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Assessment and Communication for People with Disorders of Consciousness
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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
EEGaffective brain–computer interfaceemotion recognition and regulationinter-subject variability

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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.