title: A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals.
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.
authors.affiliation: Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak, Malaysia. hafeezullahamin@gmail.com.
authors.forename: Hafeez Ullah
authors.initials: HU
authors.lastname: Amin
authors.affiliation: Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak, Malaysia. aamir_saeed@petronas.com.my.
authors.forename: Aamir Saeed
authors.initials: AS
authors.lastname: Malik
authors.affiliation: Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak, Malaysia.
authors.forename: Nidal
authors.initials: N
authors.lastname: Kamel
authors.affiliation: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Hussain
mesh_terms: D000465:Algorithms; D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods / Q000706:statistics & numerical data; D004569:Electroencephalography; D006801:Humans; D015203:Reproducibility of Results; D060388:Support Vector Machine
keywords: Classification; Data redundancy; EEG signal; Feature extraction