…raw Signal Processing ----------------- Classifiers: Convolutional Neural Network (CNN) Feature extraction: Sliding windows…
A multimodal EEG dataset of motor imagery (MI) recorded from 31 healthy participants over up to three sessions each. Participants imagined left- and right-hand grasping in response to visual cues while 64-channel EEG plus EOG were recorded at 1000 Hz. This is a MOABB-derived version of the Eye-BCI dataset, which also includes eye-tracking and high-speed video for ocular activity analysis across BCI paradigms.
…20 s and calculated the signal-to-noise ratio (SNR). There are…
This dataset contains 64-channel EEG recorded at 1000 Hz from 31 healthy participants performing a row/column P300 speller task with 4L and 5L grid sizes. Each participant completed three sessions. It is the P300 component of a multimodal eye-BCI collection that also includes eye-tracking and high-speed video for analyzing ocular activity across BCI paradigms. Events are annotated with HED tags as Target and NonTarget flashes.
…This delay cannot be estimated from the recorded signals: before the acquisition…
This dataset contains EEG recordings collected to study how expectation affects repetition suppression in nociception. It supports investigation of how the brain's responses to repeated painful stimuli are modulated by predictions about those stimuli. The data are organized in BIDS format.
Nejedly2020 multicenter iEEG graphoelement clips (MAYO) This BIDS dataset is converted from…
This dataset contains 64-channel EEG recorded from 31 healthy participants performing a two-class (left vs. right hand) motor imagery task. Each participant completed up to three sessions of 40 trials. The recordings are part of a multimodal collection that also includes eye-tracking and high-speed video, intended for analyzing ocular activity across BCI paradigms. Events are annotated with HED, and the data are organized in BIDS format via MOABB.
…The preprocessing pipeline of the EEG signals is as follows. First, a…
This dataset contains stereoelectroencephalography (sEEG) recordings from patients performing a forced two-choice response task in the epilepsy monitoring unit at Oregon Health & Science University. The data were analyzed in collaboration with the University of Oregon and were used to characterize movement-related oscillations in intracranial recordings. It is organized in BIDS format.