|
Activity Number:
|
125
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 1, 2016 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Biometrics Section
|
|
Abstract #320757
|
View Presentation
|
|
Title:
|
Classification of Multivariate EEG Records via $\Epsilon$-Complexity of Continuous Vector-Functions
|
|
Author(s):
|
Alexandra Piryatinska* and Boris Darkhovsky and Nathanael Aff
|
|
Companies:
|
San Francisco State University and Institute for Systems Analysis and San Francisco State University
|
|
Keywords:
|
EEG records ;
classification ;
epsilon-complexity ;
multivariate time series
|
|
Abstract:
|
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.
|
Authors who are presenting talks have a * after their name.
