DreamerA: Multimodal EEG and ECG Signals for Affective Computing

170,246 examples of electroencephalogram (EEG) and electrocardiogram (ECG) signals recorded during affect elicitation using audio-visual stimuli. The data was captured with a 14-channel Emotiv EPOC headset at a sampling rate of 128 Hz and is part of the MONSTER project. It was uploaded to Hugging Face by author 'monster-monash' and last updated on April 14, -2025.

Use Cases

• Train binary classification models for affective states based on EEG and ECG signals.
• Develop multimodal fusion techniques for emotion recognition using synchronized EEG and ECG data.
• Benchmark signal processing algorithms on physiological data recorded at 128 Hz.
• Study the correlation between EEG and ECG signals in response to audio-visual stimuli.

Strengths

• Contains 170,246 examples, providing a substantial volume for model training.
• Includes synchronized EEG and ECG signals, offering a multimodal perspective.
• Recorded with a standardized 14-channel Emotiv EPOC headset at 128 Hz.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• The license is listed as 'Other', requiring careful review for usage restrictions.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Part of the MONSTER project, as referenced in arXiv:2502.15122.

Collection Method

EEG and ECG signals recorded during affect elicitation using audio-visual stimuli.

Time Range

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Freshness

Last updated 2025-04-14 06:02:35; freshness should be verified.

Geography

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