EEG Responses to Vibrotactile Stimulation on Arms from 18 Human Subjects

18 human subjects had electroencephalography (EEG) responses recorded from a 64-channel system during vibrotactile stimulation on the arms. The data was used to predict tactile locations using a linear classifier and an artificial neural network, finding the beta (13–30 Hz) and high-gamma (50–100 Hz) frequency bands were most predictive. The dataset, authored by Yan Gai and hosted on Harvard Dataverse, was last updated on July 1, 2026.

Use Cases

• Decoding tactile location from EEG signals based on power in beta and gamma frequency bands.
• Training classifiers for somatosensory-evoked potential analysis using the described linear classifier and neural network methods.
• Investigating the differential role of frequency bands in tactile perception across different body locations.
• Benchmarking EEG-based decoding algorithms for vibrotactile stimulation experiments.

Strengths

• Data originates from a controlled experiment with 18 human subjects.
• EEG recordings were made using a 64-channel system, suggesting detailed spatial sampling.
• Analysis identified specific predictive frequency bands (beta 13–30 Hz and high-gamma 50–100 Hz).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The associated manuscript is described as under review, so the final published context is pending.

Provenance

Source

Harvard Dataverse

Collection Method

Recorded electroencephalography (EEG) responses from human subjects during vibrotactile stimulation.

Freshness

Last updated 2026-07-01 15:22:21; freshness should be verified.