Work place: V R Siddhartha Engineering College, Vijayawada, India
Research Interests: Medical Image Computing, Image Processing, Image Manipulation, Image Compression
VijayaSankar. Anumala received B.E. degree in Electronics and communication Engg. from Andhra University, Visakhapatnam, in 2001 and M.Tech degree in Digital Electronics and Communication Systems from Visvesvaraya Technological University, Belgaum, in 2003. He is currently pursuing the PhD degree with the Electronics and communication Engineering Department, AU College of Engineering, Visakhapatnam. His research interests are Communications, Biomedical Signal and Image Processing. He has published more than 10 Research papers in International/National Journals and conferences.
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.[...] Read more.
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