EEG artifact removal and detection via clustering
|Institution:||Texas Tech University|
|Keywords:||Electroencephalography (EEG); Clustering; Artifact removal; Isodata; Independent component analysis (ICA)|
|Full text PDF:||http://hdl.handle.net/2346/58689|
An automatic method for detecting and cleaning EEG artifactual ICA components is presented in this dissertation. Unsupervised learning is utilized for the detection of artifactual components. The artifact removal method is implemented in a six step process called ABEAR. The six steps of ABEAR are bad epoch removal, ICA, generation of component features, component clustering, cluster labeling, and component cleaning. Each step of ABEAR is evaluated using a recorded dataset with manually labeled components. A simulated dataset is also created to test the benefits of cleaning components compared to removing components. The simulated dataset reveals that cleaning components presents benefits when potential cerebral signal is included in artifactual components. ABEAR successfully detects and removes artifactual contributions to EEG signal caused by eye movements, electrocardiogram signals, electromyogram signals, movement, and bad channels.