Paper Info
Title
Removal of ocular artifacts in EEG--an improved approach combining DWT and ANC for portable applications.
Abstract
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 and measured 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.
Year
DOI
Venue
2013
10.1109/JBHI.2013.2253614
IEEE J. Biomedical and Health Informatics
Keywords
Field
DocType
eye,signal denoising,model accuracy,portable application,signal processing,tracking performance,oa reference signal,portable system requirement,adaptive noise cancellation (anc),acceptable wearable sensor attachment constraint,electroencephalogram,dwt,electroencephalography,low frequency wavelet coefficient,patient monitoring,simulated data,wavelet packet transform accuracy,ocular artifact removal,adaptive noise cancellation,medical signal processing,optimi project,online predictive tools for intervention in mental illness,body sensor networks,source separation,independent component analysis accuracy,single channel source,true eeg signal recovery,discrete wavelet transformation,anc,measured data,single channel device,electroencephalogram (eeg),stationary wavelet transform accuracy,ocular artifacts (oas),eufp 7 project,discrete wavelet transforms,frequency domain analysis,correlation,interference
Signal processing,Computer vision,Pattern recognition,Remote patient monitoring,Computer science,Artificial intelligence,Independent component analysis,Active noise control,Wavelet packet decomposition,Source separation,Wavelet,Wavelet transform
Journal
Volume
Issue
ISSN
17
3
2168-2208
Citations 
PageRank 
References 
17
0.93
8
Authors
7
Name
Order
Citations
PageRank
Hong Peng18111.42
Bin Hu2778107.21
Qiuxia Shi3212.09
Martyn Ratcliffe4565.12
Qinglin Zhao515826.30
Yanbing Qi6544.18
Guoping Gao7170.93