Binary classification of multi-channel EEG records based on the $ε$-complexity of continuous vector functions
This work addresses EEG-based mental state classification, potentially aiding in schizophrenia diagnosis, but it is incremental as it applies an extended theory to a specific domain.
The authors tackled binary classification of multi-channel EEG records for mental states by extending ε-complexity theory to vector functions and using the coefficients as features, achieving accurate classification in a four-dimensional feature space for distinguishing healthy adolescents from those with schizophrenia.
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 $ε$-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 $ε$-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 $ε$-complexity coefficients.