Алгоритм аппроксимации сигналов для классификации электроэнцефалограмм человека

Abstract

The article examines the problem of classification of electroencephalograms (EEG), where noise in the signals, caused by various factors, prevents effective analysis and interpretation of the data. The main goal of the study is to analyze the effectiveness of a signal approximation algorithm using a wavelet technique with the next piece-wise approximation in order to effectively remove noise and subsequently solve the problem of signal classification using a convolutional neural network. The classification accuracy of the proposed algorithm with a low-pass filter is compared at different cutoff frequencies. One of the key findings of the study is a two-step approach to signal processing. In the first stage, the model is trained on the raw data, and then the trained convolution kernel with the highest variance is applied to the original signals. This solves the problem of choosing the mother function. This approach aims to enhance the informative components in the signal. The second processing step involves applying the proposed approximation algorithm after the convolution, which complements the first step to create a comprehensive method. This method not only effectively reduces noise in the data, but also has high potential to improve the overall accuracy in solving EEG signal classification problems. Thus, the results of the study provide important practical and theoretical implications, highlighting the prospects for applying the proposed method in the field of signal analysis.