Classification of Working Memory Performance from EEG with Deep Artificial Neural Networks

被引：3

作者：

Kwak, Youngchul ^{[1
]}

Song, Woo-Jin ^{[1
]}

Kim, Seong-Eun ^{[2
]}

机构：

[1] Pohang Univ Sci & Technol, Dept Elect Engn, Pohang, South Korea

[2] Hanbat Natl Univ, Dept Elect & Control Engn, Daejeon, South Korea

来源：

2019 7TH INTERNATIONAL WINTER CONFERENCE ON BRAIN-COMPUTER INTERFACE (BCI) | 2019年

基金：

新加坡国家研究基金会;

关键词：

Artificial neural network (ANN); EEG band power; working memory;

D O I：

10.1109/iww-bci.2019.8737343

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Individuals have different working memory performance and some studies investigated a relationship between working memory performance and electroencephalography (EEG) band power. In this paper, we study EEG features to classify low performance group and high performance group and find that the power ratio feature of alpha and beta is more separable than their absolute powers. We test a deep artificial neural network (ANN) using the power ratio feature to classify the low performance group and high performance group. Experimental results on the working memory tasks show that some subjects have quite low accuracies (<20%) and it results in a low average classification accuracy of 61%, but we can see a possibility in the estimation of working memory performance using EEG data.

引用

页码：149 / 151

页数：3

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