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

arXiv: https://arxiv.org/abs/1904.09111 · HTML: https://mlsanity.com/paper/1904.09111/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 5 of 5)

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- [NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces](https://mlsanity.com/paper/2605.14698/) (2026)
- [EEG-based Image Feature Extraction for Visual Classification using Deep Learning](https://mlsanity.com/paper/2209.13090/) (2022)
- [AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding](https://mlsanity.com/paper/2507.11911/) (2025)

## Abstract (arXiv)

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.