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Causality graph learning on cortical information flow in Parkinson's disease patients during behaviour tests | IEEE Conference Publication | IEEE Xplore

Causality graph learning on cortical information flow in Parkinson's disease patients during behaviour tests


Abstract:

Electroencephalographs (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this pape...Show More

Abstract:

Electroencephalographs (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this paper is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include motor and phonemic fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI).
Date of Conference: 08-11 November 2015
Date Added to IEEE Xplore: 29 February 2016
ISBN Information:
Electronic ISSN: 1058-6393
Conference Location: Pacific Grove, CA, USA

References

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