Rajesh Kumar. Pullakura

Work place: A U College of Engineering, Visakhapatnam, India

E-mail: rajeshauce@gmail.com


Research Interests: Image Processing, Computer systems and computational processes, Human-Computer Interaction, Computational Science and Engineering


Dr. P. Rajesh Kumar (MIEEE‟09, FIETE‟02) received the Ph.D degree from Andhra University College of Engineering, Visakhapatnam, in 2007. He is currently working as Professor at Dept. of ECE, AUCE, Visakhapatnam, Andhra Pradesh. He has produced numerous research papers in national and international journals and conferences. He is Editorial member of many International Journals. He has guided various research projects. His research interests are Digital Signal and Image Processing, Computational Intelligence, Human Computer Interaction and Radar Signal Processing.

Author Articles
Removal of Ocular Artifacts in Single Channel EEG by EMD, EEMD and CEEMD Methods Inspired by Wavelet Thresholding

By Vijayasankar. Anumala Rajesh Kumar. Pullakura

DOI: https://doi.org/10.5815/ijigsp.2018.05.05, Pub. Date: 8 May 2018

Electroencephalogram (EEG) is a widely used signal for analyzing the activities of the brain and usually contaminated with artifacts due to movements of eye, heart, muscles and power line interference. Owing to eye movement, Ocular Activity creates significant artifacts and makes the analysis difficult.  In this paper, a new threshold is presented for correction of Ocular Artifacts (OA) from EEG signal using Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD) methods. Unlike the conventional EMD based EEG denoising techniques, which neglects the higher order low-frequency Intrinsic Mode Functions (IMFs), IMF Interval thresholding is opted to correct the artifacts. Obtained the noisy IMFs based on MI scores and perform interval thresholding to the noisy IMFs gives a relatively cleaner EEG signal. Extensive computations are carried out using EEG Motor Movement/Imagery (eegmmidb) dataset and compare the performance of Proposed Threshold (PT) with current threshold functions i.e., Universal Threshold (UT), Minimax Threshold (MT) and Statistical Threshold (ST) using several standard performance metrics: change in SNR (ΔSNR), Artifact Rejection Ratio (ARR), Correlation Coefficient (CC), and Root Mean Square Error (RMSE). Results of these studies reveal that CEEMD+PT is efficient to correct OAs in EEG signals and maintaining the background neural activity in non-artifact zones.

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