expected outcome of the SI based BCI has not been achieved yet. Hence this paper intends to present an efficient scheme for SI based BCI system. The goal of this research is to develop a method for classifying mental states which can be used by any user. For attaining this target, this study employs a supervised spatial filtering method with four types of feature extraction methods including Katz Fractal Dimension, Sub band Energy, Log Variance and Root Mean Square (RMS) and finally the obtained features are used as input to Linear Discriminant Analysis (LDA) classification model for identifying mental states for SI BCI system.

Results: The performance of the proposed design is evaluated in several ways such as considering different time window length; different frequency bands; different number of channels. The mean classification accuracy using Katz feature is 84.35% which is the maximum output compare to other features that outperforms the existing methods.

Conclusions: Our proposed design will help to make a new technology for development of real-time SI based BCI systems that can be more supportive for the motor disabled patients.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online: 11 June 2019. Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 July 2013 - 5 Sept 2019)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 July 2013 - 5 Sept 2019)
Date Deposited: 05 Sep 2019 01:43
Last Modified: 26 Sep 2019 04:43
Uncontrolled Keywords: brain-computer interface, common spatial pattern, electroencephalography, EEG, motor imagery, subject independent
Fields of Research : 09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
Identification Number or DOI: 10.1016/j.irbm.2019.05.004
URI: http://eprints.usq.edu.au/id/eprint/36979

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