Automatic Control of Reactive Brain Computer Interfaces

TL;DR

This paper explores Bayesian-based real-time control methods for reactive brain computer interfaces, enhancing performance through transfer learning with Gaussian mixture models for faster convergence and practical deployment.

Contribution

It introduces transfer learning with Gaussian mixture models to improve automatic control and feedback in reactive brain computer interfaces.

Findings

Enhanced convergence speed of control algorithms

Effective transfer learning setup with Gaussian mixture models

Improved real-time BCI performance

Abstract

This article discusses practical and theoretical aspects of real-time brain computer interface control methods based on Bayesian statistics. We investigate and improve the performance of automatic control and feedback algorithms of a reactive brain computer interface based on a visual oddball paradigm for faster statistical convergence. We introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.