This fNIRS (functional near-infrared spectroscopy) dataset examines how morphine affects cortical hemodynamic responses to painful electrical stimulation compared with placebo. Recordings target the medial frontopolar cortex (medial BA 10) in healthy participants. The dataset supports research on objective, neuroimaging-based markers of analgesic drug effects.
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.
EEG recordings from 18 healthy participants performing a gaze-independent brain-computer interface task based on covert spatial attention shifts. Participants answered yes/no questions by shifting attention to a green or red cross while maintaining central fixation, and N2pc-related ERP responses were classified with canonical correlation analysis. The data were recorded with 30 EEG channels and 2 EOG channels at 250 Hz.
Study P of the bigP3BCI collection, an open, machine-learning-ready P300-based brain-computer interface dataset. It contains 32-channel EEG recorded at 256 Hz from 19 participants with ALS, each over two sessions, while they used a 9x8 character grid speller with predictive and non-predictive spelling paradigms. Target and non-target flash events are annotated with HED tags, and the data are reformatted from the original PhysioNet release into BIDS.
An example functional near-infrared spectroscopy (fNIRS) dataset from five participants performing a finger tapping task, organized according to the draft fNIRS BIDS specification. Participants tapped their left or right thumb to fingers, or rested as a control, in response to auditory cues, with sensors placed over the motor cortex and short channels attached. The dataset demonstrates conversion of NIRx recordings to BIDS using MNE-BIDS.
EEG recordings from 10 persons with cervical spinal cord injury performing attempted arm and hand movements: hand open, palmar grasp, lateral grasp, pronation and supination. Sixty-one EEG channels and 3 EOG channels were recorded across 9 runs per participant. Low-frequency time-domain EEG (movement-related cortical potentials) was used to decode the five movement classes offline, and an online proof-of-concept for self-paced movement detection was also run. The dataset supports research on neuroprosthetic and upper-limb control brain-computer interfaces.
Electrocorticography (ECoG) recordings from 20 C57BL/6 mice in three cohorts: blinking light only, transcranial diagnostic ultrasound (tDUS) only, and combined tDUS with light. The dataset was collected to test how tDUS modulates the visual cortex's response to external visual stimulation. It includes raw, event-triggered, and processed ECoG data.
EEG recordings from 9 healthy, BCI-naive participants performing two-class motor imagery (left vs. right hand), originally released as BCI Competition 2008 (IV) Graz data set B (Dataset 2b). Each participant completed five sessions: two screening sessions without feedback and three sessions with smiley visual feedback. Three bipolar EEG channels (C3, Cz, C4) and three EOG channels were recorded at 250 Hz.
A multi-modal neuroimaging dataset combining structural MRI (T1w), magnetoencephalography (MEG), and EEG recorded during electrical median nerve stimulation at the right wrist. Participants responded to each stimulus by lifting the left index finger as quickly as possible, allowing study of somatosensory and motor responses. Anatomical fiducials were digitized to align MEG with MRI, and event triggers mark stimuli and finger-lift responses.
The MNE-Sample-Data dataset is a simultaneous MEG and 60-channel EEG recording with an accompanying structural MRI, distributed with the MNE software. Checkerboard patterns were presented to the left and right visual fields, interleaved with tones to the left or right ear, and the subject responded to occasional smiley faces. It serves as a standard example dataset for demonstrating MEG/EEG analysis workflows, and FreeSurfer-derived anatomical surfaces are included.