# A Plug&Play P300 BCI Using Information Geometry

arXiv: https://arxiv.org/abs/1409.0107 · HTML: https://mlsanity.com/paper/1409.0107/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2014.

## Built on by (most cited first, 14 of 14)

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- [End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities](https://mlsanity.com/paper/2203.07807/) (2022)
- [LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability](https://mlsanity.com/paper/2303.16407/) (2023)
- [Towards the Classification of Error-Related Potentials using Riemannian Geometry](https://mlsanity.com/paper/2109.13085/) (2021)
- [IFTT-PIN: A PIN-Entry Method Leveraging the Self-Calibration Paradigm](https://mlsanity.com/paper/2205.09534/) (2022)
- [ERP-XTTN: Interpretable Prototype-Guided Cross-Attention for Cross-Subject ERP Classification](https://mlsanity.com/paper/2606.02939/) (2026)

## Abstract (arXiv)

This paper presents a new classification methods for Event Related Potentials (ERP) based on an Information geometry framework. Through a new estimation of covariance matrices, this work extend the use of Riemannian geometry, which was previously limited to SMR-based BCI, to the problem of classification of ERPs. As compared to the state-of-the-art, this new method increases performance, reduces the number of data needed for the calibration and features good generalisation across sessions and subjects. This method is illustrated on data recorded with the P300-based game brain invaders. Finally, an online and adaptive implementation is described, where the BCI is initialized with generic parameters derived from a database and continuously adapt to the individual, allowing the user to play the game without any calibration while keeping a high accuracy.