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

Authors
Fazli, SiamacDaehne, SvenSamek, WojciechBiessmann, FelixMueller, Klaus-Robert
Issue Date
6월-2015
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Brain-computer interface (BCI); data fusion; electroencephalography (EEG); hybrid BCI; multi-modal; mutual information; near-infrared spectroscopy (NIRS); zero-training
Citation
PROCEEDINGS OF THE IEEE, v.103, no.6, pp.891 - 906
Indexed
SCIE
SCOPUS
Journal Title
PROCEEDINGS OF THE IEEE
Volume
103
Number
6
Start Page
891
End Page
906
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/93395
DOI
10.1109/JPROC.2015.2413993
ISSN
0018-9219
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.
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Graduate School > Department of Brain and Cognitive Engineering > 1. Journal Articles
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