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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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  • Application Information

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Problems solved by technology

In recent years, deep learning has also shown superiority over traditional machine learning methods, but it takes a lot of time and a lot of training data to maintain the performance of the model

Method used

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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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Embodiment Construction

[0035] Such as figure 1 with figure 2 As shown, an EEG emotion recognition system based on a learnable adjacency matrix includes the following steps:

[0036] Step 1. Data collection, processing and division: convert the collected EEG signals per second into M×N feature data matrix X∈R M×N , where M is the number of brain electrodes, and N is the number of segments in the frequency band; the data is divided, and a part of the data is used as a training set, and the other part of the data is used as a verification set.

[0037] In this embodiment, the specific process of data collection is as follows: the subject wears an EEG collection device, watches a video that can arouse different emotions, collects the corresponding EEG signal while watching the video, and performs an evaluation of the obtained EEG information. Noise reduction, preprocessing, feature data extraction. The EEG signal acquisition equipment adopts the 64-lead EEG acquisition equipment of NeuroScan Company...

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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 ...

Claims

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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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