…Department of Computer Science, Machine Learning Group Address: 10587 Berlin, Germany Country…
Raw 128-channel EEG data from 20 observers viewing object images in a rapid presentation paradigm, together with representational dissimilarity matrices. The dataset supports time-resolved neural decoding analyses that separate objects' contextual associations from their perceptual and conceptual attributes. One participant was excluded because of a technical recording error, leaving 19 usable participants.
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.
This dataset provides behavioral events and intracranial EEG recordings from a categorized delayed free recall task. Participants studied visually presented word lists built from 25 semantic categories, completed arithmetic distractor problems, and then freely recalled the words. Each 12-word list contains pairs of same-category words from three categories. Data were collected at multiple clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania.
…The study compares two domain adaptation frameworks—Generic Recentering (unsupervised) and Personally…
…In the active learning group, participants completed a review session using the…
This dataset contains functional near-infrared spectroscopy (fNIRS) recordings collected during a word-color Stroop task using a sparse fNIRS montage. It accompanies a study comparing high-density, multi-distance fNIRS with sparse configurations for detecting task-related brain activity. The data are organized in BIDS format and are mirrored on NEMAR from OpenNeuro.
…For learning trial 1, participants are placed at a random location at…
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.
…nemar.on004515) Affective state reinforcement learning task in N=54 Community participants…