A high-density scalp EEG dataset acquired during brief naps after a visual working memory task

Abstract

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 this Data in Brief includes 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/.

Corticothalamic modelling of sleep neurophysiology with applications to mobile EEG

“…The data was recorded between 10:00 and 17:00 over 1 to 2 consecutive days at the City College of New York, totalling 41 recordings. Subjects took a high- or low-load cognitive task and then took a nap for 30 minutes, where their EEG would be recorded over 64 channels of EEG and 2 channels of EOG, sampled at 1.5 KHz [61]. The data includes sleep stages and 2528 sleep spindles manually annotated, according to the AASM system.…”

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load

“…The second dataset contains high-density scalp EEG recording from 20 healthy participants ( Mei et al, 2018 ). Each participant participated in two separate sessions and completed an H-VM and L-VM task.…”

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load

“…The second dataset contains high-density scalp electroencephalography (EEG) recording from 20 healthy participants (Mei et al, 2018). Each participant participated in two separate sessions and completed a high-and low-load visual working memory task.…”