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This dataset contains EEG recordings from 34 participants (one session each) who viewed 242 object images, 121 challenge and 121 control images, selected from comparisons between human behavioural performance and AlexNet. The study examines adaptive recruitment of cortex-wide recurrent processing during visual object recognition. The derivatives folder contains outputs of the decoding analyses, including time-resolved decoding accuracy matrices for object identity (.npz).

Participants

34

Channels

63 (10-10)

Citations

2

Size

61.2 GB

Version

v1.0.0

Updated

Jun 30, 2026

…Deep learning with convolutional neural networks for EEG decoding and visualization. Human…

Participants

14

Channels

128 (10-05)

Citations

78

HED

v8.4.0

Size

31.3 GB

Version

v1.0.3

Updated

Aug 18, 2026

THINGS-EEG2 is a large EEG dataset from 10 participants who viewed 16,540 training images and 200 repeatedly presented test images from the THINGS database. Images were shown in rapid serial visual presentation at 5 Hz while 63-channel EEG was recorded at 1000 Hz across four sessions per subject. The dataset supports modeling of human visual object recognition and includes resting-state recordings, the authors' preprocessed epochs, and stimulus images.

Participants

10

Channels

63 (10-10)

Citations

88

Size

241 GB

Version

v1.1.0

Updated

Jul 5, 2026

…While convolutional neural networks captured the early visual activity evoked by geometric…

Participants

21

Citations

13

Size

71.1 GB

Version

v1.0.0

Updated

Jun 28, 2026

A multimodal neuroimaging dataset (EEG, anatomical MRI and functional MRI) for studying the spatiotemporal dynamics of visual processing in the human brain, as described by the dataset title. This is a NEMAR-hosted copy of OpenNeuro dataset ds002814. The associated article was under review at the time of the record. The source README is an unfilled placeholder, so further study details are not available in the dataset documentation. Contact the corresponding authors (Seyed-Mahdi Khaligh-Razavi, Fatemeh Ebrahiminia) for more information.

Participants

21

Citations

7

Size

48.6 GB

Version

v1.0.0

Updated

Jun 21, 2026

…CNN, Convolutional Neural Network Feature extraction: EEG2Code bitwise decoding Cross-Validation ---------------- Evaluation…

Participants

12

Channels

32 (10-10)

Citations

18

HED

v8.4.0

Size

337 MB

Version

v1.0.3

Updated

Oct 8, 2026

The Finer-grained Affective Computing EEG Dataset (FACED) contains 32-channel scalp EEG from 123 healthy participants who watched 28 emotion-eliciting video clips targeting nine emotion categories (four negative, four positive, and neutral). Participants gave self-reported ratings on 12 dimensions, including discrete emotions, valence, arousal, liking, and familiarity. The dataset supports cross-subject affective computing and the development of EEG-based emotion recognition algorithms. This version is converted to BIDS with events annotated by video and emotion labels.

Participants

123

Channels

32 (10-10)

Citations

94

Size

31.4 GB

Version

v1.1.3

Updated

May 29, 2026

…Identification of perceived sentences using deep neural networks in EEG. Journal of…

Participants

56

Channels

64 (10-10)

Citations

2

Size

25.2 GB

Version

v1.0.0

Updated

Jun 23, 2026

EEG recordings from 13 healthy participants performing a copy-spelling task with a visual 6x7 matrix ERP speller. The speller was designed for learning from label proportions (LLP), an unsupervised, calibrationless classification approach for ERP-based brain-computer interfaces. Two stimulus sequences with different target/non-target ratios allowed LLP to reconstruct mean target and non-target responses without labeled data. In the original online study, 84.5% of characters were spelled correctly on average without prior calibration.

Participants

13

Channels

31 (10-10)

Citations

46

HED

v8.4.0

Size

5.15 GB

Version

v1.0.3

Updated

Oct 8, 2026

…Convolutional Neural Network (CNN), Pearson correlation - **Feature extraction**: CNN spatial filtering (8x1…

Participants

12

Channels

32 (10-10)

Citations

18

HED

v8.4.0

Size

160 MB

Version

v1.0.0

Updated

May 11, 2026