Electroencephalogram classification method based on attention mechanism and convolutional neural network

Abstract

The present invention discloses an electroencephalogram classification method based on an attention mechanism and a convolutional neural network. Collected electroencephalograms of n of channels are set as Xn (t) and a segment of the electroencephalograms intercepted every 2 s are used as a classification sample Xn (t) for the electroencephalograms in a normal state and in an abnormal state; a short-time Fourier transform is performed on each channel in the classification sample Xn (t), and rows represent time domain and columns represent frequency domain to obtain n of time-frequency domain matrixes; elements in the time-frequency domain matrixes are complex numbers, each element in the time-frequency domain matrixes is subjected to module value taking to obtain a real-number domain STFTmatrix; the real-number domain STFT matrix is transformed into pictures with colors presenting amplitude sizes; and the transformed pictures are input into the ResNet-50 convolutional neural network for feature extraction to realize electroencephalogram classification. The method can greatly improve the classification accuracy of the electroencephalograms in the normal state and abnormal state ofepilepsy patients.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to an EEG signal classification method based on an attention mechanism and a convolutional neural network. Background technique [0002] An EEG (brain electrical signal) is a non-invasive tool that measures the electrical activity of the brain, which contains a wealth of information about brain function. Therefore, EEG signals are of great value in the diagnosis of brain diseases. Previous studies have shown that EEG signals are nonlinear, non-stationary random processes. Over the past few decades, various methods have been proposed to detect EEG signals. The methods used in these studies were also very diverse. At present, the accuracy rate of EEG signal classification of existing classification algorithms is not high enough, and the classification effect is different for different EEG signals, which is not universal. Contents of the invention [0003] Th...

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Problems solved by technology

At present, the accuracy rate of EEG signal classification of existing classification algorithms is not high enough, and the classification effect is different for different EEG signals, which is not universal

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