[论文解读] An Open-Access Multi-modal Dataset for Cognitive, Motor, and Cognitive-Motor Tasks

Zaineb Ajra, Grégoire Vergotte+6 more|arXiv (Cornell University)|Mar 24, 2026

Optical Imaging and Spectroscopy Techniques被引用 0

一句话总结

Presents an open-access multimodal dataset (EEG, fNIRS, ECG, behavioral, and subjective measures) collected from 30 healthy participants across seven cognitive, motor, and cognitive-motor tasks, stored in BIDS format and available on OpenNeuro. Includes initial validation showing task difficulty relates to neural patterns, especially in EEG channel-level data.

ABSTRACT

The incorporation of neuroimaging techniques such as electroenchephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has provided new opportunities for the analysis of dynamic brain processes involved in cognitive and motor functions. Despite the great contribution of the open-access neuroimaging datasets to neuroscience studies, they have mainly remained on a single modality and isolated task paradigms performed in a controlled environments. These limitations restrict the analysis of multi-task effects in real-world applications, thus creating a gap in the understanding of how cognitive and motor processes interact in daily life activities. To address these limitations, we present a multi-modal dataset containing neurophysiological (EEG, fNIRS), physiological (ECG), behavioral, and subjective measures collected from 30 healthy participants over three sessions. This dataset includes a hierarchical series of seven tasks ranging from single cognitive and motor activities, such as N-back, motor, passive motor, mental arithmetic and motor imagery, to combined cognitive-motor interactions simulating real life scenarios. This raw dataset provides a resource for developing advanced preprocessing methods and analysis pipelines, with potential applications in brain-computer interfaces, neurorehabilitation, and other fields requiring an understanding of multi-tasks brain dynamics. https://doi.org/10.18112/openneuro.ds007554.v1.0.0

研究动机与目标

• 通过将认知与运动任务结合在一个分层任务结构中，推动生态有效的多模态神经影像研究。
• 提供一个原始、公开可获取的数据集，以促进预处理管道与多模态分析方法的发展。
• 在真实世界情境下研究认知、运动及二者结合任务条件下的大脑-行为互动。

提出的方法

• 在三个会话中同时记录EEG（32通道）和fNIRS（前额叶与感觉运动皮层）。
• 收集生理数据（ECG）与行为数据（按键、Biodex扭矩）以及主观量测（KSS，任务难度）。
• 设计七个层级排列的任务：Mental Arithmetic、N-back、Motor Imagery、Passive Motor、Active Motor、NB-MA、以及Full NB-MA-Act-Mot。
• 使用Lab Streaming Layer对多模态数据流进行同步，生成带事件标记的对齐XDF数据。
• 以BIDS结构化格式提供数据（EEG .edf、fNIRS .snirf、生理/行为数据 .tsv/.json），不进行预处理。
• 提供验证分析（EEG/fNIRS预处理、RSA）作为基准，而非规定性分析管道。

Figure 1: Experimental paradigm overview

实验结果

研究问题

• RQ1 在独立与组合状态下，认知与运动任务如何影响EEG与fNIRS的神经信号？
• RQ2 EEG与fNIRS模式是否反映主观任务难度，且是否在全头或通道层面表现出差异？
• RQ3 原始多模态BIDS数据集是否支持开发预处理管道与多模态融合方法，用于认知-运动任务分析？
• RQ4 使用EEG、fNIRS与生理数据解码或表征认知-运动交互的可行性如何？

主要发现

• 主观任务难度在七种条件中呈系统性变化， multitask情形的难度高于单任务情形。
• EEG通道层面的RSA在若干额叶、中央与颞部通道的神经模式与任务难度之间显示显著相关性；全头EEG未显示此关系。
• fNIRS RSA在分组分析中未在O2Hb或HHb信号的通道层面经多重比较校正后呈现显著相关性，尽管数据质量支持通道层面解释。
• 预处理与验证管道（EEG PREP、clean_rawdata、Homer3、qt-nirs）可复现实验结果，展示原始数据在多样分析中的用途。
• 该数据集使在更真实的认知-运动情境中进行多变量分析、解码与BCI基准测试成为可能。

Figure 2: Configuration of the experimental fNIRS-EEG system. Left: picture of the head cap (rear view). Right: spatial layout of the 32-channel EEG electrodes arranged according to the international 10–20 system.