…True Tags ---- Modality: visual Type: perception Documentation ------------- Description: BigP3BCI: the largest public…
…True ## Tags - **Modality**: visual - **Type**: perception ## Documentation - **Description**: BigP3BCI: the largest public…
EEG dataset investigating anticipatory neural differences between attention and expectation. The recordings were collected by a research group led by Maria Ruz and are organized in BIDS format. The dataset is mirrored on NEMAR from OpenNeuro (ds004502).
…True Tags ---- Modality: visual Type: perception Documentation ------------- Description: BigP3BCI: the largest public…
EEG recordings from 31 participants performing a third-party moral judgment task embedded in a semantic congruency (N400) paradigm. Participants heard scenarios describing intentional or accidental harm and judged whether a final target word was congruent with the scenario. Agent intention and semantic congruency were manipulated orthogonally to examine the cognitive conflict triggered by moral judgment of accidental harm.
…True Tags ---- Modality: visual Type: perception Documentation ------------- Description: BigP3BCI: the largest public…
Scalp EEG recorded from 45 participants listening to a 12.4-minute audiobook recording of the first chapter of Alice's Adventures in Wonderland. The naturalistic paradigm was designed to study how the brain builds hierarchical syntactic structure and makes rapid, incremental linguistic predictions. The dataset includes word-level events annotated with lexical frequency, sound power, and n-gram, RNN, and context-free-grammar surprisal predictors, along with comprehension scores and the audio stimuli.
…True Tags ---- Modality: visual Type: perception Documentation ------------- Description: BigP3BCI: the largest public…
…True Tags ---- Modality: visual Type: perception Documentation ------------- Description: BigP3BCI: the largest public…
EEG recordings from four participants performing motor imagery of the left hand, right hand, and feet, originally reported by Zhou et al. (2016) in a study on automated trial selection for motor imagery brain-computer interfaces. Each participant completed three sessions of two runs, giving 450 trials per subject. Data were recorded from 14 channels at 250 Hz and reformatted into BIDS from a MOABB-generated Zenodo version.