Simultaneous Acquisition of EEG and NIRS during Cognitive Tasks for an Open Access Dataset

We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy subjects performed three standard cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) Go/No-go, and 3) verbal fluency tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results. For n-back (dataset A) and Go/No-go tasks (dataset B), event-related potential (ERP) analysis was performed, and spatiotemporal characteristics and classification results for “target” vs. “non-target” (dataset A) and “Go” vs. “No-go” (dataset B) are provided. Time-frequency analysis was performed to show the EEG power spectrum to differentiate the task-relevant activations. Spatiotemporal characteristics of hemodynamic responses are also shown. For the verbal fluency task (dataset C), the EEG power spectrum and spatiotemporal characteristics of hemodynamic responses are analyzed, and the potential merit of hybrid EEG-NIRS BCIs was validated with respect to classification accuracy. We expect that the dataset provided will facilitate performance evaluation and comparison of many neuroimaging analysis techniques.

本数据集为同步采集脑电图（electroencephalography, EEG）与近红外光谱（near-infrared spectroscopy, NIRS）的多模态脑成像开放获取数据集。26名健康受试者完成了三项标准认知任务：1）n-back任务（0-back、2-back与3-back范式），2）Go/No-go任务，3）言语流畅性任务。本数据集包含以下内容：1）原始实测数据，2）人口统计学数据，3）基础分析结果。针对n-back任务（数据集A）与Go/No-go任务（数据集B），本数据集开展了事件相关电位（event-related potential, ERP）分析，并提供了两类任务的相关结果：数据集A的“靶刺激”与“非靶刺激”分类结果及时空特征，数据集B的“Go”与“No-go”分类结果及时空特征。研究同时开展了时频分析，通过绘制EEG功率谱以区分任务相关脑激活模式，此外还展示了血流动力学响应的时空特征。针对言语流畅性任务（数据集C），本数据集分析了EEG功率谱与血流动力学响应的时空特征，并基于分类准确率验证了混合式EEG-NIRS脑机接口（Brain-Computer Interface, BCI）的潜在应用价值。我们期望本数据集能够为众多神经成像分析技术的性能评估与对比研究提供便利。

搜集汇总

背景与挑战

背景概述

该数据集是一个开放访问的多模态脑成像数据集，同步采集了26名健康受试者在执行n-back、Go/No-go和言语流畅性三种认知任务时的脑电图（EEG）和近红外光谱（NIRS）数据。数据集包含原始测量数据、人口统计信息以及基本分析结果，如事件相关电位、时间频率分析和血流动力学响应特征，旨在支持神经影像分析技术的性能评估和比较，并验证混合EEG-NIRS在脑机接口中的潜在优势。