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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

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A synchronized multimodal neuroimaging dataset for studying brain language processing.

Shaonan Wang^1,2, Xiaohan Zhang^3,4, Jiajun Zhang^3,4

¹National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China. shaonan.wang@nlpr.ia.ac.cn.

Scientific Data

|September 30, 2022

Summary

This summary is machine-generated.

We created SMN4Lang, a multimodal neuroimaging dataset for brain language processing. It synchronizes functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data, offering rich linguistic annotations for advanced computational language model evaluation.

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Area of Science:

Neuroscience
Computational Linguistics
Cognitive Science

Background:

Studying brain language processing requires high-quality, synchronized neuroimaging data.
Existing datasets often lack multimodal synchronization or detailed linguistic annotations.

Purpose of the Study:

To introduce the Synchronized Multimodal Neuroimaging dataset for studying brain language processing (SMN4Lang).
To provide a comprehensive resource for investigating the dynamics of language comprehension.
To establish a benchmark for evaluating and improving computational language models.

Main Methods:

Acquired synchronized functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from 12 healthy volunteers.
Collected high-resolution structural (T1, T2), diffusion MRI, and resting-state fMRI data.
Provided rich linguistic annotations, including word frequencies, syntactic structures, and embeddings.

Main Results:

The SMN4Lang dataset is verified as high-quality through assessment indicators.
The synchronized fMRI and MEG data allow for studying the temporal and spatial dynamics of language processing.
The dataset includes extensive linguistic information from naturalistic story stimuli.

Conclusions:

SMN4Lang is a valuable resource for advancing the understanding of brain language processing.
The dataset facilitates research into the neural encoding of linguistic features.
SMN4Lang can serve as a benchmark for developing and refining computational language models.