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<p>Stories, Chirps, And Noisy Sentences (MEG-SCANS) is a dataset that provides raw magnetoencephalography (MEG) recordings from 24 German-speaking subjects. Each subject listened to approximately one hour of stimuli, including two audiobooks, sentences from the Oldenburger Matrix Sentence Test (OLSA) for Speech Reception Threshold (SRT) assessment, and short up-chirps used to assess MEG signal quality. The dataset includes MEG data, structural MRI (Magnetic Resonance Imaging) scans of the head, corresponding audio material (audiobooks, OLSA envelopes, and chirp stimuli), and behavioral audiogram and SRT results from hearing screenings. Organized according to the Brain Imaging Data Structure (BIDS), this resource offers a robust benchmark for large-scale encoding/decoding analyses of temporally-resolved brain responses to speech. Comprehensive Matlab and Python code are included to replicate key data validations, ensuring transparency and reproducibility.</p>

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MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps and Noisy Sentences

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月9日

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  • Till Habersetzer,
  • Svea Steuer,
  • Andreas Radeloff,
  • Bernd T. Meyer

摘要

Stories, Chirps, And Noisy Sentences (MEG-SCANS) is a dataset that provides raw magnetoencephalography (MEG) recordings from 24 German-speaking subjects. Each subject listened to approximately one hour of stimuli, including two audiobooks, sentences from the Oldenburger Matrix Sentence Test (OLSA) for Speech Reception Threshold (SRT) assessment, and short up-chirps used to assess MEG signal quality. The dataset includes MEG data, structural MRI (Magnetic Resonance Imaging) scans of the head, corresponding audio material (audiobooks, OLSA envelopes, and chirp stimuli), and behavioral audiogram and SRT results from hearing screenings. Organized according to the Brain Imaging Data Structure (BIDS), this resource offers a robust benchmark for large-scale encoding/decoding analyses of temporally-resolved brain responses to speech. Comprehensive Matlab and Python code are included to replicate key data validations, ensuring transparency and reproducibility.