Abstract
Electrical brain activity in subjects controlling Brain-Computer Interface (BCI) based on motor imagery is studied. A used data set contains 7440 observations corresponding to distributions of electrical potential at the head surface obtained by Independent Component Analysis of 155 48-channel EEG recordings over 16 subjects. The distributions are interpreted as produced by the current dipolar sources inside the head. To reveal the sources of electrical brain activity the most typical for motor imagery, the corresponding ICA components were clustered by Attractor Neural Network with Increasing Activity (ANNIA). ANNIA was already successfully applied to clustering textual documents and genome data [8,11]. Among the expected clusters of components (blinks and mu-rhythm ERD) the ones reflecting the frontal and occipital cortex activity were also extracted. Although the cluster analysis can not substitute careful data examination and interpretation however it is a useful pre-processing step which can clearly aid in revealing data regularities which are impossible to tract by sequentially browsing through the data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bell, T.J., Sejnowski, A.J.: An Information-Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7, 1129–1159 (1995)
Bobrov, P., Frolov, A., Cantor, C., Fedulova, I., Bakhnyan, M., Zhavoronkov, A.: Brain-Computer Interface Based on Generation of Visual Images. PLoS One 6(6), e20674 (2011), doi:10.1371/journal.pone.0020674
Delorme, S., Makeig, A.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. Journal of Neuroscience Methods 134, 9–21 (2004)
Faber, J., Novak, M.: Thalamo-cortical reverberation in the brain produces alpa and deta rhythms as iterative convergence of fuzzy cognition in a stochastic ebvironment. Neural Network World 21(2), 169–192 (2011)
Frolov, A.A., Sirota, A.M., Husek, D., Muraviev, I.P., Polyakov, P.A.: Binary factorization in hopfield-like neural networks: single-step approximation and computer simulations. Neural Network World 14(2), 139–152 (2004)
Frolov, A.A., Husek, D., Muraviev, I.P., Polyakov, P.Y.: Boolean Factor Analysis by Attractor Neural Network. IEEE Transactions on Neural Networks 18(3), 698–707 (2007)
Frolov, A.A., Polyakov, P.Y., Husek, D., Rezankova, H.: Neural Network Based Boolean Factor Analysis of Parliament Voting. In: Proceedings in Computational Statistics, Heidelberg, pp. 861–868 (2007)
Frolov, A.A., Husek, D., Polyakov, Y.P.: Recurrent-Neural-Network-Based Boolean Factor Analysis and Its Application to Word Clustering. IEEE Transactions on Neural Networks 20(7), 1073–1086 (2009)
Frolov, A.A., Husek, D., Bobrov, P.: Comparison of four classification methods for brain-computer interface. Neural Network World 21(2), 101–115 (2011)
Frolov, A., Husek, D., Bobrov, P., Korshakov, A., Chernikova, L., Konovalov, R., Mokienko, O.: Sources of EEG activity most relevant to performance of brain-computer interface based on motor imagery. Neural Network World 22(1), 21–37 (2012)
Frolov, A.A., Husek, D., Polyakov, P.Y., Snasel, V.: New BFA method based on attractor neural network and likelihood maximization. Neurocomputing 132, 14–29 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bobrov, P., Frolov, A., Husek, D., Snášel, V. (2014). Clustering the Sources of EEG Activity during Motor Imagery by Attractor Neural Network with Increasing Activity (ANNIA). In: Kömer, P., Abraham, A., Snášel, V. (eds) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Advances in Intelligent Systems and Computing, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-319-08156-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-08156-4_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08155-7
Online ISBN: 978-3-319-08156-4
eBook Packages: EngineeringEngineering (R0)