Adding Human Learning in Brain--Computer Interfaces (BCIs)

Abstract

In this article, we introduce CLBCI (Co-Learning for Brain--Computer Interfaces), a BCI architecture based on co-learning in which users can give explicit feedback to the system rather than just receiving feedback. CLBCI is based on minimum distance classification with Independent Component Analysis (ICA) and allows for shorter training times compared to classical BCIs, as well as faster learning in users and a good performance progression. We further propose a new scheme for real-time two-dimensional visualization of classification outcomes using Wachspress coordinate interpolation. It allows us to represent classification outcomes for n classes in simple regular polygons. Our objective is to devise a BCI system that constitutes a practical interaction modality that can be deployed rapidly and used on a regular basis. We apply our system to an event-based control task in the form of a simple shooter game in which we evaluate the learning effect induced by our architecture compared to a classical approach. We also evaluate how much user feedback and our visualization method contribute to the performance of the system.