Classification of EEG Signals Based on GA-ELM Optimization Algorithm

被引：1

作者：

Zhang, Weiguo ^{[1
]}

Lu, Lin ^{[2
]}

Belkacem, Abdelkader Nasreddine ^{[3
]}

Zhang, Jiaxin ^{[1
]}

Li, Penghai ^{[1
]}

Liang, Jun ^{[4
]}

Wang, Changming ^{[5
]}

Chen, Chao ^{[1
,6
]}

机构：

[1] Tianjin Univ Technol, Tianjin 300384, Peoples R China

[2] Tianjin Univ Technol, Zhonghuan Informat Coll, Tianjin 300380, Peoples R China

[3] UAE Univ, Dept Comp & Network Engn, Coll Informat Technol, Al Ain 15551, U Arab Emirates

[4] Tianjin Med Univ, Gen Hosp, Tianjin 300052, Peoples R China

[5] Capital Med Univ, Beijing Anding Hosp, Beijing Key Lab Mental Disorders, Beijing 100088, Peoples R China

[6] Tianjin Univ, Acad Med Engn & Translat Med, Tianjin 300072, Peoples R China

来源：

HUMAN BRAIN AND ARTIFICIAL INTELLIGENCE, HBAI 2022 | 2023年 / 1692卷

关键词：

BCI; Motor imagination; Motor observation; Hilbert huang transform; ELM;

D O I：

10.1007/978-981-19-8222-4_1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

There are many unpredictable problems in motion visualization and observation in BCI system, such as interference from external noise and visual fatigue of subjects. These problems seriously affect the performance of the whole BCI system. To solve this problem, this paper designed the experimental paradigm of imagination and observation, and built the eeg acquisition platform by combining UNITY and MATLAB. Ten healthy subjects participated in the experiment, which was divided into two stages: in the first stage, each subject was required to perform five experiments at the same time. In the second stage, after an interval of more than one month, the eeg signals of the 10 subjects were collected again (the same experimental paradigm). In pattern recognition and Hilbert huang transform time and frequency domain characteristics of extreme learning machine recognition classification based on genetic algorithm, and using the basic method of SVM algorithm and ELM comparison between the results and draw HHT and optimization algorithm of single collection of experiment acquisition signal has a significant effect, high classification rate can reach 85.3%.

引用

页码：3 / 14

页数：12