Activity Number:
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125
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2016 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract #320757
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View Presentation
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Title:
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Classification of Multivariate EEG Records via $\Epsilon$-Complexity of Continuous Vector-Functions
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Author(s):
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Alexandra Piryatinska* and Boris Darkhovsky and Nathanael Aff
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Companies:
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San Francisco State University and Institute for Systems Analysis and San Francisco State University
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Keywords:
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EEG records ;
classification ;
epsilon-complexity ;
multivariate time series
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Abstract:
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We propose a methodology for classification of relatively-short multivariate EEG records. This methodology is based on the theory of the ?-complexity of continuous functions which is extended here to the case of vector-functions. This extension permits us to handle multichannel EEG recordings. The essence of the methodology is to use the ?-complexity coefficients as features to classify different types of vector-functions representing EEG-records corresponding to different mental states. We apply our methodology to the problem of classification of two sets of multichannel EEG-records. The first one is a classification of EEG records into alcoholic and control groups. The second one is related to a group of healthy adolescents and a group of adolescents with schizophrenia. We have established that in both cases we obtained an accurate classification in the 4-dimensional spaces of ?-complexity coefficients. For classification in these cases the best results were achieved by the Random Forest classifier. The obtained results indicate the effectiveness of the proposed methodology.
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Authors who are presenting talks have a * after their name.