Abstract
Electroencephalography (EEG) has been used extensively for classifying cognitive tasks. Many investigators have demonstrated classification accuracies well over 90% for some combinations of cognitive tasks, signal transformations, and classification methods. Unfortunately, EEG data is prone to significant interference from a wide variety of artifacts, particularly eye blinks. Most methods for classifying cognitive tasks with EEG data simply discard time windows containing eye blink artifacts. However, future applications of EEG-based cognitive task classification should not be hindered by eye blinks. The value of an EEG-controlled human-computer interface, for instance, would be severely diluted if it did not work in the presence of eye blinks. Fortunately, recent advances in blind signal separation algorithms and their applications to EEG data mitigate the artifact contamination issue. In this paper, we show how independent components analysis (ICA) and its extension for sub-Gaussian sources, extended ICA (eICA), can be applied to accurately classify cognitive tasks with eye blink contaminated EEG recordings.
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© 1999 Springer-Verlag Berlin Heidelberg
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Peterson, D.A., Anderson, C.W. (1999). EEG-based cognitive task classification with ICA and neural networks. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100493
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DOI: https://doi.org/10.1007/BFb0100493
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