Electroencephalogram spatial-temporal feature learning and emotion classification method based on hybrid neural network

Abstract

The invention discloses an electroencephalogram spatial-temporal feature learning and emotion classification method based on a hybrid neural network. The method comprises the steps of collecting electroencephalogram signals of multiple channels; extracting PSD (power spectral density) features from the electroencephalogram signals of the multiple channels; converting the features into a two-dimensional mesh matrix sequence; dividing the sequence into a plurality of fragments Pj; establishing a CASC_CNN_LSTM model and a CASC_CNN_CNN model, jointly extracting deep spatial features and temporal features of the electroencephalogram signals from each fragment Pj through the CASC_CNN_LSTM model, and inputting the deep spatial features and temporal features extracted by the CASC_CNN_LSTM model into a softmax layer corresponding to the CASC_CNN_LSTM model to perform emotion category prediction; jointly extracting deeper spatial features of the electroencephalogram signals from each fragment Pjthrough the CASC_CNN_CNN model; and inputting the deeper spatial features extracted by the CASC_CNN_CNN model into a softmax layer corresponding to the CASC_CNN_CNN model to perform emotion categoryprediction. According to the invention, the emotion classification is more accurate.

Description

technical field [0001] The invention belongs to the technical field of deep learning applications, in particular to a method for learning EEG spatiotemporal features and emotion classification based on a hybrid neural network. Background technique [0002] Emotions play a vital role in human life, positive emotions may help increase the efficiency of our daily work, while negative emotions may affect our decision-making, concentration, and more. With the development of artificial intelligence technology, emotion recognition has become a hot spot in the field of affective computing and pattern recognition research. [0003] First, EEG signals have a very low signal-to-noise ratio and are easily interfered by various noises. Second, people are often only interested in EEG signals related to specific brain activities, but it is difficult to separate this signal from the background. Therefore, in order to determine and extract the part of the EEG signal that is related to a spe...

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Problems solved by technology

[0003] First, EEG signals have a very low signal-to-noise ratio and are susceptible to interference from various noises. Second, people are often only interested in EEG signals related to specific brain activities, but it is difficult to separate this signal from the background.

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