Research Area:  Machine Learning

Abstract:

The electroencephalogram (EEG) is often contaminated by muscle artifacts. In this paper, a new method for muscle artifact removal in EEG is presented, based on canonical correlation analysis (CCA) as a blind source separation (BSS) technique. This method is demonstrated on a synthetic data set. The method outperformed a low-pass filter with different cutoff frequencies and an independent component analysis (ICA)-based technique for muscle artifact removal. In addition, the method is applied on a real ictal EEG recording contaminated with muscle artifacts. The proposed method removed successfully the muscle artifact without altering the recorded underlying ictal activity

Keywords:  

Author(s) Name:  Wim De Clercq; Anneleen Vergult; Bart Vanrumste; Wim Van Paesschen; Sabine Van Huffel

Publisher name:  IEEE

DOI:  10.1109/TBME.2006.879459

Volume Information:  Volume: 53, Issue: 12, Nov. 2006, Page(s): 2583 - 2587

Paper Link:   https://ieeexplore.ieee.org/abstract/document/4015602