Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings

—Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from Independent Component Analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event related potential (ERP)-related independent components (ICs). However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g. identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by non-biological origins like highimpedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consi...

• Type: Journal
• Year: 2016
• Where: TITB
• Authors: Yuan Zou, Viswam Nathan, Roozbeh Jafari
• Keywords: Education, TITB 2016