Independent Vector Analysis Applied to Remove Muscle Artifacts in EEG Data
Product Description
Independent Vector Analysis Applied to Remove Muscle Artifacts in EEG Data
Abstract– Electroencephalogram (EEG) data are often contaminated by various electrophysiological artifacts. Among all these artifacts, the muscle activity is particularly difficult to remove. In the literature, independent component analysis (ICA) and canonical correlation analysis (CCA), as blind source separation techniques, are the most popular methods. At the same time, novel method for removing muscle artifacts in EEG data based on independent vector analysis. This method exploits both the second-order and higher order statistical information and thus takes advantage of both ICA and CCA. The proposed method is evaluated on realistic simulated data and is shown to significantly outperform ICA and CCA. In addition, the proposed method is applied on real ictal EEG data seriously contaminated with muscle artifacts. As a result, the proposed method is able to largely suppress muscle artifacts without altering the underlying EEG activity.
Including Packages
Our Specialization
Support Service
Statistical Report
satisfied customers
3,589
Freelance projects
983
sales on Site
11,021
developers
175+
Additional Information
| Domains | |
|---|---|
| Programming Language |
