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

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 subjec...

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Bibliographic Details
Published inE-Health and Bioengineering Conference (Online) pp. 1 - 4
Main Authors Badicu, Bogdan, Udrea, Andreea
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2019
Subjects
Accuracy
Biomedical engineering
Brain modeling
EEG signals
Electroencephalography
Long short term memory
LSTM neural networks
Neural networks
single subject and cross subjects emotions classification
Testing
Training
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Summary: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.
ISSN:2575-5145
DOI:10.1109/EHB47216.2019.8969881