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

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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.

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Notes

  1. Large dataset, Epilepsy EEG dataset collected at Children's Hospital Boston, Massachusetts Institute of Technology. Available: http://physionet.org/pn6/chbmit/.

  2. Large dataset, EEG Epilepsy dataset collected at Epilepsy Center of the University Hospital of Freiburg. Available: http://epileptologie-bonn.de/cms/.

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Acknowledgments

This research work was supported by the HiCoE grant for CISIR (0153CA-002), Ministry of Education (MOE), Malaysia; and by NSTIP strategic technologies programs, grant number 12-INF2582-02 in the Kingdom of Saudi Arabia. The authors, therefore, acknowledge with thanks the technical and financial support.

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Authors and Affiliations

  1. Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak, Malaysia

    Hafeez Ullah Amin, Aamir Saeed Malik & Nidal Kamel

  2. Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Muhammad Hussain

Authors
  1. Hafeez Ullah Amin
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  2. Aamir Saeed Malik
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  3. Nidal Kamel
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  4. Muhammad Hussain
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Correspondence to Aamir Saeed Malik.

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Amin, H.U., Malik, A.S., Kamel, N. et al. A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals. Brain Topogr 29, 207–217 (2016). https://doi.org/10.1007/s10548-015-0462-2

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