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<p>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.</p>

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Multimodal EEG, ECG, and video dataset of yoga practitioners during concentration and mind-wandering tasks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月10日

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  • Alexey Kashevnik,
  • Eduard Glekler,
  • Elena Artemenko,
  • Ivan Brak,
  • Irina Shoshina,
  • Vladimir Romaniuk

摘要

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.