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.

翻译：基于过去25年在脑机接口（BCI）领域积累的经验，我们现在可以展望新一代的BCI。这类BCI将无需训练；取而代之的是，它们将利用远程海量数据库进行智能初始化，并在使用初期的一分钟内快速、有效地适应用户。它们将具备可靠性、鲁棒性，并在单次及多次使用中保持良好性能。本文提出了一种基于黎曼几何最新进展、具备上述特性的通用分类框架。该框架同样适用于基于事件相关电位（ERP）、感觉运动（mu）节律和稳态诱发电位（SSEP）的BCI。该框架在算法和计算层面都非常简洁。由于其简单性、快速学习能力（仅需少量训练数据）以及良好的跨被试与跨会话泛化性能，该策略是构建新一代BCI的极佳候选方案，因此我们在此提议将其作为该领域的基准方法。