bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset

Brain-computer interfaces (BCIs) have wide-ranging applications as solutions
for replacing or substituting neural output that has been lost because of
severe neuromuscular injury or disease, such as individuals with late-stage
amyotrophic lateral sclerosis (ALS). The P300-based BCI is one of the most
commonly researched BCI for communication. This BCI dataset is curated from
data originally generated from previous visual P300-based BCI speller studies,
which include single- and multi-session experiments under a wide range of
conditions. The BCI data are provided in an enriched and standardised format
with BCI data elements that align with developing IEEE P2731 Working Group
standards for BCI data to facilitate reusability. The data files, provided in
open European Data Format 'plus', contain: i) electroencephalography (EEG)
signals; ii) the BCI encoder, target characters and stimulus event markers for
P300 event related potential analysis; iii) BCI spelling outcomes and feedback
event markers for error related potential analysis; and if available, iv)
self-reported demographics (age, sex, race, ethnicity); v) ALS diagnosis and a
revised ALS Functional Rating Scale score obtained from medical records; and
vi) eye tracker signals.