Emotion Recognition Based on a EEG–fNIRS Hybrid Brain Network in the Source Space DOI Creative Commons

Mingxing Hou,

Xueying Zhang, Guijun Chen

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 1166 - 1166

Published: Nov. 22, 2024

Background/Objectives: Studies have shown that emotion recognition based on electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) multimodal physiological signals exhibits superior performance compared to of unimodal approaches. Nonetheless, there remains a paucity in-depth investigations analyzing the inherent relationship between EEG fNIRS constructing brain networks improve recognition. Methods: In this study, we introduce an innovative method construct hybrid in source space simultaneous EEG-fNIRS for Specifically, perform localization derive signals. Subsequently, causal are established by Granger causality signals, while coupled formed assessing coupling strength The resultant integrated create space, which serve as features Results: effectiveness our proposed is validated multiple datasets. experimental results indicate approach significantly surpasses baseline method. Conclusions: This work offers novel perspective fusion emotion-evoked paradigm provides feasible solution enhancing performance.

Language: Английский

A review of hybrid EEG-based multimodal human–computer interfaces using deep learning: applications, advances, and challenges DOI
Hyung-Tak Lee, Miseon Shim,

Xianghong Liu

et al.

Biomedical Engineering Letters, Journal Year: 2025, Volume and Issue: unknown

Published: March 22, 2025

Language: Английский

Citations

0
Multimodal Machine Learning Analysis of fNIRS Signals Using LSTM and KNN Models for Cognitive States and Brain Activation Patterns Prediction DOI
Adrian Luckiewicz, Dariusz Mikołajewski, Radosław Roszczyk

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 275 - 288

Published: Jan. 1, 2025

Language: Английский

Citations

0
TPat: Transition pattern feature extraction based Parkinson’s disorder detection using FNIRS signals DOI
Türker Tuncer, İrem Taşçı, Burak Taşçı

et al.

Applied Acoustics, Journal Year: 2024, Volume and Issue: 228, P. 110307 - 110307

Published: Sept. 27, 2024

Language: Английский

Citations

2
Emotion Recognition Based on a EEG–fNIRS Hybrid Brain Network in the Source Space DOI Creative Commons

Mingxing Hou,

Xueying Zhang, Guijun Chen

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 1166 - 1166

Published: Nov. 22, 2024

Background/Objectives: Studies have shown that emotion recognition based on electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) multimodal physiological signals exhibits superior performance compared to of unimodal approaches. Nonetheless, there remains a paucity in-depth investigations analyzing the inherent relationship between EEG fNIRS constructing brain networks improve recognition. Methods: In this study, we introduce an innovative method construct hybrid in source space simultaneous EEG-fNIRS for Specifically, perform localization derive signals. Subsequently, causal are established by Granger causality signals, while coupled formed assessing coupling strength The resultant integrated create space, which serve as features Results: effectiveness our proposed is validated multiple datasets. experimental results indicate approach significantly surpasses baseline method. Conclusions: This work offers novel perspective fusion emotion-evoked paradigm provides feasible solution enhancing performance.

Language: Английский

Citations

1