EEG Dataset for Emotion Classification Using Low-Cost and High-End Equipment

Description:This dataset contains electroencephalography (EEG) signals recorded during an emotion classification experiment using two devices: a high-end professional EEG system (BrainVision) and a low-cost brain-computer interface (Emotiv EPOC+). Data were collected from 20 participants while they were exposed to visual stimuli from the International Affective Picture System (IAPS), designed to induce four emotional states based on Russell’s valence-arousal model:

HVHA: High valence, high arousal

HVLA: High valence, low arousal

LVHA: Low valence, high arousal

LVLA: Low valence, low arousal

The dataset includes raw EEG recordings, preprocessed signals, and extracted features for further analysis. Additionally, a README file provides detailed information on the data structure, device configurations, and emotional labels assigned to each signal segment.

Data Format:

Raw EEG data: Original signal recordings (.edf, .csv, .mat).

Preprocessed EEG: Filtered and normalized data (.csv, .mat).

Extracted features: Set of statistical, spectral, and entropy-based features used in the study (.csv, .xlsx).

Metadata: Participant information (anonymized), experimental details, and emotional condition labels (.txt, .json).

Usage and Applications:This dataset can be used for research in neuroscience, emotion classification, artificial intelligence, machine learning, and EEG signal processing. It is particularly suitable for developing and validating machine learning and deep learning models applied to emotion recognition from brain signals.

License & Accessibility:The dataset is publicly available under the Creative Commons Attribution (CC BY 4.0) license, allowing free use, distribution, and modification with proper attribution.

Recommended Citation:If you use this dataset in your research, please cite the associated publication:

Sánchez-Reolid, R., Martínez-Sáez, M. C., García-Martínez, B., Fernández-Aguilar, L., Ros, L., Latorre, J. M., & Fernández-Caballero, A. (2022). Emotion classification from EEG with a low-cost BCI versus a high-end equipment. International Journal of Neural Systems, 32(10), 2250041. World Scientific. https://doi.org/10.1142/S0129065722500411