Online artifact removal for brain-computer interfaces using support vector machines and blind source separation.
We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.
Halder
Sebastian
S
Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Gartenstr. 29, 72074 Tübingen, Germany. halder@informatik.uni-tuebingen.de
Bensch
Michael
M
Mellinger
Jürgen
J
Bogdan
Martin
M
Kübler
Andrea
A
Birbaumer
Niels
N
Rosenstiel
Wolfgang
W