Single trial classification of fNIRS-based brain-computer interface mental arithmetic data

Autor(en)

Gunther Bauernfeind, David Steyrl, Clemens Brunner, Gernot R. Muller-Putz

Abstrakt

Functional near infrared spectroscopy (fNIRS) is an emerging technique for the in-vivo assessment of functional activity of the cerebral cortex as well as in the field of brain-computer-interface (BCI) research. A common challenge for the utilization of fNIRS for BCIs is a stable and reliable single trial classification of the recorded spatio-temporal hemodynamic patterns. Many different classification methods are available, but up to now, not more than two different classifiers were evaluated and compared on one data set. In this work, we overcome this issue by comparing five different classification methods on mental arithmetic fNIRS data: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVM), analytic shrinkage regularized LDA (sLDA), and analytic shrinkage regularized QDA (sQDA). Depending on the used method and feature type (oxy-Hb or deoxy-Hb), achieved classification results vary between 56.1 % (deoxy-Hb/QDA) and 86.6% (oxy-Hb/SVM). We demonstrated that regularized classifiers perform significantly better than non-regularized ones. Considering simplicity and computational effort, we recommend the use of sLDA for fNIRS-based BCIs.

Organisation(en)

Institut für Psychologie der Kognition, Emotion und Methoden

Externe Organisation(en)

Technische Universität Graz

Seiten

2004-2007

Anzahl der Seiten

4

DOI

https://doi.org/10.1109/embc.2014.6944008

Publikationsdatum

08-2014

Peer-reviewed

Ja

ÖFOS 2012

102019 Machine Learning, 202004 Brain-Computer Interface

ASJC Scopus Sachgebiete

Allgemeine Medizin, Health Informatics, Biomedical Engineering, Computer Science Applications

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/455fd527-9b1c-4a36-aa3d-77b36500079d