Clustering the Sources of EEG Activity during Motor Imagery by Attractor Neural Network with Increasing Activity (ANNIA)

0429364 - ÚI 2015 RIV CH eng C - Conference Paper (international conference)
Bobrov, P. - Frolov, A. - Húsek, Dušan - Snášel, V.
Clustering the Sources of EEG Activity during Motor Imagery by Attractor Neural Network with Increasing Activity (ANNIA).
Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Cham: Springer, 2014 - (Krömer, P.; Abraham, A.; Snášel, V.), s. 183-191. Advances in Intelligent Systems and Computing, 303. ISBN 978-3-319-08155-7. ISSN 2194-5357.
[IBICA 2014. International Conference on Innovations in Bio-Inspired Computing and Applications /5./. Ostrava (CZ), 23.06.2014-25.06.2014]
Grant - others:GA MŠk(CZ) ED1.1.00/02.0070; GA MŠk(CZ) EE.2.3.20.0073
Program: ED
Institutional support: RVO:67985807
Keywords : brain computer interface * motor imagery * independent component analysis * attractor neural network with increasing activity
Subject RIV: IN - Informatics, Computer Science

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
Permanent Link: http://hdl.handle.net/11104/0234488