…BrainVision, IEEE_FLOAT_32, µV, bit-identical to the source arrays. - `*_ieeg…
EEG recordings from 24 healthy participants performing a visual P300 Brain-Computer Interface task based on the Brain Invaders video game, collected at GIPSA-lab (Grenoble) in 2013. The experiment compared a P300 BCI with and without adaptive calibration using Riemannian geometry. Each participant completed multiple sessions with randomized adaptive and non-adaptive runs, each with a training phase and an online phase.
…The deposited trials_P*.mat arrays match exactly this processed/epoched form…
A multi-paradigm motor-imagery EEG dataset recorded from healthy participants at Inner Mongolia University. It covers five body-part tasks (hand, foot, thumb, index finger, index-thumb pinch) with left/right laterality, performed under a Classic Arrow paradigm and a Cue-Execution dual-stage paradigm. This BIDS release is a public five-subject sample of a 244-participant dataset, recorded with a 64-channel Neuroscan system at 1000 Hz.
…float64 array (n_channels, 3000)}`. Each `info` file holds `{ch_names: [...]}`. Model…
A multi-modal dataset of 32-channel dry-electrode EEG recorded together with Virtual Glove hand-kinematic tracking during motor imagery and motor execution of three right-hand movements (open/close, wrist rotation, finger tapping). Eleven healthy participants completed a rest, motor imagery, motor execution sequence with eight repetitions of the triplet. This BIDS re-release was produced via MOABB from the published MOVING dataset.
EEG recordings from 12 healthy, BCI-naive participants performing kinesthetic motor imagery of upper-limb, elbow-centered tasks. The paradigm has 10 classes: rest, elbow flexion, and eight goal-directed daily-living tasks such as drawer opening, soup eating, weight lifting and combing. Data were recorded with a 17-channel EGI system at 1000 Hz. The dataset supports research on the versatility of EEG models for motor-imagery BCI control and rehabilitation.
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.
EEG recordings of kinesthetic motor imagery of a right-hand ball squeeze at three graded force levels (10%, 40% and 70% of maximal voluntary contraction) from 10 healthy participants. Data were acquired with a 14-channel Emotiv EPOC headset at 128 Hz, with five 5-second imagery cues per 40-second recording. The dataset supports characterization and classification of motor imagery force levels for brain-computer interface research. The associated paper (Journal of Neural Engineering, 2024) is by Martinez-Peon, Garcia-Hernandez, Benavides-Bravo and Parra-Vega.
NEMAR Dataset nm000106: handwriting - Handwriting movement detection from EMG