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Related Experiment Video

Updated: May 1, 2026

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

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EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts.

Min-Ho Lee1, Adai Shomanov1, Balgyn Begim1

  • 1Nazarbayev University, Department of Computer Science, Astana, 010000, Republic of Kazakhstan.

Scientific Data
|September 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces the first public multimodal emotion dataset (EEG, audio, video) from 42 participants, enabling AI models to better understand human emotions in conversations.

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Area of Science:

  • Neuroscience and Machine Learning
  • Human-Computer Interaction
  • Affective Computing

Background:

  • Understanding human emotions is crucial for developing advanced human-machine interfaces.
  • Human behavior and psycho-physiological responses are influenced by social interactions and perceptual inputs.
  • Comprehending brain functions and behavior can accelerate the development of AI with human-like capabilities.

Purpose of the Study:

  • To introduce a novel multimodal emotion dataset for research.
  • To facilitate the development of AI models capable of recognizing human emotions.
  • To advance the understanding of the human emotional process from both neuroscience and machine learning perspectives.

Main Methods:

  • Collected multimodal data including 30-channel electroencephalography (EEG), audio, and video recordings from 42 participants.
  • Participants engaged in cue-based conversations, eliciting five emotions: neutral, anger, happiness, sadness, and calmness.
  • Evaluated baseline emotion recognition performance for each modality using deep neural network (DNN) methods.

Main Results:

  • The study created the Emotion in EEG-Audio-Visual (EAV) dataset, the first public resource of its kind.
  • The dataset comprises 8,400 interactions from 42 participants, covering listening and speaking roles.
  • Baseline performance evaluation using DNNs established initial benchmarks for emotion recognition across modalities.

Conclusions:

  • The EAV dataset is a significant contribution to emotion recognition research, particularly within a conversational context.
  • This resource is expected to advance AI models' ability to understand and replicate human emotional states.
  • The dataset supports interdisciplinary research bridging fundamental neuroscience and applied machine learning.