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EEG-based affective brain-computer interfaces: recent advancements and future challenges
Yuxin Chen1, Yong Peng1,2, Jiajia Tang1
1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China.
Journal of Neural Engineering
|June 9, 2025
Summary
This review summarizes advancements in affective brain-computer interfaces (aBCI) using electroencephalogram (EEG) for emotion recognition and regulation. It highlights challenges and opportunities for real-world aBCI applications, focusing on mental health.
Area of Science:
- Neuroscience
- Computer Science
- Biomedical Engineering
Background:
- Affective Brain-Computer Interfaces (aBCI) decode emotions from brain signals for neural regulation, particularly for depression and anxiety.
- Electroencephalogram (EEG) is a key modality for capturing neural activity related to emotional states.
- Recent progress aims to enhance closed-loop EEG-based aBCI systems for improved emotion recognition and regulation.
Purpose of the Study:
- To systematically review current progress in EEG-based emotion recognition and regulation within closed-loop aBCI systems.
- To identify key challenges and future research directions for bridging the gap between laboratory research and practical aBCI applications.
- To provide a comprehensive overview for academia and industry stakeholders in the aBCI field.
Main Methods:
- A systematic literature review was conducted using Web of Science and related databases, identifying over 100 studies.
- Studies were analyzed based on experimental paradigms, emotion recognition methods across different scenarios, and applications in affective disorder diagnosis and regulation.
- The review also considered neural mechanisms and theoretical underpinnings of EEG-based emotion recognition and regulation.
Main Results:
- Advancements in EEG-based aBCI were summarized across six key areas: emotion elicitation, EEG data exploration, multimodal data fusion, cross-scene recognition, real-world scenario considerations, and affective disorder applications.
- Key challenges hindering practical aBCI deployment were identified.
- Future opportunities for aBCI development were outlined, focusing on essential technologies for real-world applications.
Conclusions:
- The review consolidates current practices and performance in EEG-based emotion recognition and regulation.
- Future research should address identified challenges to facilitate practical aBCI system deployment.
- Guidance is provided for focusing on critical aBCI technologies necessary for widespread adoption.
