Removal of ocular artifacts in EEG - An improved approach combining DWT and ANC for portable applications

Peng, H. and Hu, B. and Shi, Q. and Ratcliffe, M. and Zhao, Q. and Qi, Y. and Gao, G. (2013) Removal of ocular artifacts in EEG - An improved approach combining DWT and ANC for portable applications. IEEE Journal of Biomedical and Health Informatics, 17 (3). pp. 600-607. ISSN 21682194 (ISSN)

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A new model to remove ocular artifacts (OA) from electroencephalograms (EEGs) is presented. The model is based on discrete wavelet transformation (DWT) and adaptive noise cancellation (ANC). Using simulated andmeasured data, the accuracy of the model is compared with the accuracy of other existing methods based on stationary wavelet transforms and our previous work based on wavelet packet transform and independent component analysis. A particularly novel feature of the new model is the use of DWTs to construct an OA reference signal, using the three lowest frequency wavelet coefficients of the EEGs. The results show that the new model demonstrates an improved performance with respect to the recovery of true EEG signals and also has a better tracking performance. Because the new model requires only single channel sources, it is well suited for use in portable environments where constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices. The model is also applied and evaluated against data recorded within the EUFP 7 Project - Online Predictive Tools for Intervention in Mental Illness (OPTIMI). The results show that the proposed model is effective in removing OAs and meets the requirements of portable systems used for patient monitoring as typified by the OPTIMI project.

Item Type: Article
Identification Number:
Uncontrolled Keywords: Adaptive noise cancellation (ANC), Electroencephalogram (EEG), Ocular artifacts (OAs), Signal processing, Adaptive noise cancellations, Discrete wavelet transformation, Electro-encephalogram (EEG), Ocular artifacts, Portable applications, Portable environments, Stationary wavelet transforms, Wavelet packet transforms, Bioelectric phenomena, Discrete wavelet transforms, Diseases, Electroencephalography, Independent component analysis, Patient monitoring, Sensors, Signal denoising, Signal processing, Spurious signal noise, Computer simulation, adult, algorithm, article, artifact, child, electroencephalography, eye movement, factual database, female, human, intellectual impairment, methodology, middle aged, pathophysiology, physiology, seizure, signal processing, theoretical model, Adult, Algorithms, Artifacts, Child, Databases, Factual, Electroencephalography, Eye Movements, Female, Humans, Intellectual Disability, Middle Aged, Models, Theoretical, Seizures, Signal Processing, Computer-Assisted
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Hussen Farooq
Date Deposited: 04 Aug 2016 13:15
Last Modified: 03 Mar 2022 15:47

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