Cross-subjects Emotions Classification from EEG Signals using a Hierarchical LSTM based Classifier

被引：9

作者：

Badicu, Bogdan ^{[1
]}

Udrea, Andreea ^{[1
]}

机构：

[1] Univ Politehn Bucuresti, Dept Automat Control & Syst Engn, Bucharest, Romania

来源：

2019 E-HEALTH AND BIOENGINEERING CONFERENCE (EHB) | 2019年

关键词：

EEG signals; LSTM neural networks; single subject and cross subjects emotions classification; RECOGNITION;

D O I：

10.1109/ehb47216.2019.8969881

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

This article focuses on cross subjects' emotions classification from electroencephalogram signals (EEG). We propose a hierarchical classifier based on Long Short Term Memory (LSTM) neural networks for this task. For model training and testing, we use the signals from SEED database. Cross subjects emotions classification into neutral, positive and negative achieved an accuracy of 80% when using the proposed method.

引用

页数：4

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