A New Generation of Brain-Computer Interface Based on Riemannian Geometry
This addresses the problem of slow and unreliable BCIs for users, though it appears incremental as it builds on existing geometric methods.
The paper tackles the challenge of creating a new generation of Brain-Computer Interfaces (BCIs) that require no training and adapt quickly, presenting a classification framework based on Riemannian geometry that achieves good performance with little data and strong generalization across subjects and sessions.
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.