This dataset contains EEG recordings from participants viewing stereoscopic scenes in a mixed reality environment, collected to characterize visual fatigue. Participants watched 2 resting scenes and 15 movement scenes, each followed by a rating scene. The data support both resting-state and task-state analyses.
Stereo-EEG recordings from six adult male patients with drug-resistant epilepsy performing a visual awareness task with saccadic report. Near-threshold Gabor gratings were presented while patients reported perception via saccade direction, enabling study of prefrontal cortex involvement in the emergence of visual awareness. The dataset includes per-trial behaviour, defaced T1 MRI, and source data with post-implantation CT and eye-position/behaviour files from patients and 10 healthy controls.
EEG dataset from a visual working memory experiment in which participants judged whether a probe grating was rotated clockwise or counterclockwise relative to a memorized orientation. Color cues either predicted a 90° segment of possible orientations or gave no information about orientation. The dataset supports investigation of how diffuse predictions stabilize and reshape neural codes during memory encoding.
Stereo-EEG recordings from a single drug-resistant epilepsy patient with a right posterior temporal periventricular nodular heterotopia (PVNH). High-frequency (43 Hz) stimulation of the PVNH elicited autoscopic hallucinations, and single-pulse electrical stimulation was used to map effective connectivity. The dataset supports multi-modal connectivity analysis of the network involving the PVNH, occipital and temporal neocortex, fusiform gyrus and parietal cortex.
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).
Stereo-EEG depth recordings from 11 patients with medically refractory temporal lobe epilepsy, collected at Sanbo Brain Hospital in Beijing. Recordings cover learning of object-location associations and targeted memory reactivation (TMR) with sounds during NREM sleep. The data were used to study electrophysiological signatures of variability in human memory consolidation. They are re-packaged in iEEG-BIDS from the authors' Zenodo release without filtering, resampling or re-referencing.
Intracranial EEG (stereo-EEG) recordings from 22 patients with drug-resistant epilepsy undergoing presurgical evaluation, capturing responses to 115 high-frequency intracranial electrical stimulations that evoked visual hallucinations. Each recording spans 21 seconds around the stimulation, with events annotated by stimulated contact pair, current intensity, and evoked effect, classified into 14 clinical categories. The data support identification of brain networks underlying visual symptoms and creation of symptom-related activation and connectivity maps.
Electroencephalography recordings from 50 participants viewing 22,248 images spanning 1,854 object concepts from the THINGS database. Images were shown in rapid serial visual presentation streams at 10 Hz while participants performed an orthogonal fixation colour change detection task. The dataset supports research on the temporal dynamics of object recognition and visual representation in the human brain.
This dataset provides simultaneous EEG and fNIRS recordings from 12 participants performing a silent naming task and three sensory-based imagery tasks (visual, auditory, tactile) on objects from the semantic categories of animals and tools. It supports research on semantic decoding and brain-computer interfacing. Stimulus images and example Python scripts for loading the data with MNE and MNE-BIDS are included.
NOD-EEG is an electroencephalography dataset of human responses to naturalistic ImageNet object images. It extends the Natural Object Dataset-fMRI (NOD-fMRI) by recording EEG, and MEG in a companion dataset, from the same subjects viewing the same stimuli. Together the three modalities support study of object recognition with both high spatial and high temporal resolution. The dataset includes raw BIDS data, preprocessed continuous data, epoched data, and detailed trial-level metadata.