ArticleScientific data2022
A synchronized multimodal neuroimaging dataset for studying brain language processing.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- Unraveling the Complexity of Multilingual Comprehension: Neuroimaging and Linguistic Profiling in 700+ Adults.Scientific data · 2026Article
- The Brain, Body, and Behavior Dataset (BBBD): Multimodal Recordings during Educational Videos.Scientific data · 2026Article
- MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps and Noisy Sentences.Scientific data · 2025Article
- An EEG Dataset on Aesthetic and Creative Judgments of Brief Structured Poetry.Scientific data · 2025Article
- Naturalistic fMRI and MEG recordings during viewing of a reality TV show.Scientific data · 2025Article
- Le Petit Prince (LPP) multi-talker: Naturalistic 7 T fMRI and EEG dataset.Scientific data · 2025Article
- Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli.Scientific data · 2025Article
- Le Petit Prince Hong Kong (LPPHK): Naturalistic fMRI and EEG data from older Cantonese speakers.Scientific data · 2024Article
- ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding.Scientific data · 2024Article
- Computational Language Modeling and the Promise of In Silico Experimentation.Neurobiology of language (Cambridge, Mass.) · 2024Article
- A synchronized multimodal neuroimaging dataset for studying brain language processing.Scientific data · 2022Article
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4 authors.
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Abstract
We present a synchronized multimodal neuroimaging dataset for studying brain language processing (SMN4Lang) that contains functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data on the same 12 healthy volunteers while the volunteers listened to 6 hours of naturalistic stories, as well as high-resolution structural (T1, T2), diffusion MRI and resting-state fMRI data for each participant. We also provide rich linguistic annotations for the stimuli, including word frequencies, syntactic tree structures, time-aligned characters and words, and various types of word and character embeddings. Quality assessment indicators verify that this is a high-quality neuroimaging dataset. Such synchronized data is separately collected by the same group of participants first listening to story materials in fMRI and then in MEG which are well suited to studying the dynamic processing of language comprehension, such as the time and location of different linguistic features encoded in the brain. In addition, this dataset, comprising a large vocabulary from stories with various topics, can serve as a brain benchmark to evaluate and improve computational language models.
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