TY - GEN
T1 - Signal Space Separation for Spin-Exchange Relaxation-Free magnetometer
AU - Gao, Yang
AU - Shi, Zemin
AU - Ma, Xin
AU - He, Ning
AU - Tang, Xiaogang
AU - Wang, Defeng
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/20
Y1 - 2021/6/20
N2 - Magnetoencephalography (MEG) provides a real-Time, non-invasive investigation of brain activity, which is very important for deep understanding of neuroscience. But MEG signals are often contaminated by various artifacts. Signal space separation (SSS) is a technique based on quasi-static Maxwell equations and Laplace equations. It can be used as a spatial filter for MEG signals denoising. In this paper, the SSS spatial filtering is carried out with multi-channel Spin-Exchange Relaxation-Free (SERF) magnetometer equipment for the first time, and the signal fluctuation is significantly reduced after filtering. One automatic method has been developed in this study to find the best SSS parameters based on the Sequential Least Squares Programming. This paper used MNE-pyhton software to generate spatial noise and internal source signal, and compared the denoised signal with internal simulation signal to find the optimal SSS parameters. The experimental results showed that the auditory evoked response was more obvious and the signal-To-noise ratio of the signal is improved in the induced period after SSS filtering.
AB - Magnetoencephalography (MEG) provides a real-Time, non-invasive investigation of brain activity, which is very important for deep understanding of neuroscience. But MEG signals are often contaminated by various artifacts. Signal space separation (SSS) is a technique based on quasi-static Maxwell equations and Laplace equations. It can be used as a spatial filter for MEG signals denoising. In this paper, the SSS spatial filtering is carried out with multi-channel Spin-Exchange Relaxation-Free (SERF) magnetometer equipment for the first time, and the signal fluctuation is significantly reduced after filtering. One automatic method has been developed in this study to find the best SSS parameters based on the Sequential Least Squares Programming. This paper used MNE-pyhton software to generate spatial noise and internal source signal, and compared the denoised signal with internal simulation signal to find the optimal SSS parameters. The experimental results showed that the auditory evoked response was more obvious and the signal-To-noise ratio of the signal is improved in the induced period after SSS filtering.
KW - Magnetoencephalography
KW - Spin-Exchange Relaxation-Free magnetometer
KW - signal space separation
UR - https://www.scopus.com/pages/publications/85114130562
U2 - 10.1109/FLEPS51544.2021.9469769
DO - 10.1109/FLEPS51544.2021.9469769
M3 - 会议稿件
AN - SCOPUS:85114130562
T3 - FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems
BT - FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2021
Y2 - 20 June 2021 through 23 June 2021
ER -