…which can help with better alignment. Per OpenNeuro’s new formatting requirements…
High-density surface EMG (HDsEMG) recordings from the right tibialis anterior of a single healthy participant performing submaximal isometric ankle dorsiflexions at 10 to 70 percent of maximal voluntary contraction. Signals were acquired with two 64-electrode arrays (128 electrodes total) alongside force measurements. The dataset includes labeled motor unit spike trains derived from concurrent invasive EMG, and supports motor unit decomposition research.
…A small Python helper is provided to make alignment one line: ```python…
A longitudinal motor imagery EEG dataset from 18 BCI-naive participants recorded across six sessions (one offline, five online) with 22 EEG channels at 512 Hz. It supports the study of transfer learning for brain-computer interface skill acquisition, comparing unsupervised Generic Recentering and supervised Personally Assisted Recentering for calibration-free training. Participants performed left/right hand motor imagery with continuous visual feedback.
This dataset, named DataSet2, is an EEG dataset originally deposited on OpenNeuro (ds002833) and mirrored on NEMAR. It was contributed by Mheich and colleagues. The available documentation gives no details on the experimental paradigm or participants.
…res-2 template, ensuring consistent alignment across participants. Functional MRI preprocessing included…
A BIDS-formatted dataset of high-density surface EMG (HDsEMG) recordings from the right tibialis anterior of six healthy male participants. Four 64-channel grids (256 electrodes) recorded trapezoidal isometric dorsiflexion contractions at 30% and 50% of maximal voluntary contraction, together with force signals. The dataset supports studies of electrode grid design and motor unit identification.
A left/right-hand motor-imagery EEG dataset from 29 healthy participants recorded at the University of West Bohemia for brain-computer interface research. Participants performed cue-based, single-class imagery of left- or right-hand movement across four runs in one session. The data are re-released in BIDS format with HED event annotations via MOABB.
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.
Functional near-infrared spectroscopy (fNIRS) dataset from a mirror neuron study examining brain correlates of motor complexity during observed and executed actions. The recordings were collected at Boston University and are shared in BIDS format, mirrored from OpenNeuro.