…adaptive calibration using Riemannian geometry; randomised order of runs (adaptive vs non…
This dataset contains 64-channel EEG recordings and behavioural ratings from four participants who read and evaluated 210 short English-language texts: Haiku, Senryu, and non-poetic control texts. Each text was rated on aesthetic appeal, vivid imagery, being moved, originality, and creativity. These participants' recordings were acquired in segments because of session interruptions and were concatenated during preprocessing. They are released as a companion to the main Poetry Assessment EEG Dataset 1 for completeness and transparency.
…Participants were randomly assigned to one of three experimental groups through an…
MEG recordings from a single infant subject in the ILABS amnoise dataset, collected in the context of research on infants at risk for dyslexia. The recordings relate to auditory processing during the linguistic sensitive period for native phoneme learning. The data are organized in BIDS format and were converted using MNE-BIDS.
…The order of target stimuli was randomly distributed among the inquiries. Between…
…learnable blocks and presented in random order. Thus, the win and loss…
A BIDS example dataset demonstrating annotation of intracranial EEG (iEEG) recordings with the HED schema library for SCORE (Standardized Computer-based Organized Reporting of EEG). It contains iEEG data from one subject recorded during clinical photic stimulation at Mayo Clinic. Events are annotated through an events.tsv column whose levels are defined as HED tags in the events.json sidecar, yielding machine-readable annotations validated with the BIDS and HED validators.
…Each trial was unique and was generated pseudo-randomly before the experiment…
…using Q-value ranges and randomized order across trials. - Task events encode…
Preprocessed 64-channel EEG recordings from 8 healthy participants performing imagined speech of three vowel phonemes (a, i, u) in response to auditory and visual cues. The dataset was re-packaged in BIDS format from the MOABB collection and supports decoding of imagined speech with Riemannian manifold features and relevance vector machine classification.