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Multimodal Facial Emotion Recognition Dataset (EEG + Thermal + Digital Image)
π Summary
This dataset contains multimodal recordings of 50 subjects across six emotional states:
- Happy
- Sad
- Anger
- Fear
- Surprise
- Neutral (Normal)
Each emotion is captured in three different modalities:
- EEG Data β 4-channel EEG signals recorded during emotion elicitation.
- Thermal Images β infrared thermal facial images.
- Digital Images β standard RGB facial images.
This dataset was created for the research project:
βMultimodal Facial Emotion Recognition by Correlating EEG, Thermal, and Digital Image Data.β
π Dataset Structure
EEG/ βββ happy/ βββ sad/ βββ anger/ βββ fear/ βββ surprise/ βββ neutral/ Thermal/ βββ happy/ βββ sad/ βββ anger/ βββ fear/ βββ surprise/ βββ neutral/ Digital_Image/ βββ happy/ βββ sad/ βββ anger/ βββ fear/ βββ surprise/ βββ neutral/
- Each subfolder contains subject-wise data samples for the corresponding emotion.
- Total size: 50 subjects Γ 6 emotions Γ 3 modalities.
- EEG files are provided in text/CSV format (per subject per emotion).
- Thermal and Digital images are stored in standard image formats (JPG/PNG).
π§ Usage
You can load this dataset with Hugging Face datasets:
from datasets import load_dataset
dataset = load_dataset("bibekram/emotion_multimodal")
print(dataset)
π‘ Applications
This dataset can be used for:
Multimodal Emotion Recognition
Human-Computer Interaction (HCI)
Affective Computing
BrainβComputer Interface (BCI)
Cross-modal feature correlation research
π License
This dataset is released under the CC BY 4.0 License.
You are free to use, modify, and share it, provided proper attribution is given.
π Citation
If you use this dataset, please cite:
Bibek Ram (2025). Multimodal Emotion Recognition Dataset (EEG + Thermal + Digital Image). Hugging Face.
BibTeX:
@dataset{bibekram2025multimodal,
author = {Bibek Ram},
title = {Multimodal Emotion Recognition Dataset (EEG + Thermal + Digital Image)},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/bibekram/emotion_multimodal}
}
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