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Ear-EEG Sleep Monitoring 2023 (EESM23) data set **Overview** This dataset was collected as part of a research project on ear-EEG sleep monitoring which took place in 2020-2022. The data set contains nightly EEG recordings from 10 healthy participants ('subjects'). The first two recordings consist of polysomnogrpahy (PSG) measurements and ear-EEG measurements. The remaining ten recordings consist of only ear-EEG measurements, though a few subjects were asked to repeat a recording. Only t

Participants

10

Size

25.7 GB

EESM19: Ear-EEG Sleep Monitoring data set This data set was collected as part of development and quality assessment of the ear-EEG as a sleep monitoring platform. Data collection took place between 2018 and 2020. First publication was in 2019 (https://doi.org/10.1038/s41598-019-53115-3), hence the '19' in the name. The data set consists of 2 parts (a & b): a: 20 subjects who each spent 4 nights sleeping with a partial PSG (EEG, EOG and chin EMG electrodes), ear-EEG and a wristworn actigr

Participants

20

Size

268 GB

# Song Familiarity Twenty-nine participants listened to song melodies and responded as soon as the song felt familiar. Participants were then asked to identify the song, if possible (title, artist, or lyrics). Next, participants were shown a multiple choice display with four song titles, selected a song title, and were given visual feedback (correct: selected option turned green and a checkmark appeared next to the title; incorrect: selected option turned red and an x appeared next to the title

Participants

29

Size

7.13 GB

Data collection took place at the NeuroCognition Laboratory (NCL) in San Diego, California under the supervision of Dr. Phillip Holcomb and Dr. Karen Emmorey. This project followed the San Diego State University’s IRB guidelines.

Participants

48

Size

2.93 GB

Data collection took place at the NeuroCognition Laboratory (NCL) in San Diego, California under the supervision of Dr. Phillip Holcomb and Dr. Karen Emmorey. This project followed the San Diego State University’s IRB guidelines.

Participants

60

Size

3.57 GB

# Summary The human brain can rapidly recognize meaningful objects from natural scenes encountered in everyday life. Neuroimaging with large-scale naturalistic stimuli is increasingly employed to elucidate these neural mechanisms of object recognition across these rich and daily natural scenes. However, most existing large-scale neuroimaging datasets with naturalistic stimuli primarily rely on functional magnetic resonance imaging (fMRI), which provides high spatial resolution to characterize s

Participants

31

Size

147 GB

## The “Cognitive Electrophysiology in Socioeconomic Context in Adulthood” Dataset # Data Description This dataset comprises electroencephalogram (EEG) data collected from 127 young adults (18-30 years), along with retrospective objective and subjective indicators of childhood family socioeconomic status (SES), as well as SES indicators in adulthood, such as educational attainment, individual and household income, food security, and home and neighborhood characteristics. The EEG data were recor

Participants

127

Size

10.6 GB

Howdy y'all. Here's data from: Pirrung, C.J.H., Singh G., Hogeveen, J., Quinn, D. & Cavanagh, J.F. (2025) Hypoactivation of ventromedial frontal cortex in major depressive disorder: an MEG study of the Reward Positivity. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging An MEG study (306-sensor Elekta Neuromag System) of the Reward Positivity during reinforcement learning. Participants were all SCID interviewed to meet either control (CTL, non-depressed, n=38) or major depre

Participants

85

Size

162 GB

# Summary The human brain can rapidly recognize meaningful objects from natural scenes encountered in everyday life. Neuroimaging with large-scale naturalistic stimuli is increasingly employed to elucidate these neural mechanisms of object recognition across these rich and daily natural scenes. However, most existing large-scale neuroimaging datasets with naturalistic stimuli primarily rely on functional magnetic resonance imaging (fMRI), which provides high spatial resolution to characterize s

Participants

19

Size

14.1 GB

The "Podcast" ECoG dataset for modeling neural activity during natural story listening. We introduce the “Podcast” electrocorticography (ECoG) dataset for modeling neural activity supporting natural narrative comprehension. This dataset combines the exceptional spatiotemporal resolution of human intracranial electrophysiology with a naturalistic experimental paradigm for language comprehension. In addition to the raw data, we provide a minimally preprocessed version in the high-gamma spectral b

Participants

9

Size

3.24 GB