Work place: Computer Science & IT University of Azad Jammu and Kashmir Muzaffarabad, Pakistan
E-mail: lall_hussain2008@live.com
Website:
Research Interests: Computational Engineering, Engineering
Biography
Lal Hussain completed his MCS degree in Computer Science from the University AJK, Pakistan, in 2005 and his MS in Communication and Networks from Iqra University, Islamabad, Pakistan, in 2012 with a gold medal. Mr. Hussain enrolled in his PhD with the Department of CS & IT, University of AJK, Muzaffarabad, Pakistan, in 2012. He is currently working as Assistant Director Quality Enhancement Cell, UAJK since 2006. His responsibilities includes arranging trainings for faculty members, assisting program teams, preparing self-assessment reports, and preparing institutional performance evaluation data, program assessments, research activities, etc. His research interests include biomedical signal processing and engineering on biomedical and physiological signals, with a concentration on the analysis of time- frequency-based techniques, including wavelet coherence, coupling, cross frequency coupling, Bayesian inference, information theory and entropy based techniques. Recently, he worked as a Research Assistant for six months at Lancaster University UK under the HEC IRSIP scholarship program.
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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