Home // ALLSENSORS 2018, The Third International Conference on Advances in Sensors, Actuators, Metering and Sensing // View article
Authors:
Kensuke Uesugi
Masafumi Hashimoto
Kazuhiko Takahashi
Keywords: fNIRS; Motion artifact; Detection and cancellation; AR model; Kalman filter; Discrete Fourier transform
Abstract:
This paper presents a method for detecting and cancelling motion artifacts related to standing and walking in a functional near-infrared spectroscopy (fNIRS) signal. Our fNIRS device has 22 channels. The motionless fNIRS signal from each channel is represented by a fourth-order autoregressive (AR) model, and the related parameters are estimated based on the motionless fNIRS signal using the Yule Walker equation. The motion artifacts included in the fNIRS signal are cancelled using the Kalman filter constructed from the AR model. However, the cancellation may be insufficient when the motion artifacts are strong. To determine in which fNIRS channels the motion artifacts are cancelled insufficiently, we apply a measurement prediction error related to the Kalman filter and a discrete Fourier transform. The brain activity of the user is then recognized from those fNIRS channels in which the motion artifacts are cancelled sufficiently. To evaluate the proposed method, a mobile robot is controlled using an fNIRS devise as worn by 10 subjects while standing, walking, or sitting. The experimental results show the performance of the proposed method.
Pages: 59 to 64
Copyright: Copyright (c) IARIA, 2018
Publication date: March 25, 2018
Published in: conference
ISSN: 2519-836X
ISBN: 978-1-61208-621-7
Location: Rome, Italy
Dates: from March 25, 2018 to March 29, 2018