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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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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

Summary

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.