Online artifact removal for brain-computer interfaces using support vector machines and blind source separation

Authors:
Sebastian Halder;Michael Bensch;Jürgen Mellinger;Martin Bogdan;Andrea Kübler;Niels Birbaumer;Wolfgang Rosenstiel
Affiliations:
Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany;Wilhelm-Schickard Institute for Computer Engineering, University of Tübingen, Tübingen, Germany;Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany;Wilhelm-Schickard Institute for Computer Engineering, University of Tübingen, Tübingen, Germany and Computer Engineering, Institute of Computer Science, Faculty of Mathematics and Comput ...;Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany;Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany and National Institutes of Health, National Institute of Neurological Disorders and ...;Wilhelm-Schickard Institute for Computer Engineering, University of Tübingen, Tübingen, Germany
Venue:
Computational Intelligence and Neuroscience - Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications
Year:
2007

Citing 5
Cited 3

A fast fixed-point algorithm for independent component analysis

Neural Computation
High-order contrasts for independent component analysis

Neural Computation
Probability Estimates for Multi-class Classification by Pairwise Coupling

The Journal of Machine Learning Research
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks

A template-based isomap algorithm for real-time removal of ocular artifacts from EEG signals

Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Automatic removal of eye-blink artifacts based on ICA and peak detection algorithm

CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Dynamic Fuzzy Neural Network Based Learning Algorithms for Ocular Artefact Reduction in EEG Recordings

Neural Processing Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.