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

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Web End = Brain Topogr (2016) 29:207217 DOI 10.1007/s10548-015-0462-2

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Web End = A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals

Hafeez Ullah Amin1 Aamir Saeed Malik1 Nidal Kamel1 Muhammad Hussain2

Received: 27 March 2015 / Accepted: 7 November 2015 / Published online: 27 November 2015 Springer Science+Business Media New York 2015

Abstract Feature extraction and classication 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 classication of EEG signals by applying support vector machine, multi-layer perceptron and k-nearest neighbors classiers. The approach is validated on three EEG datasets and achieved high accuracy rate (9599 %) in the classication. Dataset-1 includes the EEG signals recorded during uid intelligence test, dataset-2 consists of EEG signals recorded during memory recall test, and dataset-3 has epileptic seizure and non-seizure EEG. The ndings 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.

Keywords Data redundancy Feature extraction

Classication EEG signal

Introduction

Electroencephalography (EEG) is a reliable tool to measure and assess the neurophysiological changes related to postsynaptic activity in the neocortex (Tong et al. 2009). It enables the researchers and clinicians to study the brain functions such as memory, vision, intelligence, motor imagery, emotion, perception, and recognition; as well as detect its abnormalities such as epilepsy, stroke, dementia, sleep disorders, depression, and trauma.

Existing approaches of EEG analysis include the basic EEG rhythms analysis, spectral analysis, time series analysis, timefrequency analysis, statistical analysis (mean, median, and standard deviation) and so on (Tong et al. 2009). However, these approaches do not always give...