Mathematical Model of Pattern Selection for Complex Multichannel Data in EEG Processing
Abstract
Introduction: Research on real systems relies now on processing big experimental data volumes. Recognition of short oscillatory patterns corresponding to different states of complex non-stationary systems requires new processing methods. Purpose: Design of a mathematical model for objective and expertise-independent recognition of patterns corresponding to various states of real systems. Results: We propose a new method of modeling short oscillatory events (patterns) for complex non-stationary multichannel data. A mathematical realization of the model is described in terms of continuous wavelet transformation. Human brain activity states can be recognized automatically for the analysis of long EEG registrations. The proposed mathematical model application is demonstrated by the example of processing human EEG signals non-invasively recorded in the occipital scalp region. We demonstrate successful recognition of various human states based on the analysis of EEG from the visual analyzer area. We discuss the analysis of various patterns in experimental data corresponding to the state of active visual recognition of objects. Practical relevance: This modelling method can be recommended for neurophysiological data processing.Published
2018-08-23
How to Cite
Runnova, A. (2018). Mathematical Model of Pattern Selection for Complex Multichannel Data in EEG Processing. Information and Control Systems, (4), 39-44. https://doi.org/10.31799/1684-8853-2018-4-39-44
Issue
Section
System and process modeling