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Current Source Density Estimation Enhances the Performance of Motor-Imagery-Related Brain–Computer Interface

Rathee, Dheeraj and Raza, Haider and Prasad, Girijesh and Cecotti, Hubert (2017) 'Current Source Density Estimation Enhances the Performance of Motor-Imagery-Related Brain–Computer Interface.' IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (12). pp. 2461-2471. ISSN 1534-4320

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The objective is to evaluate the impact of EEG referencing schemes and spherical surface Laplacian (SSL) methods on the classification performance of motor-imagery (MI)-related brain-computer interface systems. Two EEG referencing schemes: common referencing and common average referencing and three surface Laplacian methods: current source density (CSD), finite difference method, and SSL using realistic head model were implemented separately for pre-processing of the EEG signals recorded at the scalp. A combination of filter bank common spatial filter for features extraction and support vector machine for classification was used for both pairwise binary classifications and four-class classification of MI tasks. The study provides three major outcomes: 1) the CSD method performs better than CR, providing a significant improvement of 3.02% and 5.59% across six binary classification tasks and four-class classification task, respectively; 2) the combination of a greater number of channels at the pre-processing stage as compared with the feature extraction stage yields better classification accuracies for all the Laplacian methods; and 3) the efficiency of all the surface Laplacian methods reduced significantly in the case of a fewer number of channels considered during the pre-processing.

Item Type: Article
Uncontrolled Keywords: Motor imagery; brain-computer interface; pre-processing; spatial filtering; spherical surface Laplacian
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 11 Sep 2018 13:39
Last Modified: 15 Jan 2022 01:21

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