Abstract
Brain connectivity measures have been identified as effective feature extraction tools for the classification of EEG data. However, there exist certain theoretical limitations in the computation of brain networks. First, bivariate models of brain connectivity are incapable of handling the multivariate nature of brain connections. Second, multivariate brain connectivity models are typically based on regression models. These regression models are associated with stationary assumptions, which do not hold for EEG data. To solve this problem, the authors propose clustering as a tool to perform multivariate brain connectivity analysis. Extended variants of Fuzzy c-means and self-organizing map-based clustering are proposed to compute brain networks, which are subsequently used as features for mental imagery detection. Experiments undertaken demonstrate the superiority of the proposed brain network features over its traditional counterparts.
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Kar, R., Mazumder, I. (2021). Clustering as a Brain-Network Detection Tool for Mental Imagery Identification. In: Pan, I., Mukherjee, A., Piuri, V. (eds) Proceedings of Research and Applications in Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1355. Springer, Singapore. https://doi.org/10.1007/978-981-16-1543-6_8
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DOI: https://doi.org/10.1007/978-981-16-1543-6_8
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