Home // ACHI 2021, The Fourteenth International Conference on Advances in Computer-Human Interactions // View article
Authors:
Rafal Chalupnik
Katarzyna Bialas
Ireneusz Jozwiak
Michal Kedziora
Keywords: EEG; emotion recognition; machine learning; data acquiring
Abstract:
The aim of the paper was to analyze how to acquire and process EEG data with a simplified, commercially applicable EEG interface and to check whether it is possible to recognize human emotions with it. The EEG data gathering station was built and the data was gathered from the subjects. Then, the data was processed to apply it to the classifier training. The AutoML software was used to find the best ML model, and it was also built manually to prove the output accuracy was reliable and there was no overfitting. The AutoML experiment has shown that the best classifier was the boosted decision tree algorithm, and building it manually resulted in an accuracy of recognizing four distinct emotions equal to 99.80%.
Pages: 97 to 103
Copyright: Copyright (c) IARIA, 2021
Publication date: July 18, 2021
Published in: conference
ISSN: 2308-4138
ISBN: 978-1-61208-870-9
Location: Nice, France
Dates: from July 18, 2021 to July 22, 2021