Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea.
Machine Learning Group, Berlin Institute of Technology, Berlin 10587, Germany.
Sci Data. 2018 Feb 13;5:180003. doi: 10.1038/sdata.2018.3.
We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) discrimination/selection response task (DSR) and 3) word generation (WG) tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results. For n-back (dataset A) and DSR tasks (dataset B), event-related potential (ERP) analysis was performed, and spatiotemporal characteristics and classification results for 'target' versus 'non-target' (dataset A) and symbol 'O' versus symbol 'X' (dataset B) are provided. Time-frequency analysis was performed to show the EEG spectral power to differentiate the task-relevant activations. Spatiotemporal characteristics of hemodynamic responses are also shown. For the WG task (dataset C), the EEG spectral power 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.
我们提供了一个开放获取的多模态脑成像数据集,其中包括同时进行的脑电图 (EEG) 和近红外光谱 (NIRS) 记录。26 名健康参与者执行了三个认知任务:1) n-back (0-、2- 和 3-back),2) 辨别/选择反应任务 (DSR) 和 3) 单词生成 (WG) 任务。提供的数据包括:1) 测量数据,2) 人口统计学数据,和 3) 基本分析结果。对于 n-back (数据集 A) 和 DSR 任务 (数据集 B),进行了事件相关电位 (ERP) 分析,并提供了“目标”与“非目标”(数据集 A) 和符号“O”与符号“X”(数据集 B)的时空特征和分类结果。进行时频分析以显示 EEG 频谱功率以区分任务相关激活。还显示了血流动力学响应的时频特征。对于 WG 任务 (数据集 C),分析了 EEG 频谱功率和血流动力学响应的时空特征,并验证了混合 EEG-NIRS BCI 在分类准确性方面的潜在优势。我们希望提供的数据集将有助于许多神经影像学分析技术的性能评估和比较。