…applying fatigue-based driver performance prediction algorithms. Calibration data sets were designed…
EEG recordings from 12 healthy participants performing an offline 4-class code-modulated visual evoked potential (c-VEP) task with burst stimulation sequences at 100% amplitude depth. The dataset accompanies a study comparing burst c-VEP codes with m-sequences to improve classification accuracy, reduce calibration data and improve user experience in reactive brain-computer interfaces. Data were acquired with a 32-channel BrainProducts LiveAmp system at 500 Hz.
InsurgentCivilian is an EEG dataset with HED (Hierarchical Event Descriptors) annotations, contributed by Johnson, Gordon, Touryan, and King. The README describes it only as a placeholder, so detailed information on the task, participants, and acquisition is not provided.
A preprocessed derivative EEG dataset from BCI Competition 2020 Track 4, containing motor imagery recordings of three right-arm grasping tasks (cylindrical, spherical, lumbrical) from 15 healthy subjects across three sessions. It provides 60-channel EEG at 250 Hz with 450 trials per subject, and is intended for evaluating session-to-session transfer learning in brain-computer interfaces. Data were notch filtered and epoched to the 4 s motor imagery window, and re-hosted in BIDS format via MOABB.
…second cycle of the reward prediction task. - sub-108_ses-02_task…
Electrophysiological dataset recorded from the amygdalae of nine epilepsy patients undergoing preoperative invasive monitoring in the mesial temporal lobe. Participants viewed dynamic visual sequences of fearful faces (aversive condition) interleaved with neutral landscapes (neutral condition). The dataset provides intracranial EEG in BIDS format, together with neuronal spike times and waveforms in NIX format, enabling study of amygdalar responses to aversive stimuli from macroscopic field potentials to single-neuron firing.
[ with a 41-channel Neuroscan system at 500 Hz, yielding 33,600 trials. The dataset supports research on the stability of motor imagery decoding across days and subjects.