Neural Noise

Tactile-evoked EEG dataset for natural perception using an integrated stimulation–recording framework

2 min read

  • An EEG dataset was published with integrated stimulation–recording for tactile perception research.1
  • The framework supports sensory BCI and neural signal processing studies.1
  • The dataset is reusable for tactile and natural-perception paradigms.1 1

Weekly enrichment (2026-07-20)

  • The dataset (published in Nature’s Scientific Data, 2025) comprises 64-channel EEG recorded with a NeuSen W wireless system at 1000 Hz from 10 healthy right-handed male participants aged 20–26, using passive touch to three materials — artificial leather, artificial fur, and sandpaper — delivered by a custom rotating hexagonal-prism roller (radius 15 cm, alternating flat/curved surfaces).2
  • Both hands were tested; each hand’s session had 4 blocks of 54 trials (each trial 4 s: 2 s stimulation + 2 s rest, 3-minute inter-block rest), with each material repeated 18 times per block, giving 216 samples per hand per participant.2
  • P300 peak latency was significantly affected by stimulus type at channel Cz (p=0.012) and CP2 (p=0.0338), and P300 amplitude differed by stimulus type at Cz (p=0.036).2
  • A three-class CSP + SVM classifier (one-vs-rest, 6 spatial filters, 8-fold cross-validation) reached 54.2 ± 7.6% accuracy for the left hand and 52.5 ± 3.5% for the right — above the 33% chance level — with sandpaper the most reliably recognized material.2
  • The protocol was approved by the Tianjin University ethics committee (TJUE-2021-019); the authors note the all-male, narrow-age, right-handed sample limits generalizability.2
  • The data are openly available on Figshare (doi 10.6084/m9.figshare.30479234.v1), fully anonymized, and MATLAB preprocessing code (0.1–15 Hz band-pass, 0–2 s epoch extraction) is published on GitHub.34
  • The framework is positioned as a resource for tactile-evoked BCI development and assistive/aging-population medical devices, offering an alternative to visual- and auditory-based BCI paradigms.2

Footnotes

  1. https://news.google.com/rss/articles/CBMiX0FVX3lxTE5GV3VYbU1lWWswbTFiWDdvRS0tN2tqY2lJRFpCZ29DeUxKdzFQT0RuTEpmNEdCbDVvZE9nSU9CNXdueWptLTI0VlZ3aE5WUnRvUmlZaGV6WTY3MW9fY2Mw?oc=5 ↩ ↩2 ↩3 ↩4

  2. https://www.nature.com/articles/s41597-025-06250-8 ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  3. https://doi.org/10.6084/m9.figshare.30479234.v1 ↩

  4. https://github.com/liupeishuai/Code-for-Tactile-evoked-EEG-Dataset ↩

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