A New Approach to Blind System Identification in MEG Data

Kuniharu KISHIDA; Hidekazu FUKAI; Takashi HARA; Kazuhiro SHINOSAKI

A New Approach to Blind System Identification in MEG Data

Kuniharu KISHIDA, Hidekazu FUKAI, Takashi HARA, Kazuhiro SHINOSAKI

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A new blind identification method of transfer functions between variables in feedback systems is introduced for single sweep type of MEG data. The method is based on the viewpoint of stochastic/statistical inverse problems. The required conditions of the model are stationary and linear Gaussian processes. Raw MEG data of the brain activities are heavily contaminated with several noises and artifacts. The elimination of them is a crucial problem especially for the method. Usually, these noises and artifacts are removed by notch and high-pass filters which are preset automatically. In the present paper, we will try two types of more careful preprocessing procedures for the identification method to obtain impulse functions. One is a careful notch filtering and the other is a blind source separation method based on temporal structure. As results, identifiably of transfer functions and their impulse responses are improved in both cases. Transfer functions and impulse responses identified between MEG sensors are obtained by using the method in Appendix A, when eyes are closed with rest state. Some advantages of the blind source separation method are discussed.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.3 pp.611-619

Publication Date: 2003/03/01

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Type of Manuscript: Special Section PAPER (Special Section on Blind Signal Processing: Independent Component Analysis and Signal Separation)

Category: Applications

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Kuniharu KISHIDA, Hidekazu FUKAI, Takashi HARA, Kazuhiro SHINOSAKI, "A New Approach to Blind System Identification in MEG Data" in IEICE TRANSACTIONS on Fundamentals, vol. E86-A, no. 3, pp. 611-619, March 2003, doi: .
Abstract: A new blind identification method of transfer functions between variables in feedback systems is introduced for single sweep type of MEG data. The method is based on the viewpoint of stochastic/statistical inverse problems. The required conditions of the model are stationary and linear Gaussian processes. Raw MEG data of the brain activities are heavily contaminated with several noises and artifacts. The elimination of them is a crucial problem especially for the method. Usually, these noises and artifacts are removed by notch and high-pass filters which are preset automatically. In the present paper, we will try two types of more careful preprocessing procedures for the identification method to obtain impulse functions. One is a careful notch filtering and the other is a blind source separation method based on temporal structure. As results, identifiably of transfer functions and their impulse responses are improved in both cases. Transfer functions and impulse responses identified between MEG sensors are obtained by using the method in Appendix A, when eyes are closed with rest state. Some advantages of the blind source separation method are discussed.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_3_611/_p

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@ARTICLE{e86-a_3_611,
author={Kuniharu KISHIDA, Hidekazu FUKAI, Takashi HARA, Kazuhiro SHINOSAKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A New Approach to Blind System Identification in MEG Data},
year={2003},
volume={E86-A},
number={3},
pages={611-619},
abstract={A new blind identification method of transfer functions between variables in feedback systems is introduced for single sweep type of MEG data. The method is based on the viewpoint of stochastic/statistical inverse problems. The required conditions of the model are stationary and linear Gaussian processes. Raw MEG data of the brain activities are heavily contaminated with several noises and artifacts. The elimination of them is a crucial problem especially for the method. Usually, these noises and artifacts are removed by notch and high-pass filters which are preset automatically. In the present paper, we will try two types of more careful preprocessing procedures for the identification method to obtain impulse functions. One is a careful notch filtering and the other is a blind source separation method based on temporal structure. As results, identifiably of transfer functions and their impulse responses are improved in both cases. Transfer functions and impulse responses identified between MEG sensors are obtained by using the method in Appendix A, when eyes are closed with rest state. Some advantages of the blind source separation method are discussed.},
keywords={},
doi={},
ISSN={},
month={March},}

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TY - JOUR
TI - A New Approach to Blind System Identification in MEG Data
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 611
EP - 619
AU - Kuniharu KISHIDA
AU - Hidekazu FUKAI
AU - Takashi HARA
AU - Kazuhiro SHINOSAKI
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2003
AB - A new blind identification method of transfer functions between variables in feedback systems is introduced for single sweep type of MEG data. The method is based on the viewpoint of stochastic/statistical inverse problems. The required conditions of the model are stationary and linear Gaussian processes. Raw MEG data of the brain activities are heavily contaminated with several noises and artifacts. The elimination of them is a crucial problem especially for the method. Usually, these noises and artifacts are removed by notch and high-pass filters which are preset automatically. In the present paper, we will try two types of more careful preprocessing procedures for the identification method to obtain impulse functions. One is a careful notch filtering and the other is a blind source separation method based on temporal structure. As results, identifiably of transfer functions and their impulse responses are improved in both cases. Transfer functions and impulse responses identified between MEG sensors are obtained by using the method in Appendix A, when eyes are closed with rest state. Some advantages of the blind source separation method are discussed.
ER -