A high-density scalp EEG dataset acquired during brief naps after a visual working memory task.
A high-density scalp EEG dataset acquired during brief naps after a visual working memory task.
复制标题
视觉工作记忆任务后短暂小睡期间获取的高密度头皮脑电图数据集。
DOI：
10.1016/j.dib.2018.04.073
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发表时间：
2018
期刊：
影响因子：
1.2
通讯作者：
Ellmore,TimothyM
中科院分区：
文献类型：
--
作者：
Mei,Ning;Grossberg,MichaelD;Ng,Kenneth;Navarro,KarenT;Ellmore,TimothyM
人们越来越有兴趣了解睡眠期间发生的特定神经事件，包括10至16 Hz（Hz）之间的特征性纺锤波振荡，与学习和记忆有关。睡眠期间的神经活动可以使用众所周知的头皮脑电图（EEG）方法来记录。虽然存在公开可用的睡眠EEG数据集，但大多数数据集仅包括在特定患者组中收集的几个通道，这些通道在临床环境中过夜评估睡眠障碍。本数据简报中描述的数据集包括22名参与者，他们每人在两天内参与EEG记录。该数据集包括睡眠阶段的手动注释和2528个手动注释的纺锤波。来自64个通道的信号在1 kHz下用高密度有源电极系统连续记录，而参与者在执行高负荷或低负荷视觉工作记忆任务后在声音衰减测试室内小睡30或60分钟，其中负荷在记录日之间是随机的。高密度EEG数据集与单通道或少通道数据集相比具有几个优点，包括最值得注意的是探索神经事件分布中的空间差异的机会，包括纺锤波是仅在少数通道上局部发生还是在许多通道上全局共同发生，纺锤波频率、持续时间和振幅是否随大脑半球和前后轴而变化，以及纺锤体出现的概率是否作为正在进行的缓慢振荡的相位的函数而变化。该数据集，沿着用于文件输入和信号处理的Python源代码，可通过链接https://osf.io/chav7/在开放科学框架中免费获得。
There is growing interest in understanding how specific neural events that occur during sleep, including characteristic spindle oscillations between 10 and 16 Hz (Hz), are related to learning and memory. Neural events can be recorded during sleep using the well-known method of scalp electroencephalography (EEG). While publicly available sleep EEG datasets exist, most consist of only a few channels collected in specific patient groups being evaluated overnight for sleep disorders in clinical settings. The dataset described in thisData in Briefincludes 22 participants who each participated in EEG recordings on two separate days. The dataset includes manual annotation of sleep stages and 2528 manually annotated spindles. Signals from 64-channels were continuously recorded at 1 kHz with a high-density active electrode system while participants napped for 30 or 60 min inside a sound-attenuated testing booth after performing a high- or low-load visual working memory task where load was randomized across recording days. The high-density EEG datasets present several advantages over single- or few-channel datasets including most notably the opportunity to explore spatial differences in the distribution of neural events, including whether spindles occur locally on only a few channels or co-occur globally across many channels, whether spindle frequency, duration, and amplitude vary as a function of brain hemisphere and anterior-posterior axis, and whether the probability of spindle occurrence varies as a function of the phase of ongoing slow oscillations. The dataset, along with python source code for file input and signal processing, is made freely available at the Open Science Framework through the link https://osf.io/chav7/.