Paper Info
Title
Learning From More Than One Data Source: Data Fusion Techniques for Sensorimotor Rhythm-Based Brain–Computer Interfaces
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.
Year
DOI
Venue
2015
10.1109/JPROC.2015.2413993
Proceedings of the IEEE
Keywords
Field
DocType
Brain-computer interface (BCI),data fusion,electroencephalography (EEG),hybrid BCI,multi-modal,mutual information,near-infrared spectroscopy (NIRS),zero-training
Data source,Data integration,Sensorimotor rhythm,Computer science,Brain–computer interface,Speech recognition,Robustness (computer science),Sensor fusion,Human–computer interaction,Mutual information,Electroencephalography
Journal
Volume
Issue
ISSN
103
6
0018-9219
Citations 
PageRank 
References 
16
0.68
47
Authors
5
Name
Order
Citations
PageRank
Siamac Fazli1160.68
Sven Dähne2161.02
Wojciech Samek3323.40
Felix Bießmann4161.02
Klaus-Robert Müller5127561615.17