MEG and EEG Recordings of English and Russian Podcast Listening

Raw magnetoencephalography and electroencephalography recordings capture brain activity from 15 participants listening to podcasts in their native languages. The dataset was created by author neukym and was last updated on the platform in April 2026. It is described in a scientific publication with the DOI 10.1038/s41597-026-06579-8.

Use Cases

• Classify participant's native language (English vs. Russian) based on MEG/EEG signal patterns.
• Analyze temporal dynamics of auditory processing by aligning neural time-series data with podcast audio stimuli.
• Train multimodal models to correlate specific linguistic features from the podcast transcripts with concurrent brain activity recordings.
• Investigate individual differences in neural responses by comparing data across the 15 participants.

Strengths

• Includes data from 15 participants, providing a basis for individual-differences analysis.
• Combines two complementary neuroimaging modalities: magnetoencephalography and electroencephalography.
• Covers two distinct native language groups (English and Russian speakers) for cross-linguistic comparison.

Limitations

• Sample size of 15 participants is relatively small for robust population-level machine learning models.
• Specific details on recording parameters, preprocessing steps, and data quality metrics are not provided in the input.
• The geographic and demographic scope of the participant pool is unknown, which may limit generalizability.

Provenance

Source

Author neukym on Hugging Face, with a linked scientific publication.

Collection Method

Recordings of participants listening to podcasts in their native language.

Freshness

Platform record was last updated in April 2026.