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Assessment of Independent EEG Components Obtained by Different Methods for BCI Based on Motor Imagery
Húsek, Dušan ; Frolov, A. A. ; Kerechanin, J. V. ; Bobrov, P.D.
Eight methods of decomposition of a multichannel EEG signal are compared in terms of their ability to identify the most physiologically significant components. The criterion for the meaningfulness of a method is its ability to reduce mutual information between components; to create components that can be attributed to the activity of dipoles located in the cerebral cortex; find components that are provided by other methods and for this case; and at the same time, these components should most contribute to the accuracy of the BCI based on imaginary movement. Independent component analysis methods AMICA, RUNICA and FASTICA outperform others in the first three criteria and are second only to the Common Spatial Patterns method in the fourth criterion. The components created by all methods for 386 experimental sessions of 27 subjects were combined into more than 100 clusters containing more than 10 elements. Additionally, the components of the 12 largest clusters were analyzed. They have proven to be of great importance in controlling BCI, their origins can be modeled using dipoles in the brain, and they have been detected by several degradation methods. Five of the 12 selected components have been identified and described in our previous articles. Even if the physiological and functional origins of the rest of identified components’ are to be the subject of further research, we have shown that their physiological nature is at least highly probable.\n
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Source localization for EEG patterns relevant to motor imagery BCI control
Bobrov, P. ; Frolov, A. ; Húsek, Dušan ; Tintěra, J.
This work concerns spatial localization of sources of EEG patterns the most specific for control of the motor imagery based BCI. In our previous work we have shown that performance of Bayesian BCI classifier can be drastically improved by extraction of the most relevant independent components of the EEG signal. This paper presents the results of spatial localization of electrical brain activity sources which activity is reflected by the extracted components. The localization was performed by solving the inverse problem in EEG source localization, using individual finite-element head models. The sources were located in central sulcus (Brodmann area 3a), in the superior regions of post- and precentral gyri, and supplementary motor cortex.
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Classification Methods for Brain-Computer Interface
Bobrov, P. ; Frolov, A. A. ; Húsek, Dušan
The performance of four classifiers for Brain Computer Interface (BCI) systems based on multichannel EEG recordings is tested in this work. The classifiers are designed to distinguish EEG patterns corresponding to performance of several mental tasks. It is shown that relatively simple classifiers based on the Bayesian approach are comparable in classification accuracy with more sophisticated classifiers based on Common Spatial Patterns and Common Tensor Discriminant Analysis
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