Single-trial classification of multi-user P300-based Brain-Computer Interface using riemannian geometry

The classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair.

[1]  B. Achiriloaie,et al.  VI REFERENCES , 1961 .

[2]  Guillaume Gibert,et al.  OpenViBE: An Open-Source Software Platform to Design, Test, and Use BrainComputer Interfaces in Real and Virtual Environments , 2010, PRESENCE: Teleoperators and Virtual Environments.

[3]  Marco Congedo,et al.  EEG Source Analysis , 2013 .

[4]  Anatole Lécuyer,et al.  Author manuscript, published in "IEEE Transactions on Computational Intelligence and AI in games (2013)" Two Brains, One Game: Design and Evaluation of a Multi-User BCI Video Game Based on Motor Imagery , 2022 .

[5]  Xiaoping Li,et al.  Common Spatio-Temporal Pattern for Single-Trial Detection of Event-Related Potential in Rapid Serial Visual Presentation Triage , 2011, IEEE Transactions on Biomedical Engineering.

[6]  Laura Astolfi,et al.  Defecting or Not Defecting: How to “Read” Human Behavior during Cooperative Games by EEG Measurements , 2010, PloS one.

[7]  Alexandre Barachant,et al.  A Plug&Play P300 BCI Using Information Geometry , 2014, ArXiv.

[8]  Peng Yuan,et al.  Study on an online collaborative BCI to accelerate response to visual targets , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Anton Nijholt,et al.  Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games , 2009, INTETAIN.

[10]  Christian Jutten,et al.  " Brain Invaders": a prototype of an open-source P300-based video game working with the OpenViBE platform , 2011 .

[11]  Tzyy-Ping Jung,et al.  A Collaborative Brain-Computer Interface for Improving Human Performance , 2011, PloS one.

[12]  Maher Moakher,et al.  Means of Hermitian positive-definite matrices based on the log-determinant α-divergence function , 2012 .

[13]  A. Kübler,et al.  Motivation modulates the P300 amplitude during brain–computer interface use , 2010, Clinical Neurophysiology.

[14]  G. Pfurtscheller,et al.  Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Guillaume Gibert,et al.  xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface , 2009, IEEE Transactions on Biomedical Engineering.

[16]  Xavier Pennec,et al.  A Riemannian Framework for Tensor Computing , 2005, International Journal of Computer Vision.

[17]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[18]  Dean J Krusienski,et al.  A comparison of classification techniques for the P300 Speller , 2006, Journal of neural engineering.

[19]  R. Malach,et al.  Intersubject Synchronization of Cortical Activity During Natural Vision , 2004, Science.