Abstract
Independent Component Analysis (ICA) is often used in EEG signal processing but only when the matrix of channels is big enough. If the matrix is small, the artifacts usually cannot be extracted as single components and the decision which components should be removed from the components’ set is impeded. Therefore, in our opinion, in order to apply ICA successfully for a low-dimensional EEG, the strategy for dealing with the components’ set should be reversed.
In the paper we propose a strategy of searching for components correlating with the desired brain activity, instead of looking for artifact-components. Obviously, since the brain activity depends on the task at hand, different tasks would require adaptation of the proposed approach but the overall scheme is independent from the task. In the paper we describe the strategy and illustrate it via the experiment with a simple 2-states Motor Imagery Brain Computer Interface. Our results show that even if we added only one spare channel to the core set of two channels (C3 and C4) essential for hand movement recognition, we obtained 18% increase in the recognition accuracy after applying ICA and our strategy.
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Rejer, I., Górski, P. (2018). EEG Classification for MI-BCI with Independent Component Analysis. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_41
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DOI: https://doi.org/10.1007/978-3-319-59162-9_41
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