International Journal of Computational Intelligence Systems

Volume 10, Issue 1, 2017, Pages 1280 - 1288

Analysis of Time – Frequency EEG Feature Extraction Methods for Mental Task Classification

Authors
Caglar Uyulan1, cuyulan@itu.edu.tr, Turker Tekin Erguzel2, *, turker.erguzel@uskudar.edu.tr
1Mechatronics Engineering, Istanbul Technical University, Istanbul, Turkey
2Computer Engineering, Uskudar University, Istanbul, Turkey
*Corresponding author: turker.erguzel@uskudar.edu.tr, 90 216 400 22 22, AltunizadeMah. HalukTurksoy Sk. No:14, 34662 Uskudar, Istanbul, Turkey.
Corresponding Author
Turker Tekin Erguzelturker.erguzel@uskudar.edu.tr
Received 8 February 2017, Accepted 11 July 2017, Available Online 26 July 2017.
DOI
https://doi.org/10.2991/ijcis.10.1.87How to use a DOI?
Keywords
Feature extraction, time-frequency EEG analysis, task classification, artificial intelligence
Abstract

Many endogenous and external components may affect the physiological, mental and behavioral states in humans. Monitoring tools are required to evaluate biomarkers, identify biological events, and predict their outcomes. Being one of the valuable indicators, brain biomarkers derived from temporal or spectral electroencephalography (EEG) signals processing, allow for the classification of mental disorders and mental tasks. An EEG signal has a non-stationary nature and individual frequency feature, hence it can be concluded that each subject has peculiar timing and data to extract unique features. In order to classify data, which are collected by performing four mental task (reciting the alphabet backwards, imagination of rotation of a cube, imagination of right hand movements (open/close) and performing mathematical operations), discriminative features were extracted using four competitive time-frequency techniques; Wavelet Packet Decomposition (WPD), Morlet Wavelet Transform (MWT), Short Time Fourier Transform (STFT) and Wavelet Filter Bank (WFB), respectively. The extracted features using both time and frequency domain information were then reduced using a principal component analysis for subset reduction. Finally, the reduced subsets were fed into a multi-layer perceptron neural network (MP-NN) trained with back propagation (BP) algorithm to generate a predictive model. This study mainly focuses on comparing the relative performance of time-frequency feature extraction methods that are used to classify mental tasks. The real-time (RT) conducted experimental results underlined that the WPD feature extraction method outperforms with 92% classification accuracy compared to three other aforementioned methods for four different mental tasks.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
10 - 1
Pages
1280 - 1288
Publication Date
2017/07
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.10.1.87How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Caglar Uyulan
AU  - Turker Tekin Erguzel
PY  - 2017
DA  - 2017/07
TI  - Analysis of Time – Frequency EEG Feature Extraction Methods for Mental Task Classification
JO  - International Journal of Computational Intelligence Systems
SP  - 1280
EP  - 1288
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.10.1.87
DO  - https://doi.org/10.2991/ijcis.10.1.87
ID  - Uyulan2017
ER  -