fNIRS-GANs: data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy.

Journal: Journal of neural engineering

PMID: 31945755

Abstract

OBJECTIVE: Functional near-infrared spectroscopy (fNIRS) is expected to be applied to brain-computer interface (BCI) technologies. Since lengthy fNIRS measurements are uncomfortable for participants, it is difficult to obtain enough data to train classification models; hence, the fNIRS-BCI accuracy decreases.

Authors

Tomoyuki Nagasawa

Graduate School of Engineering, Nagaoka University of Technology, Nagaoka, Japan.
Takanori Sato
Isao Nambu

Department of Electrical Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, Japan.
Yasuhiro Wada

Department of Electrical Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, Japan.

Keywords

Adult Brain-Computer Interfaces Female Humans Male Motor Cortex Movement Neural Networks, Computer Psychomotor Performance Spectroscopy, Near-Infrared Support Vector Machine Young Adult

External Resources

View on PubMed Access via DOI PubMed (31945755)

fNIRS-GANs: data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy.

Abstract

Authors

Keywords

External Resources

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