Cross-phase Emotion Recognition using Multiple Source Domain Adaptation

Ke-Ming Ding, Tsukasa Kimura, Ken-ichi Fukui, Masayuki Numao

2021

Abstract

EEG signal, the brain wave, has been widely applied in detecting human emotion. Due to the human brain’s complexity, the EEG pattern varies from different individuals, leading to low cross-subject classification performance. What is more, even within the same subject, EEG data also shows diversity for the same reason. Many researchers have conducted experiments to deal with the variance between subjects by transfer learning or domain adaptation. However, most of them are still low-performance, especially when the new subject does not share generality with training samples. In this study, we examined using cross-phase data instead of cross-subject data because the discrepancy of different phase data should be smaller than that of different subjects. Different phases represent data recorded multiple times from the same subject with the same stimuli. Two neural networks are adopted to verify the effectiveness of the cross-phase domain adaptation. As a result, experiments on the public EEG dataset showed approximation level accuracy compared to the state-of-the-art method but much lower standard derivation. Moreover, multiple source domains promote accuracy in contrast to one single domain. This study helps develop a more robust and high-performance real-time EEG system by transferring knowledge from previous data phases.

Paper Citation

in Harvard Style

Ding K., Kimura T., Fukui K. and Numao M. (2021). Cross-phase Emotion Recognition using Multiple Source Domain Adaptation. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS; ISBN 978-989-758-490-9, SciTePress, pages 150-157. DOI: 10.5220/0010200700002865

in Bibtex Style

@conference{biosignals21,
author={Ke-Ming Ding and Tsukasa Kimura and Ken-ichi Fukui and Masayuki Numao},
title={Cross-phase Emotion Recognition using Multiple Source Domain Adaptation},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS},
year={2021},
pages={150-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010200700002865},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS
TI - Cross-phase Emotion Recognition using Multiple Source Domain Adaptation
SN - 978-989-758-490-9
AU - Ding K.
AU - Kimura T.
AU - Fukui K.
AU - Numao M.
PY - 2021
SP - 150
EP - 157
DO - 10.5220/0010200700002865
PB - SciTePress