Open AccessOnline Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation
Author(s) -
Sebastian Halder,
Michael Bensch,
Jürgen Mellinger,
Martin Bogdan,
Andrea Kübler,
Niels Birbaumer,
Wolfgang Rosenstiel
Publication year - 2007
Publication title -
computational intelligence and neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 52
eISSN - 1687-5273
pISSN - 1687-5265
DOI - 10.1155/2007/82069
Subject(s) - independent component analysis , infomax , computer science , fastica , blind signal separation , artifact (error) , support vector machine , artificial intelligence , pattern recognition (psychology) , filter (signal processing) , identification (biology) , brain–computer interface , signal (programming language) , source separation , speech recognition , electroencephalography , computer vision , psychology , computer network , channel (broadcasting) , botany , psychiatry , biology , programming language
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.
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