FACE-DEC
…face_1_prep Decoding: face_2_dec RSA: face_3_rsa Statistical…
- Participants
- 21
- Citations
- 1
- Size
- 27.6 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
100 results for "neural decoding" · page 6 of 10 · ranked by relevance
…face_1_prep Decoding: face_2_dec RSA: face_3_rsa Statistical…
…CNN, Convolutional Neural Network Feature extraction: EEG2Code bitwise decoding Cross-Validation ---------------- Evaluation…
This dataset comprises magnetoencephalography (MEG) recordings of auditory single word recognition in human subjects. Participants listened to isolated words while neural activity was recorded, providing insights into the temporal dynamics of auditory word comprehension. The dataset is described in Gaston et al. (2022) 'Auditory word comprehension is less incremental in isolated words' published in Neurobiology of Language. The dataset includes raw MEG data, stimulus information, and associated metadata organized according to the Brain Imaging Data Structure (BIDS) standard. This is a NEMAR-converted version of OpenNeuro dataset ds004276.
…It may also be useful for benchmarking EEG analysis and neural decoding…
NeuroMorph is a high-temporal resolution MEG dataset designed for morpheme-based linguistic analysis, comprising recordings from 24 subjects totaling over 17 hours of data. Data were collected using a KIT/Yokogawa MEG system to investigate visual language and cognitive processing, with a focus on morphological structure in written language comprehension.
[ recordings collected to investigate the relationship between auditory streaming—the perceptual organization of sound sequences—and interoceptive awareness. The study examines how the brain processes complex auditory stimuli and integrates this information with internal bodily signals, contributing to our understanding of sensory integration and conscious perception.
…Minimal visible movement required - **Predictive decoding**: Intent detection before motion completion ## Known…
THINGS-EEG2 is a large-scale EEG dataset comprising recordings from 10 subjects viewing 16,540 distinct training images and 200 test images presented via rapid serial visual presentation at 5 Hz. The dataset includes 63-channel EEG data sampled at 1000 Hz across 4 sessions per subject, with approximately 32,540 training trials and 16,000 test trials, designed to support computational modeling of human visual object recognition. Stimuli are drawn from the THINGS database, and the dataset includes resting-state recordings and behavioral annotations for each trial.
This dataset comprises auditory neurophysiological recordings investigating the relationship between auditory streaming perception and interoceptive awareness. Participants engaged in auditory tasks while electrophysiological signals were recorded, enabling examination of how the brain processes complex auditory scenes and integrates bodily state information during perceptual organization.