Multimodal EEG, ECG, and video dataset of yoga practitioners during concentration and mind-wandering tasks
A. Kashevnik, Eduard Glekler, E. Artemenko, I. Brak, I. Shoshina, Vladimir Romaniuk
Scientific Data April 10, 2026 DOI: 10.1038/s41597-026-07209-z (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Dataset description Peer reviewed |
|---|---|
| Sample size | 49 |
| Population | 25 experienced yoga practitioners and 24 individuals without prior experience in yoga or other self-regulation practices |
| Duration | Single session of approximately 45 minutes |
| Key points | The authors present a synchronized, anonymized multimodal dataset of EEG, ECG, and video recordings from 49 participants, 25 experienced yoga practitioners and 24 without prior yoga or self-regulation experience, each completing five tasks in a single roughly 45-minute session. They propose the dataset may support research in attention, concentration, meditation, affective computing, and multimodal signal integration. |
Abstract
We present a multimodal dataset containing electroencephalography (EEG), electrocardiography (ECG), and video recordings from 49 participants. Each participant completed a single session lasting approximately 45 minutes. During each session they performed five tasks: resting state, inward concentration (focusing on the center of the forehead), outward concentration (visual search), and mind-wandering. EEG data were recorded from 64 channels at 2048 Hz; ECG and frontal video were collected simultaneously. The dataset includes 49 validated recordings: 25 from experienced yoga practitioners and 24 from individuals without prior experience in yoga or other self-regulation practices. The recordings are synchronized and structured for convenient use in cross-modal analyses. This dataset may support research in attention, concentration, meditation, affective computing, and multimodal signal integration. All data are anonymized and organized for easy access and reuse.