Electroencephalography as a generally accepted method of monitoring the electrical activity of brain neurons is widely used both in diseases and in healthy conditions. The recorded electrical signal is usually obtained from several electrodes located on the scalp. While EEG recording techniques are largely standardized, the interpretation of some aspects is still an open question. There is hardly questionable progress in detecting abnormal EEG signals known as seizures. A less explored field is the detection and classification of non-pathological conditions such as emotional and other functional states of the brain. This requires special approaches and techniques that have been widely developed over the past decade. The current paper describes an attempt to use algorithmic complexity concepts and tools for EEG transformation making it possible to combine this approach and machine learning for classification purposes.