nm000259 NEMAR-native dataset
Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models
This dataset contains EEG recordings from 10 healthy adults performing a P300 speller task using a 6x6 character matrix, under two stimulus conditions (Famous Faces overlay and Inverting). Data were collected across two sessions per subject with three runs each, using a 32-channel g.tec EEG system at 256 Hz. The dataset is a BIDS-formatted derivative generated via MOABB from the original data reported in Speier et al. (2017), which compared classification approaches for the P300 speller using language models.
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Coming soon. Per-file data-quality summaries are precomputed by the NEMAR processing pipeline. The static aggregate is on the way — tracked at nemar-cli#511.