A New Generation of Brain-Computer Interface Based on Riemannian Geometry

TL;DR

This paper introduces a simple, robust, and adaptive Riemannian geometry-based classification framework for brain-computer interfaces that requires no training and generalizes well across sessions and subjects.

Contribution

It presents a novel, computationally efficient BCI classification method leveraging Riemannian geometry, enabling rapid adaptation and broad applicability without training.

Findings

Effective across ERP, mu rhythms, and SSEP signals

Requires minimal training data and adapts quickly

Serves as a benchmark for future BCI development

Abstract

Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not require training; instead they will be smartly initialized using remote massive databases and will adapt to the user fast and effectively in the first minute of use. They will be reliable, robust and will maintain good performances within and across sessions. A general classification framework based on recent advances in Riemannian geometry and possessing these characteristics is presented. It applies equally well to BCI based on event-related potentials (ERP), sensorimotor (mu) rhythms and steady-state evoked potential (SSEP). The framework is very simple, both algorithmically and computationally. Due to its simplicity, its ability to learn rapidly (with little training data) and its good across-subject and across-session generalization, this strategy a very good candidate for building a new generation of BCIs, thus we hereby propose it as a benchmark method for the field.