Title :
Complex tensor based blind source separation of EEG for tracking P300 subcomponents
Author :
Samaneh Kouchaki;Shirin Enshaeifar;Clive Cheong Took;Saeid Sanei
Author_Institution :
Faculty of Engineering and Physical Sciences, University of Surrey, United Kingdom
Abstract :
Complex tensor factorisation of correlated brain sources is addressed in this paper. The electrical brain responses due to motory, sensory, or cognitive stimuli, i.e. event related potentials (ERPs), particularly P300, have been used for cognitive information processing. P300 has two subcomponents, P3a and P3b which are correlated and therefore, the traditional blind source separation approaches cannot solve the problem. In this work, a complex-valued tensor factorisation of electroencephalography (EEG) signals is introduced with the aim of separating P300 subcomponents. The proposed method uses complex-valued statistics to exploit the data correlation. In this way, the variations of P3a and p3b can be tracked for the assessment of the brain state. The results of this work will be compared with those of spatial principal component analysis (SPCA) method.
Keywords :
"Tensile stress","Electroencephalography","Covariance matrices","Correlation","Principal component analysis","Electrodes","Brain modeling"
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE
Electronic_ISBN :
1558-4615
DOI :
10.1109/EMBC.2015.7320003