Binary classification of multi-channel EEG records based on the $\epsilon$-complexity of continuous vector functions

TL;DR

This paper introduces a novel, model-free method for binary classification of multichannel EEG data based on the extended $\

Contribution

It extends the $\

Findings

Accurately classifies EEG data related to mental states.

Effective in distinguishing healthy adolescents from those with schizophrenia.

Operates successfully in a four-dimensional feature space.

Abstract

A methodology for binary classification of EEG records which correspond to different mental states is proposed. This model-free methodology is based on our theory of the $\epsilon$-complexity of continuous functions which is extended here (see Appendix) to the case of vector functions. This extension permits us to handle multichannel EEG recordings. The essence of the methodology is to use the $\epsilon$-complexity coefficients as features to classify (using well known classifiers) different types of vector functions representing EEG-records corresponding to different types of mental states. We apply our methodology to the problem of classification of multichannel EEG-records related to a group of healthy adolescents and a group of adolescents with schizophrenia. We found that our methodology permits accurate classification of the data in the four-dimensional feather space of the $\epsilon$-complexity coefficients.