Kensuke Sekihara1,3 Tomohiko Shibuya2Shuichi Okawa2 Shigenori Kawabata1

Environmental noise cancellation for room-temperature magneto-resistive (MR) sensor arrays

1 Tokyo Medical and Dental University2 TDK Corporation3 Signal Analysis Inc.

Development of a sensor system that can measure biomagnetic signals in a room-temperature environment has gained great interests.

Particularly, MR sensors are promising candidates for room-temperature biomagnetic systems because the MR sensor system can lead to developing novel low-initial-cost and maintenance-free biomagnetic instruments.

However, to attain such a goal, an efficient method of environmental noise cancellation method should be developed. This is because with such low-cost instruments, the cost for a magnetic shielded room should also be low.

Outline

In our study, we have applied DSSP algorithm previously-proposed for removing the stimulus-induced artifacts in magnetospinography to remove environmental noise.

We present results of our experiments in which the sixty four channel MCG data were measured at COEX hotel lobby as a part of commercial exhibition/demonstration in Biomag Soul 2016.

We show that, although the data contained a large amount of environmental noise, the DSSP algorithm was able to remove the most of these noise, demonstrating the effectiveness of the DSSP algorithm in environmental noise cancellation.

Outline-continued

jr

Voxel Lead field Matrix

SVD of LV

Denoting distinctively large singular values by singular vectors, form orthonormal basis vectors of the pseudo signal subspace.

, , ,

, ,ξ

ξ

γ γu u

1

1

Projector onto pseudo-signal subspace

Pseudo signal subspace projector：T

, , , ,ξ ξ = P u u u u1 1

S I

I

ε

ε

= + +− = − + −

PB B PB PBI P B I P B I P B( ) ( ) ( )

DSSP AlgorithmData Model:

S I ε= + +B B B B

Signal

InterferenceSensor noise

Using the pseudo signal subspace projector, DSSP computes two kinds of spatiotemporal matrices:

DSSP estimates the interference subspace using:

Interference subspace = row space of row space of row space of

I

≈ −BPB I P B( ) ( ( ) )

TT

MT

TM

φ

ϕ

Λ Λ

Λ Λ

= = − = =

PB S D S d d d

I P B T E T e e e1

' '1

, , , ,

( ) , , , ,

[1] Golub G. H. , Van Loan C. F. (1996) Matrix computations. The Johns Hopkins University Press, Baltimore and London

SVD of and −PB I P B( ) :

Orthonormal basis vectors of the intersection between span and span can be obtained using an algorithm described in [1]:

1

1

{ , , }

{ , , }φ

ϕ

d de e

Denoting these basis vectors by DSSP algorithm removes interference by using:

1 r, , ,ψ ψ

TS r r= −B B I ψ ψ ψ ψ1 1ˆ ( [ , , ][ , , ] )

DSSP Algorithm-continued

Commercial exhibition/demonstration in Biomag Soul 2016MCG was measured from a number of volunteers

1ch2ch3ch4ch5ch6ch7ch

8ch14ch

15ch20ch

21ch25ch26ch31ch

32ch36ch37ch42ch

43ch47ch48ch53ch

54ch58ch59ch64ch

Sensor layout overlaid onto an X-ray projection image (a representative example)

The 7th, 59th, and 64th channels were used as reference sensors in experiments using the adaptive noise cancelling

Case #1A single channel ECG data

Adaptive noise cancelling experiments

Reference sensor data

64 channel MCG data

DSSP cleaned results Cleaned results

Adaptive noise cancelling experiments

Case#2A single channel ECG data

64 channel MCG data

DSSP cleaned results

Reference sensor data

Cleaned results

Case #3A single channel ECG data

64 channel MCG data

DSSP cleaned results

Adaptive noise cancelling experiments

Reference sensor data

Cleaned results

The DSSP algorithm was published in Journal of Neural Engineering. The PDF can be downloaded from

http://www.signalanalysis.jp/images/dssp.pdf

The paper “Subspace-based interference removal methods for multichannel biomagnetic sensor arrays” has been accepted for publication in Journal of Neural Engineering. The PDF can be downloaded from:

• The DSSP algorithm was applied to remove environmental noise from MCG data measured at COEX hotel lobby as a part of commercial exhibition/demonstration in Biomag Soul 2016.

• Although the data contained a large amount of environmental noise, the DSSP algorithm was able to remove the most of these noise, demonstrating the effectiveness of the DSSP algorithm in environmental noise cancellation.

Summary

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