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An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface

Gaur, Pramod, Pachori, R. B., Wang, Hui and Prasad, Girijesh (2015) An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface. In: 2015 International Joint Conference on Neural Networks (IJCNN), Killarne, Ireland.. IEEE. 7 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7280754&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7280754

DOI: 10.1109/IJCNN.2015.7280754

Abstract

In this paper, we present a new filtering method based on the empirical mode decomposition (EMD) for classification of motor imagery (MI) electroencephalogram (EEG) signals for enhancing brain-computer interface (BCI). The EMD method decomposes EEG signals into a set of intrinsic mode functions (IMFs). These IMFs can be considered narrow-band, amplitude and frequency modulated (AM-FM) signals. The mean frequency measure of these IMFs has been used to combine these IMFs in order to obtain the enhanced EEG signals which have major contributions due to μ and β rhythms. The main aim of the proposed method is to filter EEG signals before feature extraction and classification to enhance the features separability and ultimately the BCI task classification performance. The features namely, Hjorth and band power features computed from the enhanced EEG signals, have been used as a feature set for classification of left hand and right hand MIs using a linear discriminant analysis (LDA) based classification method. Significant superior performance is obtained when the method is tested on the BCI competition IV datasets, which demonstrates the effectiveness of the proposed method.

Item Type:Conference contribution (Paper)
Keywords:Brain-computer interface (BCI), Hjorth and band power features, empirical mode decomposition (EMD), linear discriminant analysis (LDA) classifier.
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:34475
Deposited By: Professor Girijesh Prasad
Deposited On:18 Apr 2016 13:10
Last Modified:18 Apr 2016 13:10

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