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Selection of an efficient feature space for EEG-based mental task discrimination

Paper ID Volume ID Publish Year Pages File Format Full-Text
5214 351 2014 10 PDF Available
Title
Selection of an efficient feature space for EEG-based mental task discrimination
Abstract

The aim of this paper is to contribute toward exploring an optimal feature space for discriminating mental tasks. Empirical mode decomposition (EMD) algorithm seems useful for designing such a feature space. The adjustment of nonlinear and non-stationary properties of the EEG signals with this algorithm and the successful application of this approach together biomedical signal processing problems encourage us to examine a variety of statistical and spectral measures within the EMD space as the adapted features. In this sense, as a measure of complexity, the Lempel–Ziv algorithm is utilized within the framework of the EMD algorithm. A modified form of the Lempel–Ziv complexity algorithm is then proposed. The features derived from the modified algorithm outperform the other features individually. By combining the modified Lempel–Ziv features with the other adopted features, in average, 97.78% classification accuracy is achieved for different subjects. It is concluded that the EMD–LZ kernel allows for achieving of better performances in classifying mental tasks than the results obtained with other methods.

Keywords
Mental task; Electroencephalogram signals (EEG); Empirical mode decomposition (EMD); Lempel–Ziv
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Publisher
Database: Elsevier - ScienceDirect
Journal: Biocybernetics and Biomedical Engineering - Volume 34, Issue 3, 2014, Pages 159–168
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
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