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

Pramod Gaur, R. B. Pachori, Hui Wang, Girijesh Prasad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages1-7
Number of pages7
DOIs
Publication statusPublished - 12 Jul 2015
Event2015 International Joint Conference on Neural Networks (IJCNN) - Killarne, Ireland.
Duration: 12 Jul 2015 → …

Conference

Conference2015 International Joint Conference on Neural Networks (IJCNN)
Period12/07/15 → …

Fingerprint

Brain computer interface
Electroencephalography
Decomposition
Discriminant analysis
Feature extraction

Keywords

  • Brain-computer interface (BCI)
  • Hjorth and band power features
  • empirical mode decomposition (EMD)
  • linear discriminant analysis (LDA) classifier.

Cite this

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title = "An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface",
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.",
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Gaur, P, Pachori, RB, Wang, H & Prasad, G 2015, An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface. in Unknown Host Publication. pp. 1-7, 2015 International Joint Conference on Neural Networks (IJCNN), 12/07/15. https://doi.org/10.1109/IJCNN.2015.7280754

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; Prasad, Girijesh.

Unknown Host Publication. 2015. p. 1-7.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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