Concepedia

Learning From More Than One Data Source: Data Fusion Techniques for Sensorimotor Rhythm-Based Brain–Computer Interfaces

Siamac Fazli, Sven Dähne, Wojciech Samek, Felix Bieszmann, Klaus‐Robert Müller

Proceedings of the IEEE · 2015 · 85 citations · 118 references

TL;DR

Brain‑computer interfaces are widely used but face challenges such as low signal‑to‑noise ratio, decoder robustness, short calibration times, and limited real‑world applicability, prompting hybrid BCI approaches that combine complementary analyses and recording techniques. This review examines recent data‑fusion and hybrid techniques for sensorimotor‑rhythm BCIs. The authors outline three main research lines—feature integration, multi‑session fusion, and multi‑subject fusion—to illustrate how these methods can improve modern BCI performance.

Abstract

Brain-computer interfaces (BCIs) are successfully used in scientific, therapeutic and other applications. Remaining challenges are among others a low signal-to-noise ratio of neural signals, lack of robustness for decoders in the presence of inter-trial and inter-subject variability, time constraints on the calibration phase and the use of BCIs outside a controlled lab environment. Recent advances in BCI research addressed these issues by novel combinations of complementary analysis as well as recording techniques, so called hybrid BCIs. In this paper, we review a number of data fusion techniques for BCI along with hybrid methods for BCI that have recently emerged. Our focus will be on sensorimotor rhythm-based BCIs. We will give an overview of the three main lines of research in this area, integration of complementary features of neural activation, integration of multiple previous sessions and of multiple subjects, and show how these techniques can be used to enhance modern BCI systems.