IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 44, NO. 9, SEPTEMBER 1997

839

Noise Covariance Incorporated MEG-MUSIC
Algorithm: A Method for Multiple-Dipole
Estimation Tolerant of the Influenceof Background Brain Activity
Kensuke Sekihara,* Member, IEEE, David Poeppel, Alec Marantz, Hideaki Koizumi, and Yasushi Miyashita

Abstract— This paper proposes a method of localizing multiple current dipoles from spatio-temporal biomagnetic data. The
method is based on the multiple signal classification (MUSIC)
algorithm and is tolerant of the influence of background brain
activity. In this method, the noise covariance matrix is estimatedusing a portion of the data that contains noise, but does notcontain any signal information. Then, a modified noise subspace
projector is formed using the generalized eigenvectors of the noiseand measured-data covariance matrices. The MUSIC localizer
is calculated using this noise subspace projector and the noise
covariance matrix. The results from a computer simulation haveverified the effectiveness of the method. The method was thenapplied to source estimation for auditory-evoked fields elicited bysyllable speech sounds. The results strongly suggest the method’s
effectiveness in removing the influence of background activity.
Index Terms—Array signal processing, biomagnetics, biomedical electromagnetic imaging, biomedical signal processing, fuctional brain imaging, inverse problems.

I. INTRODUCTIONALTHOUGH the single equivalent current dipole (ECD)
is a widely accepted source model in biomagnetism [1],its usefulness is considerably limited when one proceeds to themeasurement of higher-order brain functions or when attempts
to reconstruct the entire current pathway in a human heart. The
obvious extension of single ECD modeling to accommodate
more complex biocurrent distributions is multiple dipole modeling. However, if we apply multidipole modeling to the dataat each single time point, the estimation accuracy is generally
limited by the ill-posed nature of the inverse problem and,thus, multidipole modeling with more than three dipoles isseldom applied in practice.
Manuscript received June 26, 1996; revised April 17, 1997. Asterisk
indicates corresponding author.
*K. Sekihara is with the Mind Articulation Project, Japan Science andTechnology Corporation (JST), Yushima 4-9-2, Bunkyo-ku, Tokyo 113 Japan(e-mail: ksekihara@po.iijnet.or.jp).
D. Poeppel is with the Biomagnetic Imaging Laboratory, University ofCalifornia, San Francisco, CA 94143-0628 USA.
A. Marantz is with the Department of Linguistics, Massachusetts Instituteof Technology, Cambridge, MA 02139 USA.
H. Koizumi is with the Mind Articulation Project, Japan Science andTechnology Corporation (JST), Tokyo 113 Japan.
Y. Miyashita is with the Mind Articulation Project, Japan Science andTechnology Corporation (JST), Tokyo 113 Japan, and the Department of
Physiology, University of Tokyo, School of Medicine, Tokyo 113 Japan.
Publisher Item Identifier S 0018-9294(97)06111-9.

The spatio-temporal modeling pioneered by Scherg et al.
[2], [3] reduces the limitations caused by this ill posedness
and enables a more accurate estimation to be attained. In thismodeling, the dipole locations are assumed to be unchanged
during measurement. By introducing this constraint, the dipole
locations can be estimated using a spatio-temporal data set.
Because the biomagnetic data can be acquired with a sampling interval of less than one millisecond, a few hundred
to several thousand data points can be contained in a single
spatio-temporal data set. Obviously, the use of a complete
spatio-temporal data set greatly improves the accuracy of theestimation, compared with estimation using the data at eachtime point separately.
Even when using spatio-temporal modeling, though, estimating multiple dipole parameters generally requires a highly
multidimensional nonlinear optimization search in which no
existing technique can guarantee that the true solution will
be attained within the practical limits of computational time.
Thus, the success of multiple dipole parameter estimation
greatly depends on how close the initial values are set to thetrue values of the dipole parameters in this search. One cleverway to avoid this highly multidimensional search is to use
the multiple signal classification (MUSIC) algorithm, which
provides suboptimal estimates for multiple dipole locations byusing only a three-dimensional (3-D) search, regardless of thenumber of sources. This algorithm was proposed by Schmidt[4], [5] in the field of antenna-array processing and introduced
to the magnetoencephalographic (MEG) inverse problem by
Mosher et al. [6].
The measurement of the biomagnetic field from a humanbrain often suffers from a very low signal-to-noise ratio (SNR).
Often, the noise that most seriously affects the localization
accuracy arises from spontaneous brain activity that is not
related to the neural activity under study. Therefore, such
spontaneous brain activity is usually referred to as brain noise.
It has been reported that the use of a covariance matrix
for such brain noise can significantly reduce its influence insource localization [7], [8]. This paper proposes a MUSICtype algorithm that incorporates a noise covariance matrix ofbackground brain activity. The algorithm can provide multipledipole estimation almost free of the influence of background
activity.

0018–9294/97$10.00  1997 IEEE

840

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 44, NO. 9, SEPTEMBER 1997

In this paper, Section II describes the proposed method.
Sections III and IV present results from computer simulationand from the method’s application to auditory evoked fieldmeasurements; both sets of results strongly suggest that the
proposed algorithm is effective. Throughout this paper, plain
italics indicate scalars, lower-case boldface italics indicate
vectors, and upper-case boldface italics indicate matrices. The
superscript indicates the matrix transpose. The eigenvalues
are numbered in decreasing order.
II. METHOD
A. Definitions and Problem Formulation
Let us define the magnetic field measured by the thdetector coil at timeas
, and a vector
as a set of measured data
at
where
.
is the total numberof detector coils, andis the total number of time
points. The spatio-temporal data matrix
is defined as. We assume that a total
current-dipole sources generate a biomagnetic field. A
spherical homogeneous conductor [9] is assumed, and twotangential components, the and components, of the source
moment are considered. For simplicity, we assume in thispaper that all dipole sources have fixed orientations during
measurement, and the dipole orientation is defined as its
normal vector, where. We also
define amatrix that expresses the orientations ofwholedipole sources as , such that
..
.
..
.

..

(1)

.

The magnitude of the th dipole-source moment is definedas. The source magnitude vector atis defined as. The source temporal
behavior is defined as.
The lead field vectors for the and components of theth source are defined asand. We define the lead field matrix
for the th source as, and the lead field vector
for the th source is defined as. The lead field
matrix for the entire set of
dipole sources is defined as. Then, the relationship between themeasurement vector
and the source intensity vector
is expressed as, whereis theadditive noise. The relationship between and is expressed
as
(2)
where

is

the

noise matrix defined by.
The conventional way of estimating the locations of thedipole sources is, based on the maximum-likelihood principle,

to minimize the following least squares cost function(3)Here, is the unit matrix, and the estimates of , , andare denoted as , , and , respectively. This minimization,
however, requires adimensional search where is again
the number of sources. Generally, for such a highly multidimensional search, there is no guarantee of obtaining the correct
solution unless we can set the initial estimate very close to thetrue solution.
The MUSIC algorithm [5], [6], [10] has been introduced
to avoid this highly multidimensional search. A distinct advantage of this algorithm is that, regardless of the numberof dipole sources, it can give a suboptimal estimate of thesource locations by using only a 3-D search in the solution
space. The MUSIC algorithm, first, implements the eigen
decomposition of the measured-data covariance matrix,. We denote thewhich is obtained by using
eigenvectors ofas, where. Unlesssome of the source activities are perfectly correlated witheach other,haseigenvalues arising from the signal
sources andeigenvalues arising from the noise. Let
us define the matrices
and
as
and
. The span of the columns ofiscalled the signal subspace and that ofis called the noise
subspace. To estimate the locations of the dipole sources, theMUSIC algorithm uses the fact that the lead field vector ateach source location is orthogonal to the noise subspace. The
source locations are estimated by checking the orthogonality
between the lead field vector and the noise subspace projector.
In practice, some kind of measure to evaluate the orthogonality is needed to implement the MUSIC algorithm; thisorthogonality measure is often called the MUSIC localizer.
For such a localizer, we can use [6]
(4)
where
indicates the generalized minimum eigenvalue of the matrix pair given in parenthesis. In this equation,is anmatrix and is expressed as, whereandare the lead field vectors
for the and components of a source at . The MUSIC
localizer is calculated in a volume where sources can exist,
and the locations where the localizer reaches a peak are chosen
as the source locations. Note that the localizer shown in (4) is
derived under the assumption that the source orientations are
fixed during the measurements. It is, however, proven in [6]that this localizer is also effective for dipole sources whoseorientations vary during measurement.
B. Noise Covariance Incorporated MUSIC
External noise fields cause spatially correlated noise inbiomagnetic measurements, and the incorporation of noisecorrelation is known to reduce the influence of such noise
fields [7], [8]. In the MUSIC algorithm, this incorporation canbe done in the following manner.

SEKIHARA et al.: NOISE COVARIANCE INCORPORATED MEG-MUSIC ALGORITHM

841

Let us define the covariance matrix of the dipole-source
activities as
and the noise covariance matrix as.
Using (2), we get(5)
for correlated noise. We assume that the noise and the signal magnetic field are uncorrelated. Let us denoteas aneigenvector obtained by solving the generalized eigenvalue
problemFig. 1. Source and detector configuration assumed in computer simulation.

(6)
Using

, it is easy to show thatforandSince both
equation results in(7)

are full rank matrices, the above
for

(8)

Equation (8) indicates that the locations of the dipole
sources are found by checking the orthogonality between themodified noise subspace projector
and the lead field
vector. Here,is defined as. The
eigenvectors are normalized in such a way that
. Here,is Kronecker’s delta;
when
, and
when
. Therefore, the localizer for correlated
noise is given by

(a)

(9)
(b)

This paper proposes using the above localizer to reducethe influence of background brain activities in the MUSIC
localization procedure. An accurate estimate of the noise
covariance matrix, however, is needed to use the localizer. For
this purpose, one should find a portion of the data that contains
only noise fields and does not contain any signal information.
For evoked neuromagnetic experiments, such a portion can befound in a data portion taken before a stimulus is applied.
When the prestimulus data portion is sufficiently long and theinterstimulus interval is sufficiently large, an accurate estimateof the covariance matrix can be obtained.
III. COMPUTER SIMULATION
We performed computer simulations to test the effectivenessof the proposed method. A 37-channel magnetometer whose
coil alignment was the same as that of the Magnes biomagnetic
measurement system (Biomagnetic Technologies, Inc., San
Diego, CA) was assumed. The direction was defined as the
direction perpendicular to the detector coil at the center of thecoil alignment, and is equal to zero at this coil position.
The values of the spatial coordinates
are expressedin centimeters. Two signal dipole sources are assumed to exist:
the first dipole source at (3.7, 1.0, 4.6) and the second dipole

Fig. 2. (a) Waveforms assumed for calculating the  components of two
signal sources (solid lines) and one background noise source (dotted line) in
the computer simulation. (b) Simulated 37-channel recordings calculated in
the computer simulation. The portion between 0 and 300 ms is used for theMUSIC multidipole analysis, and that between 300 and 0 ms is used to
calculate the noise covariance matrix.

0source at ( 2.1, 1.0, 7.3). The source of the background
activity was assumed to exist at (4.5, 1.3, 11.3). The
source and detector configuration for this simulation is shown
schematically in Fig. 1.
To generate the simulated biomagnetic field, the components of the signal sources were modeled using exponentiallycomponent of thedamped sinusoidal functions, and thebackground activity was modeled using a sinusoidal function.
The components for both types of sources were set at zerofor simplicity. The waveforms of the components are shownin Fig. 2(a). The simulated biomagnetic field was calculated
by using these waveforms at 1-ms intervals. Uncorrelated
Gaussian noise was added to make the final signal-to-noise
ratio equal to 3.6. The SNR was defined by the ratio of the
Frobenius norm of the signal-magnetic-field spatio-temporalmatrix to that of the noise matrix. The simulated 37-channel
recordings are shown in Fig. 2(b).

842IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 44, NO. 9, SEPTEMBER 1997

(a)

(a)

(b)

(b)

(c)

(d)

=1

Fig. 3. Results of calculating MUSIC localizers on the plane y
withthe simulated 37-channel recordings in the computer simulation. (a) The
conventional MUSIC localizer given in (4) was used and (b) the localizer
proposed in (9) was used. Each contour shows the relative value of thelocalizer, and each area where the localizer reaches a peak is considered
to be the location of one dipole.

The data portion from 0 to 300 ms was used for the MUSIC localization experiments. First, the conventional MUSIC
localizer shown in (4) was applied to this data portion, andthe results of calculating the localizer on theplane
are shown in Fig. 3(a). These contours show the relativevalue of the localizer, and each area where the localizer
reaches a peak is considered to be the location of one dipole
source. Here, the localizer detects two signal sources, but also
detects a false source caused by the influence of background
activity. Next, the proposed localizer in (9) was applied tothe same portion of the data. The noise covariance matrix
was calculated using the data from 300 to 0 ms. The
results are shown in Fig. 3(b). Comparing Fig. 3(a) with
Fig. 3(b), one can see that the influence of the background
activity is almost completely eliminated. The results in Fig. 3
clearly demonstrate the effectiveness of the proposed MUSIC
localizer.
IV. APPLICATION TO AUDITORY-EVOKED FIELD
The proposed method was applied to source localization for
the N1m component of auditory-evoked responses elicited byspeech sounds. The biomagnetic fields were measured using a
37-channel Magnes magnetometer installed at the Biomagnetic
Imaging Laboratory, University of California, San Francisco,
CA. All measurements were done in a magnetically shieldedroom.
The subject was a male volunteer with no audiologicalabnormalities. The auditory stimuli used in the experiments
were four kinds of syllables /dae/, /bae/, /pae/, and /tae/. The
subject was asked to discriminate the voiced syllables /dae/Fig. 4. Overlapped display of 37-channel recordings of auditory evoked
responses elicited by the four syllables: (a) /bae/, (b) /dae/, (c) /pae/, and(d) /tae/.

Fig. 5. The x; y , and z coordinates used to express the localization results
shown in Figs. 6–8. The midpoint between the left and right preauricularpoints is defined as the coordinate origin. The axis directed away from theorigin toward the left preauricular point is defined as the y axis, and thatfrom the origin to the nasion is the x axis. The z axis is defined as theaxis that is perpendicular to both these axes and directed from the origin to
the vertex.

+

+

+

and /bae/ from the voiceless syllables /pae/ and /tae/. The
subject pressed one response button when perceiving a /dae/or /bae/ and pressed another button when perceiving a /pae/ or/tae/. Stimuli were presented to the subject’s right ear, and themagnetometer was placed above the subject’s left hemisphere.
The subject used his left hand to press the response buttons.
The four syllables were presented in a pseudo random orderat variable interstimulus intervals ranging from 1 s to 1.5 s. The
stimulus duration was 300 ms and the total data acquisition
time was 600 ms, including a 100-ms prestimulus interval.
The data sampling frequency was 1041.7 Hz and a bandpass
on-line filter with a bandwidth between 1 Hz and 400 Hz
was applied. The 100 epochs were averaged and no off-line
digital filter was applied. The averaged 37-channel recordings
for the four kinds of auditory stimuli are shown in Fig. 4. In
the prestimulus data portions of these recordings (from 100

SEKIHARA et al.: NOISE COVARIANCE INCORPORATED MEG-MUSIC ALGORITHM

843

(a)

(b)

(c)

(d)

Fig. 6. Single-dipole estimation results for the auditory evoked responses shown in Fig. 4. Results are shown for the evoked responses from (a) /bae/, (b)/dae/, (c) /pae/, and (d) /tae/. The estimated locations whose goodness-of-fit was greater than 0.95 were selected, and their projections onto the transverse,
coronal, and sagittal planes were plotted. The circles depicting a human head represent the projections of the sphere used in the forward model onto thetransverse, coronal, and sagittal planes. Each dot represents one estimated dipole location. L and R indicate the subject’s left and right hemispheres.

to 0 ms), a fairly large amount of background noise can beobserved. This may be caused by the overlapping of motor
cortex activations elicited by the button-press response to theprevious syllable.
, and coordinates used to express the estimatedThe
results are depicted in Fig. 5. The midpoint between the left
and right preauricular points is defined as the coordinate
origin. The axis directed away from the origin toward the left
axis, and that from thepreauricular point is defined as theaxis. The
axis is defined asorigin to the nasion is thethe axis that is perpendicular to both these axes and directedfrom the origin to the vertex. These coordinates are measured
in centimeters.
A single dipole analysis was applied to the data portion
from 0 ms to 300 ms of the auditory evoked field data.
In this analysis, the biomagnetic recordings were digitallyfiltered with a bandpass filter with a bandwidth ranging
from 1 Hz to 20 Hz, and the location of a single dipole
was estimated for each time point. From approximately 300
estimated locations, those whose goodness-of-fit was greater

844IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 44, NO. 9, SEPTEMBER 1997

(a)

(b)

(c)

(d)

Fig. 7. The results of calculating the conventional localizer in (4) in a 3-D volume. The projections onto the transverse, coronal, and sagittal planes areshown. Results are shown for the evoked responses to (a) /bae/, (b) /dae/, (c) /pae/, and (d) /tae/.

than 0.95 were selected and the projections of these locations
onto the transverse, coronal, and sagittal planes are plotted inFig. 6.
Fig. 6 (a)–(c) and (e), respectively, shows the results for thesyllables /dae/, /bae/, /pae/, and /tae/. Each dot represents one
estimated location of a single dipole. The circles depicting ahuman head represent the projections of the sphere used to
calculate forward solutions. All of the results show clusters of
dipole locations near the primary auditory cortex area. This isin good agreement with previous analysis on the speech-soundelicited neuromagnetic fields, in which a single dipole source
was localized near the primary auditory cortex [11], [12].

The dipole clusters are concentrated in the results for /bae/and /tae/, but are fairly widely distributed for /dae/ and /pae/,
probably because of the influence of background activity.
The results of applying the MUSIC algorithm with theconventional localizer given in (4) are shown in Fig. 7. Here,using the data shown in Fig. 4, the MUSIC localizer was
calculated with an interval of 0.5 cm within a volume defined,, and. All of theseasresults show complex source configurations probably due tothe background brain activity. The MUSIC algorithm with thelocalizer proposed in (9) was next applied. The results areshown in Fig. 8. These results indicate that a single source

SEKIHARA et al.: NOISE COVARIANCE INCORPORATED MEG-MUSIC ALGORITHM

845

(a)

(b)

(c)

(d)

Fig. 8. The results of calculating the localizer proposed in (9) in a 3-D volume. The projections onto the transverse, coronal, and sagittal planes are shown.
Results are for the evoked response to (a) /bae/, (b) /dae/, (c) /pae/, and (d) /tae/.

exists in the area near the primary auditory cortex in all fourcases. It is easy to see that each peak location in Fig. 8 isvery close to the center of one dipole cluster in Fig. 6. Thus,
the MUSIC results are in very good agreement with the single
dipole estimation results. Although the nature of sources forspeech-sound-elicited neuromagnetic fields has not yet been
fully understood, several previous investigations suggest asingle dipole source located near the primary auditory cortex
for such neuromagnetic fields [11]–[13]. Thus, the results
obtained using the proposed localizer are more plausible
than the results in Fig. 7, which were obtained using theconventional localizer in (4). These results in Fig. 8 strongly

suggest the effectiveness of the proposed MUSIC localizer inremoving the influence of background activity.

V. DISCUSSIONSIn the proposed algorithm, the number of sources can, in
principle, be obtained by separating nonzero generalized eigenvalues
wherefrom zero-level generalized
where. This separation
eigenvalues
may not be easy when the number of time points used to
calculate the covariance matrix is small, or when sources are
partially correlated. The eigenvalues for the data set obtained

846IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 44, NO. 9, SEPTEMBER 1997(a)when applying the proposed method, the data acquisition
should be designed to provide as many time points as possible
for the noise covariance estimation. Also, the proposed method
may not work well when the SNR for the uncorrelated
sensor noise is extremely high. This is because the noise
covariance matrix becomes almost singular, and the inverse of
the covariance matrix cannot be calculated. This fact usually
does not affect the practical application of the method to
the MEG inverse problem, because in most cases of MEG
measurement the SNR of the sensor noise seldom exceeds 50.
However, if the method is applied to extremely high SNR data,
one should be careful when interpreting the results.
VI. CONCLUSION
In summary, this paper proposes a multiple dipole estimation method that is tolerant of the influence of backgroundbrain activity. This method is based on the MEG-MUSIC algorithm and incorporates information on the noise covariancematrix. A computer simulation verified the effectiveness ofthe proposed method. The method was also applied to speechsound-elicited auditory evoked fields, and results strongly
suggesting the method’s effectiveness were obtained.
ACKNOWLEDGMENT(b)~ j in (6) calculated
Fig. 9. (a) The plot of the generalized eigenvalues using the evoked responses to /bae/. (b) The results of applying the proposed
localizer to the evoked response to /bae/ when setting the number of sources
at seven.

from the stimulus /bae/ are plotted in Fig. 9(a). The MUSIC
results for /bae/ shown in Fig. 8 were obtained by setting
the number of sources to three, but as can be seen from
Fig. 9(a), the determination of the source number to three
is somewhat ambiguous. However, in general, the MUSIC
algorithm is tolerant of source-number overestimation [14].
This tolerance is demonstrated in Fig. 9(b) where the MUSIC
results for /bae/ were calculated by setting the source number
to seven. Even though the number of sources was considerably
overestimated, no serious distortion resulted. Therefore, if
there is any ambiguity in determining the number of sources,
the largest possible number should be used.
The success of the proposed algorithm depends on how
accurately the noise covariance matrix is estimated. Thisaccuracy is primarily determined by the number of time
points used for the estimation. It is, however, not easy todetermine the number needed to give an accurate estimate of
the covariance matrix, because this number depends on various
conditions during data acquisition, such as the amplitude of thenoise and the source and detector configurations. Therefore,

The authors would like to thank Dr. A. Oppelt and Dr. B.
Scholz for the discussions that led to the development of the
proposed method. They would also like to thank to Dr. A.
Paulraj for his insightful comments on the MUSIC algorithm
and other high-resolution eigenstructure methods. They also
thank E. Yellin for his help in preparing the biomagnetic data.
This work was carried out as part of the MIT-JST International
Research Project “Mind Articulation.”
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[12] C. Eulitz, C. Pantev, E. Diesc, S. Hampson, and T. Elbert, “Magnetic
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evoked by five Japanese vowels,” in Proc. 14th Int. Conf. Acoustics, M.
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[14] P. Stoica and A. Nehorai, “MUSIC, maximum likelihood, andCramer–Rao bound: Further results and comparisons,” IEEE Trans.
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Kenusuke Sekihara (M’88) received the M.S. degree in 1976 and the Ph.D. degree in 1987, bothfrom the Tokyo Institute of Technology, Tokyo,
Japan.
Since 1976, he has worked with Central ResearchLaboratory, Hitachi, Ltd., Tokyo, Japan. He was avisiting Research Scientist at Stanford University,
Stanford, CA, from 1985 to 1986 and at Basic
Development, Siemens Medical Engineering, Erlangen, Germany, from 1991 to 1992. He is currently
working with Mind Articulation research projectsponsored by Massachusetts Institute of Technology and Japan Scienceand Technology Corporation. His research interests include the biomagnetic
inverse problems and statistical estimation theory, especially its application tononinvasive measurements of brain functions.
Dr. Sekihara is a Member of IEEE Medicine and Biology Society, IEEE
Signal Processing Society, and International Society of Magnetic Resonancein Medicine.

David Poeppel received the B.S. degree and Ph.D.
degree in cognitive neuroscience from the Massachusetts Institute of Technology, Cambridge, in1990 and 1995, respectively.
He works in the Biomagnetic Imaging Laboratory, Department of Radiology, University of California at San Francisco. His research interests include the neural basis of speech and language processing and functional brain imaging.

847

Alec Marantz received the B.A. degree in psycholinguistics from Oberlin College, Oberlin, OH,
in 1978 and the Ph.D. degree in linguistics from
the Massachusetts Institute of Technology (M.I.T.),
Cambridge, in 1981.
He joined the faculty at M.I.T. in 1990, where heis currently Professor of Linguistics in the Department of Linguistics and Philosophy. His researchinterests include the syntax and morphology ofnatural languages, linguistic universals, and the neurobiology of language. He is currently involved
in revising morphological theory within linguistics and in exploring MEG
techniques to uncover how the brain processes language.

Hideaki Koizumi was born in Tokyo, Japan, in1946. He graduated from the Department of Pureand Applied Sciences, the University of Tokyo,
Japan, in 1971. He received the Doctoral degree inphysics from the University of Tokyo, in 1976, for
his thesis creating polarized Zeeman-effect atomic
absorption spectrometry.
He joined Hitachi, Ltd., Tokyo, Japan, in 1976
and was a Visiting Faculty Member at the Lawrence
Berkeley Laboratory, University of California, Livermore, from 1978 to 1979. Upon returning toJapan, he led MRI development projects. He now leads projects on environmental measurement and analysis, and noninvasive higher-order brain function
analysis. He is a Chief Research Scientist at the Central Research Laboratory,
Hitachi, Ltd. and a Professor of the Research Institute for Electronic Science,
Hokkaido University, Sapporo, Japan.
Dr. Koizumi has received many awards, such as the Ohkochi Memorial
Prize, the Prize of Science and Technology Minister, and IR-100. He is alsoa member of the Board of Directors of the Chemical Society of Japan.

Yasushi Miyashita received the Ph.D. degree inphysiology from the University of Tokyo, Japan, in
1979.
He is currently Professor and Chairman of the
Physiology Department, the University of Tokyo
School of Medicine, Tokyo, Japan. His research interests include neural basis of cognition in primates,
functional imaging, and image processing with MRI,
MEG, and optical devices.