NDLI: A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals

Content Provider	Springer Nature Link
Author	Amin, Hafeez Ullah Malik, Aamir Saeed Kamel, Nidal Hussain, Muhammad
Copyright Year	2015
Abstract	Feature extraction and classification for electroencephalogram (EEG) in medical applications is a challenging task. The EEG signals produce a huge amount of redundant data or repeating information. This redundancy causes potential hurdles in EEG analysis. Hence, we propose to use this redundant information of EEG as a feature to discriminate and classify different EEG datasets. In this study, we have proposed a JPEG2000 based approach for computing data redundancy from multi-channels EEG signals and have used the redundancy as a feature for classification of EEG signals by applying support vector machine, multi-layer perceptron and k-nearest neighbors classifiers. The approach is validated on three EEG datasets and achieved high accuracy rate (95–99 %) in the classification. Dataset-1 includes the EEG signals recorded during fluid intelligence test, dataset-2 consists of EEG signals recorded during memory recall test, and dataset-3 has epileptic seizure and non-seizure EEG. The findings demonstrate that the approach has the ability to extract robust feature and classify the EEG signals in various applications including clinical as well as normal EEG patterns.
Starting Page	207
Ending Page	217
Page Count	11
File Format	PDF
ISSN	08960267
Journal	Brain Topography
Volume Number	29
Issue Number	2
e-ISSN	15736792
Language	English
Publisher	Springer US
Publisher Date	2015-11-27
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data redundancy Feature extraction Classification EEG signal Neurosciences Psychiatry Neurology
Content Type	Text
Resource Type	Article
Subject	Neurology Anatomy Radiology, Nuclear Medicine and Imaging Neurology (clinical) Radiological and Ultrasound Technology

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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