J. Magn. Soc. Jpn., 41, 70-74 (2017)<Paper>

Evaluation Method of Magnetic Sensors
Using the Calibrated Phantom for Magnetoencephalography
D. Oyama, Y. Adachi, and G. Uehara

Applied Electronics Laboratory, Kanazawa Institute of Technology, Amaike 3, Kanazawa, Ishikawa 920-1331, Japan

In recent years, many kinds of magnetic sensors have been developed for biomagnetic measurement, such as
magnetocardiography (MCG) and magnetoencephalography (MEG). However, it is difficult to evaluate theirperformance using only actual MCG or MEG measurements. In this paper, we propose the use of the calibrated
MEG phantom for quantitative evaluation of magnetic sensors and present the experimental method. We choose amagneto-impedance (MI) sensor as an example of the magnetic sensor to be evaluated. The magnetic field
distribution near the phantom was measured using the MI sensor and a signal source was localized with different
averaging numbers and different signal source intensities. The results suggest that the MEG signal cannot beobserved in the usual averaging time (i.e., 100), even when the sensor is located near the head; 4.0 mm of source
localization accuracy can be achieved with 400-times averaging if the sensor noise decreases to 1/10. The use of thecalibrated phantom, instead of examination with human subjects, is effective for quantitative evaluation of theperformance of magnetic sensors.
Key words: magnetoencephalography, phantom, magneto-impedance sensor
1. Introduction
Biomagnetic
signalmeasurements,
suchasmagnetoencephalography(MEG)
andmagnetocardiography (MCG) are utilized in clinical applications
and neuroscience studies. The magnetic signals fromthe brain and heart are very weak; therefore,
superconducting quantum interference device (SQUID)
sensors have been used for practical MEG and MCG
systems for which the sensitivity is less than 10 fT/Hz1/2.
On the other hand, SQUID sensors must be cooled byliquidheliumtomaintainsuperconductivity.
Helium-less MCG or MEG systems are importantbecause of the cost and low availability of liquid helium.
In recent years, the development of refrigerant-less orroom-temperature magnetic sensors has advanced, withthe aim of realizing new biomagnetic measurement
systems. Some groups have succeeded in detecting themagnetic signal from the human heart or brain1)-6), andpractical applications are expected.
However, the evaluation of such magnetic sensors isdifficult with actual MCG or MEG measurement,
because there is no guarantee of reproducibility or
reliability of the signal sources. Objective evidence isimportant to prove the effectiveness of newly developedsensors. Additionally, quantitative evaluation isnecessary for designing the biomagnetic measurement
system using these sensors.
Therefore, the authors propose the use of a phantom
for the evaluation of the newly developed magneticsensors. A phantom is an artificial object that imitates
the human body. Quantitative evaluation can beachieved using phantom experiments instead of
examination of a human subject. The authors havedeveloped a new phantom and an associated calibration70

method designed for quantitative evaluation of MEG
systems7). This phantom was calibrated and its
uncertainty was determined so as to ensure
reproducibility and reliability.
In this paper, an evaluation method for aroom-temperature magnetic sensor using the calibrated
phantom is introduced. As an example of theexperimental evaluation with the phantom, we chose amagneto-impedance (MI) sensor that is a candidate forrealizing a helium-less MEG system. The experimental
setup is detailed in Section 2. The measured data arepresented in Section 3. The feasibility of MEG signal
detection by the MI sensor is discussed in Section 4.
2. Method
2.1 Phantom
One popular method to analyze the MEG data is toestimate a magnetic source in a human brain using theSarvas formula8). In the model of the Sarvas formula,
the human brain and the source current are aconductive sphere and a current dipole, respectively.
There are two types of phantoms: one is the “wetphantom”, composed of two electrodes installed in a
sphere filled with saline, and the other is the “dryphantom”, composed of a triangular wire based on
Ilmonieimi’s suggestion9). We have chosen the dry-type
phantom because it is much easier to handle, maintain,and calibrate.
Figure 1 shows the schematics of the phantom. Two
individual isosceles triangles were wound around aquadrangular pyramidal bobbin. The isosceles
triangular model has a 5-mm base and 65-mm height.
Twenty-five bobbins were assembled inside a domed
cover that imitates a human skull. The equivalentcurrent dipoles (ECDs) corresponding to fifty triangular

Journal of the Magnetics Society of Japan Vol.41, No.4, 2017

PHANTOM
5 mm
65 mm

R = 75 mmqx(a)ycurrentzMI sensor
1 mm≒65 mm
75 mmz(b)Magnetic flux density [pT/Hz1/2]

Fig. 1 Configuration of the MEG phantom. (a)schematic of an isosceles-triangular coil pair. (b)photograph of the MEG phantom7).

yMI sensor
currentxZ76 mm(a)100current

MI sensor

10sensingdirection
10.1

1101001000

Frequency [Hz]Fig. 2 Noise level of the MI sensor recorded inside amagnetically shielded room.
wires were estimated based on three-dimensional
measurement of the current paths and numericalcalculations. The details of the phantom configurationand calibration are described in Ref. 7.
2.2 Sensor
In this study, we used a commercially available MIsensor(MI-CB-1DH,
Aichi
MicroIntelligent
Corporation). Figure 2 shows the magnetic noise
spectrum recorded inside a magnetically shielded room(MSR). The noise level was approximately 10 pT/Hz1/2
at 10 Hz. The noise level of a SQUID sensor for an MEG
system should be of femto-Tesla order. Although thesensitivity of the MI sensor is insufficient, theroom-temperature sensor has the advantage of being
placed at a closer position to the head. Improvement ofthe signal-to-noise ratio is expected because of theshorter distance between the sensor and the signalsource.

(b)

Fig. 3 Setup for experimental evaluation of the MIsensor using the phantom. (a) Schematics of theexperimental setup and (b) photograph of the phantom
and the MI sensor.
2.3 Experimental setup
Figure 3 shows (a) the experimental setup and (b) thephotograph of the phantom and the MI sensor. One
triangular wire was chosen for the experiment and anelectric current was applied to it by a function generator.
The waveform was a sinusoidal burst at a frequency of
11 Hz. The measurement was performed using different
current amplitudes I = 10 and 100 A. The intensity of
the ECD corresponding to an applied current amplitudeof 10 A was approximately 50 nA·m. This ECD
intensity is similar to that estimated from the recorded
data of human auditory evoked responses.
To measure the magnetic field distribution near thephantom, the MI sensor and the phantom were fixed ona three-axial stage and a rotation stage, respectively.
The sensing direction of the MI sensor was indicated byarrows in Fig. 3, therefore, the magnetic field normal tothe phantom surface was detected by the MI sensor.
The total number of measuring points was 54; these

Journal of the Magnetics Society of Japan Vol.41, No.4, 201771

(b) I = 10 A, Nave = 400(a) I = 10 A, Nave = 100

5 pT

5 pT50 ms(c) I = 10 A, Nave = 800(a)(b)(c)(d)

(e)

(f)

50 ms

(d) I = 10 A, Nave = 1200

5 pT

5 pT
50 ms

50 ms

(e) I = 10 A, Nave = 1600(f) I = 100 A, Nave = 10050 pT

5 pT
50 ms

50 ms

(g)

(h)

(g) I = 100 A, Nave = 400heightz = 10mm

rotation angle
q = 10 degree

5 A

50 pT
50 ms

50 ms

15150

10100

550

00-5-50-10-100-15-150signal intensity in pT
left : (a)-(e), right : (f)-(g)Fig. 4 Measured waveforms (a)-(g) with different
current amplitudes (I) and averaging numbers (Nave),and the applied current waveform (h).

Fig. 5 Contour maps with different current amplitudes(I) and averaging numbers (Nave).

points were obtained by rotating the phantom (q = 0°,±4°, ±12°, ±20°, ±28°) and vertically shifting theMI sensor (z = −20, −10, 0, 10, 20, 30 mm). The
phantom and the MI sensor were placed in an MSR
while measurements were taken.
The output signal of the MI sensor was amplified(×1000) and band-pass filtered (cut-off frequencies = 0.1
Hz and 500 Hz) before recording. The recording wasperformed by a 16-bit A/D converter (PCIe-6353,National Instruments). The sampling frequency was2000 Hz, and the recording time was 440 s for I = 10 A
and 120 s for I = 100 A. After recording, the waveforms
were averaged to reduce noise just as in the case wemeasure a human evoked response. We applieddifferent averaging numbers, namely, 100, 400, 800,
1200, and 1600 for I = 10 A, and 100 and 400 for I =100 A, so as to consider different signal-to-noise ratios.
Moving-average processing was also conducted with awindow length of 16.5 ms to reduce power-line
interference (60 Hz). Then, source localization using theSarvas formula was conducted for each data set.

(a)-(g) correspond to different averaging numbers andapplied current amplitude 100, 400, 800, and 1600 for I= 10 A, and 100 and 400 for I = 100 A, respectively.
The waveform detected at 54 measuring points isoverlapped. The applied current waveform when I = 10
A is shown in Fig. 4 (h).
Figure 5 shows the contour maps of the measured
magnetic field distributions. Figure 5 (a) - (g) are thesame as those of Fig. 4. The time point of the displayed
data was the first peak of the sinusoidal waveform,indicated by a triangular arrow in Fig. 4(h).
The source estimation was conducted using aleast-mean-square method and the Sarvas formula.
Figure 6 shows the source localization error andgoodness-of-fit (GOF) value in the estimation. Figure6(a)-(g) are the same as those of Figs. 4 and 5. Source
estimation was performed at every four peaks of thesinusoidal waveform; the length of the bar indicates themean value of the source localization error and the GOF.
By increasing the signal-to-noise ratio, the source
localization error decreased and the GOF increased.

3. Result

4. Discussion

Figure 4 shows the waveforms of measured data andapplied current in (h). The measured waveforms inThe amplitude of the magnetic signal from thehuman brain detected by a SQUID-based MEG system

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Journal of the Magnetics Society of Japan Vol.41, No.4, 2017

Source localization error [mm]8610 A(a)

(b)
(c)

100 A(d)(e)

420100Goodness of fit [%]

(g)

(f)

(f)

80(c)

(g)

(e)

(b)

60

40(d)

(a)

200

100

400

800
1200 1600
Averaging number

100400Fig. 6 Source localization error and goodness of fit in
ECD analysis.
is typically found to be approximately 1 pT or less.
However, larger waveforms, as shown in Fig. 4(e), canbe detected by a room-temperature sensor that waslocated much closer to the target. These results show
the possibility of realizing a room-temperature
sensor-based MEG system.
When a new magnetic sensor is developed or thesensitivity of a magnetic sensor is improved, theaveraging number used to observe the biomagnetic
signal is usually considered a criterion for theevaluation of its performance. As shown in Fig. 6, thesource localization error with different averaging
numbers can be obtained using the phantom. For
example, we conclude that 4.2 mm of accuracy and 80%
of GOF can be achieved with 1600-times averaging for
MEG measurements when using the MI sensor.
In the case of MEG measurement using a
SQUID-based system, the averaging number is usually
set to approximately 100, in consideration of thetrade-off between signal-to-noise ratio and fatigue
and/or the duration of concentration of a subject. In
contrast, we choose 1600-times averaging as a
maximum to obtain a better signal-to-noise ratio. One of
the benefits of using the phantom is highreproducibility of the measurement result with a highsignal-to-noise
ratio
basedonlong-duration
measurement.
Furthermore, we also carried out measurements at acurrent amplitude of 100 A such that the signal
amplitude was 10-times-larger than the human MEG
signal. The obtained signal-to-noise ratio is equivalent
to that with a 100-times-larger averaging number

because the noise decreases in proportion to the squareroot of the averaging number. Therefore, the results in
Fig.6 (f) and (g) correspond to the source localization
errors and GOF values at an averaging number of
10000 and 40000 for I = 10 A, respectively. By
applying a large electric current to the phantom, we canobtain a result equivalent to using a large averagingnumber. From these results, another conclusion isobtained: 4.0 mm of accuracy and over 99% of GOF canbe achieved with 400-times averaging if the sensor
noise decreases to 1/10. These conclusions provide thetarget specifications of the room-temperature magnetic
sensors used to realize MEG measurements.
In addition, we should point out the value of thesource localization error. In this experiment, thesmallest source localization error was 4.0 mm when I =100 A and the averaging number is 400. This error
was much larger than that obtained by theSQUID-based MEG systems7). A lower signal-to-noise
ratio, smaller measuring points, and a lack of accuracyof sensor positioning are considered to be the causes ofthe larger source localization error. Specifically, theaccuracy of the sensor positioning is supposed to be themajor cause of the source localization error because
there is a large magnetic field gradient near the signal
source comparing with that of SQUID-based MEGsystems10). It is also important to accurately calibrate
the sensor position and orientation to realize the MEGsystem.
5. Conclusion
We demonstrated the experimental evaluation of theMI sensor using the calibrated MEG phantom. The
signal source was estimated from the observed
magnetic field distribution at different signal-to-noise
ratios. The results showed the possibility (anddifficulty) of realizing MEG measurements using the MIsensor. The use of the calibrated phantom is effective
for evaluating the performance of magnetic sensors.
Acknowledgements This research was partly supportedby The Hokuriku Industrial Advancement Center.
References1) S. Yabukami, K. Kato, T. Ozawa, N. Kobayashi, and K. I.
Arai: J. Magn. Soc. Jpn., 38, 25 (2014).
2) H. Karo, and I. Sasada: J. Appl. Phys, 117, 17B322 (2015).
3) T. Yamamoto, K. Tashiro, and H. Wakiwaka: The Papers ofTech. Meeting on “Magnetics”, MAG-15-9, 41 (2015).
4) H. Xia, A. B-A. Baranga, D. Hoffman, and M. V. Romalis:
Appl. Phys. Lett., 89, 211104 (2006).
5) K. Wang, S. Tajima, Y. Asano, Y. Okuda, N. Hamada, C. Cai,and T. Uchiyama: Proc. of the 8th Intl. Conf. on Sens. Tech.,547 (2014).
6) Y. Ando: Journal of Japan Biomagnetism andBioelectromagnetics Society, 29, 20 (2016)7) D. Oyama, Y. Adachi, M. Yumoto, I. Hashimoto, and G.
Uehara: J. Neurosci. Methods, 251, 24 (2015).
8) J. Sarvas: Physics in Medicine and Biology, 32, 11 (1987)
9) R. J. Illmonieimi, M. S. Hamalainenm, and J. Knuutila: inJournal of the Magnetics Society of Japan Vol.41, No.4, 201773

Proc. Biomagnetism: Applications & Theory, New York,Pergamon, 278 (1985)
10) D. Oyama, Y. Adachi, and G. Uehara: The Papers ofTechnical Meeting on “Magnetics”, IEE Japan, MAG-16-1(2016)
Received Dec. 22, 2016; Revised Mar. 13, 2016; Accepted Apr.
05, 2017

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