Multi-source domain adaptation for EEG emotion recognition based on inter-domain sample hybridization

Our proposed method aims to alleviate the difficulties of emotion recognition from the limited generalization ability of EEG features across subjects and sessions. Though adapting the multi-source domain adaptation and the sample hybridization method, the proposed method can effectively transfer the emotion-related knowledge of known subjects and achieve accurate emotion recognition on unlabeled subjects.

Authors

Institutions

Publication Details

Journal
Frontiers in Human Neuroscience
Published
2024-10-31
DOI
https://doi.org/10.3389/fnhum.2024.1464431
Citations
9
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
4.57

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-source domain adaptation for EEG emotion recognition based on inter-domain sample hybridization

Xu Wu, Xiangyu Ju, Sheng Dai, Ming Li et al.
9 citations
Frontiers in Human Neuroscience
Emotion and Mood Recognition
4.57
article

Multi-source domain adaptation for EEG emotion recognition based on inter-domain sample hybridization

Xu Wu, Xiangyu Ju, Sheng Dai, Ming Li, Xinyu Li
article en
9 citations

Abstract

Our proposed method aims to alleviate the difficulties of emotion recognition from the limited generalization ability of EEG features across subjects and sessions. Though adapting the multi-source domain adaptation and the sample hybridization method, the proposed method can effectively transfer the emotion-related knowledge of known subjects and achieve accurate emotion recognition on unlabeled subjects.

Frontiers in Human NeuroscienceVol. 18
National University of Defense Technology (CN)
National Natural Science Foundation of China
Reduced inequalities
Openalex Percentile: Top 5%
Emotion and Mood Recognition
4.57
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.