Electroencephalogram emotion recognition method and system, computer equipment and wearable equipment

Abstract

The invention belongs to the technical field of crossing of machine learning and emotion recognition, and discloses an electroencephalogram emotion recognition method and system, computer equipment and wearable equipment, and aims to reduce the influence of non-emotion signals on emotion recognition by removing electroencephalogram signals generated at the beginning of video conversion and subtracting the average value of the signals from remaining data. The method comprises the steps of: extracting time-frequency domain features of the pre-processed electroencephalogram signals by using short-time Fourier transform; putting the features into a convolutional neural network for training, and extracting high-quality features; and performing hypergraph learning on the obtained features, constructing a hypergraph classifier model, and completing emotion classification and recognition. According to the invention, the time-frequency features of electroencephalogram signals are optimized by adopting a deep learning method, and then training and classification are carried out by using a hypergraph learning method for sampling, so that the training time is effectively shortened on the basisof improving the classification accuracy of hypergraph learning, the operation space is compressed, and the method is of great significance to design, research and development of portable wearable equipment.

Description

technical field [0001] The invention belongs to the cross technical field of machine learning and emotion recognition, and in particular relates to an EEG emotion recognition method, system, computer equipment, and wearable equipment. Background technique [0002] At present: Compared with traditional emotion recognition methods based on facial expressions, words and actions, the use of EEG for emotion recognition can conduct research on problems more directly and objectively. In modern life, a good emotional state is beneficial to people's physical and mental health, Family and work have a positive impact. Having a good mood can improve people's efficiency in study and work, while long-term depression often leads to serious mental illness, and even has serious adverse effects on family and society. Many studies It shows that many mental diseases, such as depression, will be manifested in EEG signals. Therefore, through the identification and evaluation of EEG emotions, peop...

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Problems solved by technology

[0006] The difficulty of solving the above problems and defects is: when deep learning wants to obtain higher accuracy, it often constructs a deeper neural network, but the deeper the neural network, the more difficult it is to solve the optimization problem, and its memory consumption is huge and the calculation is complicated. It will take a lot of time to find the optimal parameters, and the final result is often a local optimal solution

The EEG features extracted by traditional feature extraction methods are not ideal in hypergraph learning, especially in unsupervised hypergraph learning, where the classification accuracy is low, so the optimal selection of input data is a key point of hypergraph learning. difficulty

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