Correction of Ocular Artifacts from EEG by DWT with an Improved Thresholding

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)

Abstract

Electroencephalogram (EEG) signals are widely being used for analyzing the activities of brain. It is extensively used for diagnosing different central nervous system disorders such as Alzheimer’s, Parkinson’s, seizures, epilepsy, etc. Ocular activity creates significant artifacts in EEG recordings. Analysis of the EEG and obtaining clinical information is difficult because of these noise sources. This paper proposes discrete wavelet transform (DWT) based denoising method with new statistical thresholding for single channel EEG signal. This method is evaluated on EEG signals taken from polysomnographic records, eegmmidb database. The effectiveness of the proposed method was measured using parameters such as signal to noise ratio (SNR), artifact rejection ratio (ARR) and comparing with the existing threshold method. Result of this study reveals that DWT with proposed thresholding method has shown superior performance in terms of SNR and ARR and effectively eliminates ocular artifacts.

Keywords

EEG Ocular artifacts DWT Statistical thresholding SNR ARR 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of ECEV R Siddhartha Engineering CollegeVijayawadaIndia
  2. 2.Department of ECEAndhra University College of EngineeringVisakhapatnamIndia

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