Discover datasets

TMNRED is an EEG dataset collected from 30 healthy, right-handed native Chinese speakers performing a natural reading task designed to investigate fuzzy semantic target identification. Participants read Chinese news headlines and short sentences containing target and non-target semantic items while EEG was recorded across 8 blocks of 400 trials each. The dataset supports research into semantic processing mechanisms during naturalistic reading in the Chinese language.

Participants

30

Channels

30 (10-10)

Citations

1

Size

17.4 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains behavioral events and intracranial electrophysiology recordings from a delayed free recall task with closed-loop electrical stimulation delivered during encoding, guided by a classifier trained on encoding data. Participants studied lists of visually presented words, performed arithmetic distractor tasks, and then freely recalled the words. Data were collected at multiple clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania, and represent a closed-loop stimulation extension of the FR1 and FR2 free recall datasets.

Participants

18

Size

34.7 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains preprocessed EEG data from Experiment 2 of a study investigating how changes in behavioral priority influence the accessibility of information held in working memory. The data are provided in BIDS format with accompanying HED annotations, derived from a related OpenNeuro dataset, and are intended to support analyses of working memory prioritization effects.

Participants

33

Channels

62 (10-10)

Size

10.4 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset contains EEG recordings collected during a computerized spatial span task designed to probe visuo-spatial working memory (VSWM) for sequences. The study examines theta and gamma band oscillatory dynamics during item-level sequence encoding and recall, and compares these processes between younger and older adults to investigate effects of healthy cognitive aging on VSWM.

Participants

62

Channels

65 (10-10)

Citations

1

Size

25.1 GB

Version

v1.0.0

Updated

Aug 19, 2026

Imported from OpenNeuro ds007262

Participants

20

Channels

19 (10-20)

Citations

1

Size

1.92 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains electroencephalographic (EEG) recordings from 24 participants performing object classification and passive viewing tasks in Experiment 1 of a study investigating how the brain represents object movement and aliveness. Participants classified static images of objects with varying capacities for movement (moving/non-moving) and animacy (alive/not alive) as either alive or capable of movement. The study demonstrates that movement capacity is a significant organizational principle in visual object representation, independent of animacy.

Participants

24

Citations

15

Size

82.2 GB

Version

v1.0.0

Updated

Jul 10, 2026

The Penn Electrophysiology of Encoding and Retrieval Study (PEERS) is a large-scale investigation of the behavioral and electrophysiological correlates of memory encoding and retrieval. The dataset comprises EEG recordings from over 300 subjects across three experiments (ltpFR, ltpFR2, and VFFR), totaling more than 7,000 ninety-minute memory testing sessions. Data were acquired using either 129-channel Geodesic Sensor Net or 128-channel BioSemi systems, providing a comprehensive resource for studying neural mechanisms of human memory.

Participants

364

Channels

125 (egi-geodesic)

Citations

9

Size

8.71 TB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains preprocessed EEG data from Experiment 1 of a study investigating how changes in behavioral priority influence the accessibility of information held in working memory. The data are formatted according to BIDS standards and are intended to support reanalysis of working memory encoding and retrieval dynamics using EEG-based decoding methods.

Participants

34

Channels

62 (10-10)

Size

10.7 GB

Version

v1.0.0

Updated

Aug 19, 2026

ROAMM is a large-scale multimodal dataset combining simultaneous EEG and eye-tracking recordings collected during naturalistic reading, with span-level mind-wandering annotations from 44 participants. It provides a benchmark for mind-wandering detection and EEG-to-text decoding, supporting research on attention-related degradation in language decoding from brain activity during naturalistic reading.

Participants

1

Channels

64 (10-10)

Citations

2

Size

46.6 GB

Version

v1.0.0

Updated

Aug 18, 2026

This dataset comprises 64-channel EEG recordings and behavioural ratings from 47 participants who read and evaluated 210 short English-language texts, including Haiku, Senryu, and non-poetic Control texts. Each text was rated on five subjective dimensions: Aesthetic Appeal, Vivid Imagery, Being Moved, Originality, and Creativity. The dataset supports research into the neural and cognitive processes underlying aesthetic and creative engagement with poetic language, and includes resting-state EEG segments recorded before and after the experimental session.

Participants

47

Channels

64 (10-10)

Size

45.4 GB

Version

v1.0.0

Updated

Aug 19, 2026