Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

Wavelet Packet Sub-band Based Classification of Alcoholic and Controlled State EEG Signals

Authors
D. Puri, R. Ingle, P. Kachare, M. Patil, R. Awale
Corresponding Author
D. Puri
Available Online December 2016.
DOI
https://doi.org/10.2991/iccasp-16.2017.82How to use a DOI?
Keywords
Electroencephalogram, wavelet packet transform, sub-band decomposition, support vector machine.
Abstract
Electro-encephalogram (EEG) is one of the most practiced signals in brain computer interface systems. Several distinct EEG patterns have been analyzed in identifying physiological and psychological states. Work presented here focuses on classification of EEG patterns for alcoholic and controlled states. Third level sub-band energy features are generated for either classes using multi-resolution wavelet packet transformation. A well-known support vector classifier is employed to segregate these features in two well defined classes. Experimental results show significant improvement over wavelet tree feature extraction. Cross-validation tests confirm the greater classification accuracy for proposed technique.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
International Conference on Communication and Signal Processing 2016 (ICCASP 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2016
ISBN
978-94-6252-305-0
ISSN
1951-6851
DOI
https://doi.org/10.2991/iccasp-16.2017.82How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - D. Puri
AU  - R. Ingle
AU  - P. Kachare
AU  - M. Patil
AU  - R. Awale
PY  - 2016/12
DA  - 2016/12
TI  - Wavelet Packet Sub-band Based Classification of Alcoholic and Controlled State EEG Signals
BT  - International Conference on Communication and Signal Processing 2016 (ICCASP 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasp-16.2017.82
DO  - https://doi.org/10.2991/iccasp-16.2017.82
ID  - Puri2016/12
ER  -