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

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