Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing
Abstract The “MEG-MASC” dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fictional stories from the Manually Annotated...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-12-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02752-5 |
| _version_ | 1797398228999602176 |
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| author | Laura Gwilliams Graham Flick Alec Marantz Liina Pylkkänen David Poeppel Jean-Rémi King |
| author_facet | Laura Gwilliams Graham Flick Alec Marantz Liina Pylkkänen David Poeppel Jean-Rémi King |
| author_sort | Laura Gwilliams |
| collection | DOAJ |
| description | Abstract The “MEG-MASC” dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fictional stories from the Manually Annotated Sub-Corpus (MASC) intermixed with random word lists and comprehension questions. We time-stamp the onset and offset of each word and phoneme in the metadata of the recording, and organize the dataset according to the ‘Brain Imaging Data Structure’ (BIDS). This data collection provides a suitable benchmark to large-scale encoding and decoding analyses of temporally-resolved brain responses to speech. We provide the Python code to replicate several validations analyses of the MEG evoked responses such as the temporal decoding of phonetic features and word frequency. All code and MEG, audio and text data are publicly available to keep with best practices in transparent and reproducible research. |
| first_indexed | 2024-03-09T01:21:35Z |
| format | Article |
| id | doaj.art-8cfe30f3477c4945956717aa916b40ee |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-09T01:21:35Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-8cfe30f3477c4945956717aa916b40ee2023-12-10T12:06:47ZengNature PortfolioScientific Data2052-44632023-12-011011910.1038/s41597-023-02752-5Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processingLaura Gwilliams0Graham Flick1Alec Marantz2Liina Pylkkänen3David Poeppel4Jean-Rémi King5Department of Psychology, Stanford UniversityDepartment of Psychology, New York UniversityDepartment of Psychology, New York UniversityDepartment of Psychology, New York UniversityDepartment of Psychology, New York UniversityDepartment of Psychology, New York UniversityAbstract The “MEG-MASC” dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fictional stories from the Manually Annotated Sub-Corpus (MASC) intermixed with random word lists and comprehension questions. We time-stamp the onset and offset of each word and phoneme in the metadata of the recording, and organize the dataset according to the ‘Brain Imaging Data Structure’ (BIDS). This data collection provides a suitable benchmark to large-scale encoding and decoding analyses of temporally-resolved brain responses to speech. We provide the Python code to replicate several validations analyses of the MEG evoked responses such as the temporal decoding of phonetic features and word frequency. All code and MEG, audio and text data are publicly available to keep with best practices in transparent and reproducible research.https://doi.org/10.1038/s41597-023-02752-5 |
| spellingShingle | Laura Gwilliams Graham Flick Alec Marantz Liina Pylkkänen David Poeppel Jean-Rémi King Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing Scientific Data |
| title | Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing |
| title_full | Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing |
| title_fullStr | Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing |
| title_full_unstemmed | Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing |
| title_short | Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing |
| title_sort | introducing meg masc a high quality magneto encephalography dataset for evaluating natural speech processing |
| url | https://doi.org/10.1038/s41597-023-02752-5 |
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