A BIDS-standardized example dataset of simultaneously recorded EEG and eye-tracking data from a single subject, drawn from the EEGEyeNet resource. EEGEyeNet was designed as a dataset and benchmark for eye movement prediction from EEG. This version reformats the original Open Science Framework data to the BIDS standard.
Electroencephalography recordings from 16 participants viewing rapid serial visual presentation streams of Gabor-like stimuli. The dataset was collected to study the temporal dynamics of visual feature coding and integration in perception. Stimuli were presented at 6.67 Hz and 20 Hz while participants performed an orthogonal fixation colour change detection task.
EEG recordings from 36 participants performing an orientation discrimination task in a visual tilt illusion paradigm. A central target grating, titrated to appear vertical on average, was surrounded by an annular grating tilted by ±30 degrees. The dataset supports analysis of prestimulus EEG correlates, including alpha oscillation power and phase, of increased or decreased tilt illusion strength.
Intracranial EEG (iEEG) recordings used to evaluate a flexible, scalable, high-channel-count micro-stereo-electrode for recording neural activity in the brain. The dataset includes raw and preprocessed (epoched) recordings from multiple species, including human patients, non-human primates, pigs and rodents. Direct electrical stimulation was used to examine the effects of stimulation on brain activity. Data are organized in iEEG-BIDS format, with binary files and channel maps in the derivatives folder.
A BIDS-standardized subset of the EEGEyeNet dataset containing simultaneously recorded EEG and eye-tracking data from a single subject. The original EEGEyeNet resource was designed as a dataset and benchmark for eye movement prediction from EEG. This version was reformatted to BIDS to support reuse and standardized analysis.
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.
This dataset contains synchronized EEG, ECG and pupil recordings from 36 young and 39 older adults performing auditory cued reaction time tasks, plus a passive listening condition. The active tasks were a cued simple reaction time task and a cued go/no-go task, and they allow investigation of age-related differences in cortical, autonomic and pupillary responses during anticipation and perceptual decision-making.
EEG recordings from healthy participants performing self-initiated palmar and lateral reach-and-grasp actions, acquired simultaneously across three recording systems: gel-based laboratory EEG, water-based mobile EEG, and dry-electrode mobile EEG. The dataset supports investigation of the feasibility of decoding natural reach-and-grasp movement-related cortical potentials across EEG acquisition modalities for brain-computer interface applications. It is distributed in BIDS format as part of the MOABB benchmark collection.
A 60-channel EEG dataset with EOG and EMG recordings from 22 healthy participants performing sit-to-stand and stand-to-sit transitions under motor execution and motor imagery conditions. Each participant completed two sessions, with about 20 trials per transition class. It is described as the first public EEG dataset to explicitly target postural transitions, and supports research on neural activation patterns and brain-computer interfaces for lower-limb movement. This BIDS release was reformatted from the original Zenodo deposit using MOABB.
The Bern-Barcelona database contains 7500 pairs of intracranial EEG signals (3750 focal and 3750 non-focal) from five patients with pharmacoresistant temporal lobe epilepsy. Each pair consists of two neighbouring channels, recorded for 20 s at 512 Hz and drawn from seizure-free intervals, and the pairs have been used to study nonrandomness, nonlinear dependence and nonstationarity of EEG. This BIDS release is a lossless repackaging of the original published text files into BrainVision format. Patient identity is not retained, so all runs are pooled under a single pseudo-subject.