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Brain Invaders Adaptive versus Non-Adaptive P300 Brain-Computer Interface dataset

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arxiv 1904.09111 v1 pith:KGQOMAZW submitted 2019-04-19 cs.HC

Erwan Vaineau (GIPSA-VIBS) , Alexandre Barachant (GIPSA-VIBS) , Anton Andreev (GIPSA-Services) , Pedro C. Rodrigues (GIPSA-VIBS) , Grégoire Cattan (GIPSA-VIBS , IHMTEK) , Marco Congedo (GIPSA-VIBS) This is my paper · ORCID
classification cs.HC
keywords brain-computerdatasetinterfacedataexperimentp300visualadaptive
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We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.1494163 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment on PC. The visual P300 is an event-related potential elicited by visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC with and without adaptive calibration using Riemannian geometry. The brain-computer interface is based on electroencephalography (EEG). EEG data were recorded thanks to 16 electrodes. Data were recorded during an experiment taking place in the GIPSA-lab, Grenoble, France, in 2013 (Congedo, 2013). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA. The ID of this dataset is BI.EEG.2013-GIPSA.

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Cited by 2 Pith papers

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  1. NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces

    cs.LG 2026-05 unverdicted novelty 7.0 of 10

    NeuroAtlas benchmarks foundation models on 42 EEG datasets and reports that EEG-specific models do not consistently outperform generic time-series models, standard metrics miss clinical utility, and rankings vary by domain.

  2. AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding

    cs.HC 2025-07 conditional novelty 5.0 of 10

    A calibration-free EEG decoding framework that aligns and patches multi-dataset signals so a pretrained model can decode new users without labeled tuning.

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