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Fundamental Study for A Noise Reduction Method on Human Brain Activity Data of NIRS using AR Model

Authors:
Toshiya Tsubota
Tomohiko Kuroiwa
Takuya Kiryu
Yu Kikuchi
Hiroaki Inoue
Fumikazu Miwakeichi
Shunji Shimizu

Keywords: Near Infra-Red Spectroscopy (NIRS) ; Auto Regressive (AR) model ; signal to noise ratio.

Abstract:
Currently, Near Infra-red Spectroscopy (NIRS) is used as a diagnostic aid for mental illness in hospitals [1]. In our previous study, we have already reported the relationship between human brain activity change and turning the corner while driving a car or carrying out human living motions, using NIRS [2][3]. In that research, it was very difficult to discriminate noise from measurement signals not only by using NIRS but also by using Magnetoencephalography (MEG), Electroencephalogram (EEG), and so on. In Particular, for a study to measure the brain activity related to tasks of memory or intention, the effects of the ratio of signal to noise are very important. Our experimental results show that our model removes the noise of the heartbeat and breathing motion from the brain activity data of NIRS.

Pages: 33 to 38

Copyright: Copyright (c) IARIA, 2018

Publication date: March 25, 2018

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-61208-616-3

Location: Rome, Italy

Dates: from March 25, 2018 to March 29, 2018