Title :
Removal of Ocular Artifacts in EEG—An Improved Approach Combining DWT and ANC for Portable Applications
Author :
Hong Peng ; Bin Hu ; Qiuxia Shi ; Ratcliffe, Martyn ; Qinglin Zhao ; Yanbing Qi ; Guoping Gao
Author_Institution :
Lab. of Ubiquitous Awareness & Intell. Solutions, Lanzhou Univ., Lanzhou, China
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.
Keywords :
body sensor networks; discrete wavelet transforms; electroencephalography; eye; medical signal processing; signal denoising; source separation; ANC; DWT; EUFP 7 Project; OA reference signal; OPTIMI project; Online Predictive Tools for Intervention in Mental Illness; acceptable wearable sensor attachment constraint; adaptive noise cancellation; discrete wavelet transformation; electroencephalogram; independent component analysis accuracy; low frequency wavelet coefficient; measured data; model accuracy; ocular artifact removal; patient monitoring; portable application; portable system requirement; simulated data; single channel device; single channel source; stationary wavelet transform accuracy; tracking performance; true EEG signal recovery; wavelet packet transform accuracy; Adaptation models; Brain modeling; Correlation; Discrete wavelet transforms; Electroencephalography; Frequency-domain analysis; Interference; Adaptive noise cancellation (ANC); electroencephalogram (EEG); ocular artifacts (OAs); signal processing;
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
DOI :
10.1109/JBHI.2013.2253614