Wajid Aziz

Work place: Computer Science & IT University of Azad Jammu and Kashmir Muzaffarabad, Pakistan

E-mail: Kh_wajid@yahoo.com

Website:

Research Interests: Computational Science and Engineering, Computational Engineering, Engineering

Biography

Dr. Wajid Aziz Loun is a Professor and Chairman of the Department of Computer Sciences & Information Technology at the University of Azad Jammu and Kashmir (AJ&K), Muzaffarabad, Pakistan. He received his MS from the University of AJ&K. He received his PhD from Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad, Pakistan in 2006 and his Post Doctorate from the University of Leicester UK in 2011. His research interests include biomedical signal processing and nonlinear time series analyses.

Author Articles
Time-Frequency Wavelet Based Coherence Analysis of EEG in EC and EO during Resting State

By Lal Hussain Wajid Aziz

DOI: https://doi.org/10.5815/ijieeb.2015.05.08, Pub. Date: 8 Sep. 2015

The electrophysiological brain activities are nonlinear in nature as measured by Electroencephalography (EEG). There are coherent activities in brain not only seen during explicit tasks but also during rest. This article aims to employ most robust nonlinear dynamics Time - Frequency representation (TFR) techniques such as wavelet phase coherence to investigate brain activity in different frequency bands at temporal and spatial scale dynamics in form of topographic maps in resting state networks. The TFR has the advantages to study the combined effect of time and frequency domains simultaneously. The wavelet coherence computed in this way exhibit high precision to detect the phase coherence in different frequency intervals to analyze highly complex non-autonomous and non-stationary EEG signals. The spatiotemporal dynamics of resting state networks are investigated by computing coherence. We have investigated the Wavelet based Phase coherence of oscillations of eye closed and eye open signals during resting states. The wavelet coherence is computed for selected 19 electrodes according to 10-20 system from 129 channel EEG signals. The significance was obtained using Wilcoxon Signed Rank test and pairwise wavelet coherence was computed for each possible combination. The Wavelet Phase Coherence using Wavelet Transform gives significantly high results (P<0.05) in EC and EO signals during resting states in frequency interval 0.5-50 Hz overall as well as in the band intervals such as delta (05-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), beta (13-22 Hz) and gamma (22-50 Hz). By computing the spatial wavelet phase coherence, we observed significant pathways including sagittal factor (anterior-posterior interhemispheric) and lateral factor (perpendicular to anterior-posterior axis). The lateral factor differences have less affect than the sagittal factor. Each band was involved in different activities in some way, however alpha band showed distinct anterior-posterior activity when the eye-closed coherence was higher than the eye open coherence.

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