A Simulation Model and Pattern Recognition Method of Electroencephalogram Driven by Expression

doi:10.7652/xjtuxb201606001

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A Simulation Model and Pattern Recognition Method of Electroencephalogram Driven by Expression

更新时间：2025-07-09

- A Simulation Model and Pattern Recognition Method of Electroencephalogram Driven by Expression
- Vol. 50, Issue 6, Pages: 1-8(2016)
- 作者机构：
  
  1. 西安交通大学现代设计及转子轴承系统教育部重点实验室,西安,710049
  2. 西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201606001
  CLC： TP911.7
- Online First：10 June 2016，
  
  Published：2016
- 稿件说明：
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A Simulation Model and Pattern Recognition Method of Electroencephalogram Driven by Expression[J]. 2016, 50(6): 1-8.
DOI：

A Simulation Model and Pattern Recognition Method of Electroencephalogram Driven by Expression[J]. 2016, 50(6): 1-8. DOI： 10.7652/xjtuxb201606001.

摘要

针对自发型脑电信号识别率低、个体差异度大等问题

提出了一种新的基于表情驱动脑电信号的脑机接口方式

并进行了建模仿真及实验验证。利用神经元集群模型对表情驱动脑电信号进行机理建模与仿真分析

得到自发表情的相关脑区及表情驱动脑电信号的频率分布特性; 提出了一种基于小波变换和人工神经网络模型映射的表情驱动下脑电信号分析识别方法

有效提高了表情驱动下脑电信号的识别率。从神经生理学角度验证了表情驱动脑电信号的特征来源是受大脑前额叶皮层和边缘系统相互协调共同控制的

并通过实验验证了所提脑电信号分类识别算法切实有效

其最高分类准确率可达85%。

Abstract

An expression driven brain-computer interface system is proposed to solve the low recognition rate and huge individual differences of spontaneous electroencephalogram(EEG)signal

and simulation and experimental verification are conducted. A neural mass model is used in mechanism modeling and simulation of expressions driven EEG signal

and the results show the related brain areas of facial expression and the frequency distribution characteristics of expression driven EEG signals. A classification and recognition method based on the wavelet transform and an artificial neural network is put forward

to improve the recognition rate of expression driven EEG signal. This research elaborates that it is the prefrontal cortex and the limbic system to cooperate on the generation and control of expression driven EEG signals

and verifies the feasibility of the classification and recognition method whose highest off recognition rate is 85%.

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references

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