Electroencephalogram emotion recognition system based on learnable adjacent matrix

An adjacency matrix and emotion recognition technology, which is applied in the field of EEG emotion recognition, can solve problems such as models that consume a lot of time, and achieve the effects of convenient construction, improved emotion recognition effect, and low learning difficulty

Pending Publication Date: 2022-02-25
MEI HOSPITAL UNIV OF CHINESE ACAD OF SCI
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In recent years, deep learning has also shown superiority over traditional machine learning metho

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  • Electroencephalogram emotion recognition system based on learnable adjacent matrix
  • Electroencephalogram emotion recognition system based on learnable adjacent matrix
  • Electroencephalogram emotion recognition system based on learnable adjacent matrix

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Example Embodiment

[0035] Such as figure 1 and figure 2 As shown, a brain eM emotional identification system based on the learning neighboring matrix includes the following steps:

[0036] Step 1, the acquisition, processing, and division of the data: convert the extracted EEG signal into M × N feature data matrix x∈R M×N Where m is the number of brain electrodes, n is the number of segments of the frequency band; data division, part of the data as a training set, and another part of the data is a validation set.

[0037] In the present embodiment, the specific process of data acquisition is: the subject head wears the EMG collection device, viewing a video that can cause different emotions, to view the corresponding eElectronic signal when viewing the video, and performs the obtained EEG information Noise reduction, pretreatment, extraction characteristics. EEG signal acquisition equipment adopts NEUROSCAN's 64-director-e-electric collection equipment, because the data collected by the partial elec...

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Abstract

The invention discloses an electroencephalogram emotion recognition system based on a learnable adjacency matrix. The electroencephalogram emotion recognition system comprises the following steps: step 1, data collection, processing and division: converting collected electroencephalogram signals per second into M * N feature data, dividing data, taking a part of data as a training set, and taking the other part of data as a verification set; step 2, construction of an adjacent matrix: constructing an initial adjacent matrix A which belongs to RM * M according to an electrode distribution diagram, wherein M is the number of brain electrodes, and in the adjacent matrix A, for a certain electrode i and any other electrode j, Aij is equal to 1 when i is adjacent to j, Aij is equal to 0 when i is not adjacent to j, and an adjacency relation between any other electrode and the electrode is that Ai is equal to 1; step 3, training of a data input model; and 4, inputting of a to-be-tested sample into the model, and outputting of emotion corresponding to the electroencephalogram signals by the model. The system has the advantages that model learning difficulty is low, and emotion recognition effect can be improved.

Description

technical field [0001] The invention relates to the technical field of EEG emotion recognition, in particular to an EEG emotion recognition system based on a learnable adjacency matrix. Background technique [0002] Emotion recognition plays a key role in human perception, reasoning, decision-making, social interaction and behavioral choices. Human emotions should be taken into account when building more friendly and humanized human-computer interaction systems, including intelligent machines that can sense, recognize, and understand human emotions. The first step toward this goal is emotion recognition, an interdisciplinary technique that combines physiology, neuroscience, and computer science. [0003] Traditional emotion recognition methods use facial expressions, language, and physical actions to infer people's emotions. Although these signals are easy to collect, because people of different cultures and backgrounds have different expression habits, it is difficult to ...

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Application Information

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IPC IPC(8): A61B5/16A61B5/372A61B5/378
CPCA61B5/165A61B5/378A61B5/372A61B5/7203A61B5/7225A61B5/7264
Inventor 李劲鹏金明李主南陈昊蔡挺
Owner MEI HOSPITAL UNIV OF CHINESE ACAD OF SCI
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