EEGMeditation: Multimodal Recordings of Yoga Practitioners During Cognitive Tasks

A multimodal dataset containing simultaneous electroencephalography, electrocardiography, and RGB video recordings from yoga practitioners and control participants. The data was collected during resting state, mind-wandering, and concentration tasks, with primary data in BrainVision format. The dataset was authored by alexeykashevnik and last updated on 2026-03-27.

Use Cases

• Train models to classify cognitive states (e.g., concentration vs. mind-wandering) based on EEG and ECG signals.
• Study the physiological synchronization between brain and heart activity during meditation tasks.
• Develop multimodal fusion techniques using time-aligned EEG, ECG, and video streams.
• Benchmark signal processing algorithms for artifact removal in naturalistic EEG recordings.

Strengths

• Contains simultaneous recordings of three data modalities (EEG, ECG, RGB video), enabling multimodal analysis.
• Data includes both a target group (yoga practitioners) and a control group for comparative studies.
• Recordings are structured around specific cognitive tasks (resting state, mind-wandering, concentration).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.
• The description references a full description on an external page, requiring navigation for complete details.

Provenance

Source

huggingface

Collection Method

Recordings from participants during laboratory tasks.

Freshness

Last updated 2026-03-27 07:35:30; freshness should be verified.