THE APPLICATION OF SPECTRAL ANALYSIS OF EEG DATA FOR THE IDENTIFICATION OF OPEN AND CLOSED EYE STATES

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The article examines the analysis of electroencephalogram (EEG) data for the classification of open and closed eye states using the Fast Fourier Transform (FFT). It is shown that this method demonstrates stable recognition accuracy at the level of 70-80% in distinguishing between open- and closed-eye states, demonstrating its effectiveness in classifying biomedical signals. General information about EEG is described, points for their reading, in particular about the “10-10 system”, information about the main types of brain rhythms is given. Modern methods for analyzing EEG data were also reviewed, highlighting three main approaches: spectral analysis, recurrence analysis, and machine learning methods. Software was developed for classification of information presented in the form of EEG time series obtained in the state of open and closed eyes. The software was developed in Python utilizing the PyRQA library.

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