Extended ICA removes artifacts from electroencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Computational intelligent brain computer interaction and its applications on driving cognition
IEEE Computational Intelligence Magazine
Automatic removal of eye-blink artifacts for neurofeedback training systems
Proceedings of the 7th International Convention on Rehabilitation Engineering and Assistive Technology
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Removal of artifacts is an important step in any research in /application of electroencephalogram (EEG). The artifacts may contain eye-blinking, muscle noise, heart signal, line noise, and environmental effect. Such noises often make the raw EEG signals not very useful for extraction/identification of physiological phenomena from EEG. The independent component analysis (ICA) is a popular technique for artifact removal in brain research and some reports demonstrate that ICA can remove the artifacts with lower (acceptable) loss of information. But, these reports select useful independent components manually, primarily by looking at the scalp-plots. This is of great inconvenience and is a barrier for BCI or real-time applications of EEG. In this paper, we demonstrate that machine learning methods could be quite effective to discriminate useful independent components from artifacts and our findings suggests the possibility of developing a 'universal' machine for artifact removal in EEG.