ARTIFACTS CLASSIFICATION IN EEG SIGNALS BASED ON TEMPORAL AVERAGE STATISTICS

Authors

  • Abdul Qayoom Department of Computer Sciences, KICT, International Islamic University Malaysia Kuala Lumpur, Malaysia.
  • Abdul Wahab Department of Computer Sciences, KICT, International Islamic University Malaysia Kuala Lumpur, Malaysia.
  • Norhaslinda Kamaruddin Faculty of Computer and Mathematical Sciences, MARA University of Technology (Malacca), Jasin Campus, Malaysia.
  • Zahid Zahid Department of IT and ITSS , University of Kashmir, Srinagar.

DOI:

https://doi.org/10.11113/jt.v77.6251

Keywords:

EEG classification, EEG artifacts, statistical features, temporal averages, multi layer perceptron.

Abstract

EEG data contamination due to artifacts, such as eye blink, muscle activity, body movement and others pose as an issue in EEG analysis. This study aims to classify three different types of artifacts in EEG signal, namely; ocular, facial muscle and hand movement using statistical features coupled with neural networks as classifier. Temporal averages of five features are used as the feature vector for MLP classification. The experimental results for ocular, facial muscle and hand movement artifacts identification are ranging between 80% and 92%. The classification accuracy for the combination of these EEG artifacts and normal EEG of the subject for resting and eyes-close state are 86% and 96% respectively

References

H. Adeli and S. Ghosh-Dastidar, 2010.Automated EEG-Based Diagnosis Of Neurological Disorders: Inventing The Future Of Neurology. CRC Press.

C. Neuper, G. R. Müller, A. Kübler, N. Birbaumer, and G. Pfurtscheller, 2003. “Clinical Application Of An EEG-Based Brain–Computer Interface: A Case Study In A Patient With Severe Motor Impairment,†Clin. Neurophysiol. 114 (3) : 399–409.

S. Sanei and J. A. Chambers, 2013. EEG Signal Processing. John Wiley & Sons.

A. K. Jain, R. P. W. Duin, J. Mao, and S. Member, 2000. “Statistical Pattern Recognition: A Review,†IEEE Trans. Pattern Anal. Mach. Intell. 22 : 4–37.

X. Niu, L. Zhu, and H. Ding, 2005. “New Statistical Moments For The Detection Of Defects In Rolling Element Bearings,†Int. J. Adv. Manuf. Technol. 26 (11–12) : 1268–1274.

M. Othman, A. Qayoom, and A. Wahab, “Affective Mapping Of EEG During Executive Function Tasks,†in 2012 15th International Conference on Computer and Information Technology (ICCIT). 2012 : 144–149.

N. Kamaruddin and A. Wahab, 2008. “Speech Emotion Verification System (SEVS) Based On MFCC For Real Time Applications†.

A. Wahab, N. Kamaruddin, L. K. Palaniappan, M. Li, and R. Khosrowabadi, 2010. “EEG Signals For Emotion Recognition,†J. Comput. Methods Sci. Eng. 10 (1–2S1) : 1–11.

Downloads

Published

2015-11-12

Issue

Vol. 77 No. 7: Medical Engineering Vol. 2

Section

Science and Engineering

License

Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

ARTIFACTS CLASSIFICATION IN EEG SIGNALS BASED ON TEMPORAL AVERAGE STATISTICS. (2015). Jurnal Teknologi (Sciences & Engineering), 77(7). https://doi.org/10.11113/jt.v77.6251