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Assessment of Independent EEG Components Obtained by Different Methods for BCI Based on Motor Imagery
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SYSNO ASEP 0545601 Document Type V - Research Report R&D Document Type The record was not marked in the RIV Title Assessment of Independent EEG Components Obtained by Different Methods for BCI Based on Motor Imagery Author(s) Húsek, Dušan (UIVT-O) RID, SAI, ORCID
Frolov, A. A. (RU)
Kerechanin, J. V. (RU)
Bobrov, P.D. (RU)Issue data Prague: ICS CAS, 2021 Series Technical Report Series number V-1279 Number of pages 17 s. Language eng - English Country CZ - Czech Republic Keywords brain–computer interface ; motor imagery ; blind source separation ; independent component analysis ; common spatial patterns ; cluster analysis ; EEG pattern extraction ; EEG analysis ; ICA ; CSP ; BCI ; motor imagery Institutional support UIVT-O - RVO:67985807 Annotation 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.
Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2022
Number of the records: 1