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On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI

Gupta, Akshansh and Agrawal, Ramesh Kumar and Kirar, Jyoti Singh and Andreu-Perez, Javier and Ding, Wei-Ping and Lin, Chin-Teng and Prasad, Mukesh (2019) 'On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI.' IEEE Transactions on Systems Man and Cybernetics: Systems. ISSN 2168-2216

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Abstract

In this paper, classification of mental task-root brain-computer interfaces (BCIs) is being investigated. The mental tasks are dominant area of investigations in BCI, which utmost interest as these system can be augmented life of people having severe disabilities. The performance of BCI model primarily depends on the construction of features from brain, electroencephalography (EEG), signal, and the size of feature vector, which are obtained through multiple channels. The availability of training samples to features are minimal for mental task classification. The feature selection is used to increase the ratio for the mental task classification by getting rid of irrelevant and superfluous features. This paper suggests an approach to augment the performance of a learning algorithm for the mental task classification on the utility of power spectral density (PSD) using feature selection. This paper also deals a comparative analysis of multivariate and univariate feature selection for mental task classification. After applying the above stated method, the findings demonstrate substantial improvements in the performance of learning model for mental task classification. Moreover, the efficacy of the proposed approach is endorsed by carrying out a robust ranking algorithm and Friedman's statistical test for finding the best combinations and compare various combinations of PSD and feature selection methods.

Item Type: Article
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 15 Oct 2019 17:57
Last Modified: 15 Oct 2019 17:57
URI: http://repository.essex.ac.uk/id/eprint/25609

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