Research Article | OPEN ACCESS
Pattern Classification of EEG Signals on Different States of Cognition Using Linear and Nonlinear Classifiers
1R. Kalpana, 2M. Chitra and 3K. Vijayalakshmi
1Anna University, Chennai,
2Department of Information Technology, Sona College of Technology, Salem
3Department of Medical Electronics, BMS College of Engineering, Bangalore, India
Research Journal of Applied Sciences, Engineering and Technology 2015 6:623-629
Received: April ‎17, ‎2015 | Accepted: June ‎14, ‎2015 | Published: October 25, 2015
Abstract
There is a need to analyze and interpret the EEG data obtained from the brain which has its importance in various fields and applications. In this study we have acquired the EEG signal from the subjects while performing different tasks and then use pattern classification to differentiate the various tasks. Artifacts in the EEG signal are removed in the preprocessing stage. Features extracted from EEG datasets of various subjects were used as input to the neural network for training, validation and classification. Nearest neighbor and feed forward Neural Networks were used for classification and their results were compared.
Keywords:
Classification, EEG, feed forward neural networks, nearest neighbour classifier, pattern classification,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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