Paper Info
Title
The Removal Of Ocular Artifacts From Eeg Signals: An Adaptive Modeling Technique For Portable Applications
Abstract
Modeling and prediction of Electroencephalogram (EEG) signals is very important for Portable applications; EEG signals are however widely regarded as being chaotic in nature. An adaptive modeling technique that combines Discrete Wavelet Transformation (DWT) to predict contaminated EEG signals for removal of ocular artifacts (OAs) from EEG records is proposed as an effective a data processing tool for Interventions in Mental Illness Based on Bio-feedback. The proposed method is well suited for use in portable environments where constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices. Using simulated and measured data the accuracy of the proposed model is compared to the accuracy of other pre-existing methods based on Wavelet Packet Transform (WPT) and independent component analysis (ICA) using DWT and adaptive noise cancellation (ANC) for Portable applications. The results show that the our new model not only demonstrates an improved performance with respect to the recovery of true EEG signals, achieves improved computational speed, and demonstrates better tracking performance.
Year
DOI
Venue
2013
10.1109/BIBM.2013.6732494
2013 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Keywords
DocType
Volume
EEG, ocular artifacts, Adaptive model, DWT, Portable Applications
Conference
null
Issue
ISSN
Citations 
null
2156-1125
0
PageRank 
References 
Authors
0.34
5
7
Name
Order
Citations
PageRank
Li Yang135963.68
Bin Hu214018.53
Qinglin Zhao315826.30
Hong Peng48111.42
Yujun Shi500.34
Yunpeng Li657845.91
Philip Moore715822.41