Revealing Time-Unlocked Brain Activity from MEG
Measurements by Common Waveform Estimation
Yusuke Takeda1*, Kentaro Yamanaka2, Noriko Yamagishi3,4,5, Masa-aki Sato1
1 Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, Kyoto, Japan, 2 Graduate School of Human Life Sciences, Showa Women’s
University, Tokyo, Japan, 3 Department of Cognitive Neuroscience, ATR Cognitive Mechanisms Laboratories, Kyoto, Japan, 4 Brain Networks and Communication
Laboratory, Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka, Japan, 5 Japan Science andTechnology Agency, PRESTO, Saitama, Japan

Abstract
Brain activities related to cognitive functions, such as attention, occur with unknown and variable delays after stimulus
onsets. Recently, we proposed a method (Common Waveform Estimation, CWE) that could extract such brain activities frommagnetoencephalography (MEG) or electroencephalography (EEG) measurements. CWE estimates spatiotemporal MEG/EEG
patterns occurring with unknown and variable delays, referred to here as unlocked waveforms, without hypotheses about
their shapes. The purpose of this study is to demonstrate the usefulness of CWE for cognitive neuroscience. For thispurpose, we show procedures to estimate unlocked waveforms using CWE and to examine their role. We applied CWE tothe MEG epochs during Go trials of a visual Go/NoGo task. This revealed unlocked waveforms with interesting properties,
specifically large alpha oscillations around the temporal areas. To examine the role of the unlocked waveform, we
attempted to estimate the strength of the brain activity of the unlocked waveform in various conditions. We made a spatial
filter to extract the component reflecting the brain activity of the unlocked waveform, applied this spatial filter to MEG data
under different conditions (a passive viewing, a simple reaction time, and Go/NoGo tasks), and calculated the powers of theextracted components. Comparing the powers across these conditions suggests that the unlocked waveforms may reflect
the inhibition of the task-irrelevant activities in the temporal regions while the subject attends to the visual stimulus. Our
results demonstrate that CWE is a potential tool for revealing new findings of cognitive brain functions without any
hypothesis in advance.
Citation: Takeda Y, Yamanaka K, Yamagishi N, Sato M-a (2014) Revealing Time-Unlocked Brain Activity from MEG Measurements by Common Waveform
Estimation. PLoS ONE 9(5): e98014. doi:10.1371/journal.pone.0098014
Editor: Gareth R. Barnes, University College of London - Institute of Neurology, United Kingdom
Received December 9, 2013; Accepted April 28, 2014; Published May 30, 2014
Copyright: ! 2014 Takeda et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by contracts with the National Institute of Information and Communications Technology entitled, ‘‘Multimodal integration
for brain imaging measurements’’ and ‘‘Development of network dynamics modeling methods for human brain data simulation systems’’, and JSPS KAKENHI
Grant Number 23700499. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: takeda@atr.jp

Introduction

channels, frequencies and time. To preserve the rich informationof unlocked activities, it is preferable to estimate their waveforms.
Some studies have proposed methods to estimate waveforms of
unlocked activities [4–6]. However, these methods have alimitation in that they assume only one waveform in a MEG/
EEG epoch and cannot estimate multiple waveforms. In the caseof cognitive stimulus-response tasks, such as Go/NoGo tasks, it isassumed that there are three types of waveforms: stimulus-locked,
response-locked, and unlocked. Therefore, to estimate unlocked
waveforms from MEG/EEG data during cognitive tasks, estimating multiple waveforms is necessary. Recently, we proposed a
general method to estimate MEG/EEG waveforms that arecommon across trials (Common Waveform Estimation, CWE) [7].
The main advantage of CWE is its ability to estimate multiple
waveforms without hypotheses about their shapes, even if theirdelays are variable and unknown. Therefore, applying CWE is asuitable solution for the estimation of unlocked waveforms instudies of cognitive functions.
However, the second difficulty, interpretation of the estimatedunlocked waveforms, remains to be solved. Many studies havealready examined the roles of MEG/EEG waveforms bycomparing their amplitudes across different conditions. For

Brain activities related to cognitive functions, such as attention
and decision making, occur with unknown and variable delays
after stimulus onsets. Therefore, to reveal such brain activities
from magnetoencephalography (MEG) or electroencephalography(EEG) measurements, it is necessary to examine spatiotemporal
MEG/EEG patterns occurring with unknown and variable delays
after stimulus onsets, which are referred to as unlocked waveforms
in this paper. There are two difficulties in examining unlocked
waveforms: their estimation and their interpretation.
The conventional averaging procedures, such as stimulustriggered averaging, cannot be used to estimate unlocked
waveforms because they cancel out the unlocked waveforms
owing to their variable delays. Previously, several methods havebeen proposed for examining unlocked brain activities, such as
using time-frequency analyses [1–3]. To avoid the cancellation of
unlocked activities, time-frequency analyses are first applied tosingle-trial EEG epochs, and then the obtained time-frequency
powers are averaged across trials. The limitation of this method isthat it only provides power information on the unlocked activities,
while some information is lost, such as phase relations across

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Revealing Time-Unlocked Brain Activity

were conducted using the remaining eight subjects (ages 32.167.1(mean 6 standard deviation [SD]), including one female).

example, the role of P300 was examined by comparing amplitudes
across target and non-target conditions. If the amplitude is larger
in one condition that needs a specific brain function, such asattention, the waveforms are believed to reflect this. In thisprocedure, it is implicitly assumed that compared waveforms
reflect the same brain function based on the fact that they occur
with the same delay to the same event, such as stimulus onsets, insimilar conditions. If waveforms in different conditions reflect
different brain functions, such as attention and decision making,comparing their amplitudes does not make sense. Because they are
not time-locked to externally observable events, it is not clear
whether unlocked waveforms estimated from different conditions
reflect the same brain function. Therefore, comparing amplitudes
of unlocked waveforms may result in a misleading interpretation.
This difficulty of interpretation discourages the use of CWE forexamining the cognitive brain activities occurring with unknown
and variable delays.
The purpose of this study is to demonstrate the usefulness of
CWE in cognitive neuroscience studies. Toward achieving thisaim, we show the procedure used to examine the role of theunlocked waveform. In this procedure, we attempted to estimate
the strength of the brain activity of the unlocked waveform undervarious conditions. We made a spatial filter to extract thecomponent that reflects the target brain activity corresponding
to the unlocked waveform. This spatial filter was applied to MEG
data under various conditions. The powers of the components
extracted by the spatial filter were calculated while assuming thatthese powers indicate the strengths of the target brain activity.
This study was conducted in a data-driven way. MEG epochs
during the Go trials of a visual Go/NoGo task were assumed toconsist of stimulus-locked, response-locked, and unlocked waveforms. Without any hypotheses on their shapes, using CWE
allowed us to estimate the three waveforms from the MEG epochs.
We found that the estimated unlocked waveforms had large alphaoscillations at 8–10 Hz around the temporal areas. Together withthe suggested roles of alpha oscillations in previous studies [8–13],
we hypothesized that the unlocked waveforms reflected theinhibition of the task-irrelevant activities in the temporal regions
while the subject attends to the visual stimulus. We calculated thepowers of the components reflecting the brain activities of theunlocked waveforms under different conditions: a passive viewing,a simple reaction time, and Go/NoGo tasks. Using these powers,
we tested our hypothesis in the following three ways. First, toexamine whether the unlocked waveforms are related to attention,
we compared the powers across conditions that either require or
do not require attention. Second, to examine whether theunlocked waveforms reflect the inhibition of the task-irrelevant
activities, we examined the relation between the powers and thevariability of reaction times (RTs) under the Go condition. Finally,
to examine whether the unlocked waveforms are specificallyrelated to ‘‘visual’’ attention, we compared the powers between the‘‘visual’’ and ‘‘auditory’’ Go/NoGo conditions. The above
analyses all supported our hypothesis.

Experimental Design
We conducted MEG and functional Magnetic ResonanceImaging (fMRI) experiments. The fMRI data were used as priorinformation in estimating the current sources of the MEG
waveforms [14].
For the MEG and fMRI experiments, the same experimental
stimuli and event-related design were used (Figure 1). Each trial
began with a warning beep. Following the beep, a white cross waspresented for a variable duration of 1–1.5 s, and subjects were
instructed to fixate on it. Then a cue stimulus ‘‘.’’ or ‘‘,’’ waspresented for 1 s to instruct subjects to initiate a task. Cue stimuli‘‘.’’ and ‘‘,’’ were presented in random order at equal
probability. The cue stimulus onset is referred to as the stimulus
onset. Following the cue stimulus, a gray cross was presented for avariable duration of 3–4 s. Subjects were allowed to blink only
during this period. Each run consisted of 50 trials. Different runs
had different sequences.
Three tasks were conducted in the following order: a passive
viewing (Passive) task, a simple reaction time (SRT) task, and a
Go/NoGo task. In the Passive task, subjects passively viewed thecue stimulus. In the SRT task, subjects pushed a button with theirright index finger immediately after the stimulus onset. In the Go/
NoGo task, subjects pushed a button with their right index finger
immediately after the ‘‘.’’ stimulus onset (Go) and did not push itafter the ‘‘,’’ stimulus onset (NoGo). We set the Go condition as
the target, in which unlocked waveforms were examined, and used
the other conditions for comparison. For the Passive and SRT
tasks, all subjects participated in two runs (100 trials). For the Go/
NoGo task, one subject participated in five runs (125 Go and
NoGo trials) and the other subjects participated in four runs (100Go and NoGo trials).

Data Acquisition
MEG recording. A whole-head 400-channel system (210channel Axial Gradiometer and 190-channel Planar Gradiometer;
PQ1400RM; Yokogawa Electric Co., Japan) was used for the
MEG recording. The sampling frequency was 1 kHz. An electrooculogram (EOG) value was simultaneously recorded. Before the
MEG experiment, the subject’s face and head shape were scanned
using a hand-held laser scanner and a stylus marker (FastSCAN
Cobra; Polhemus, U.S.A.) for later co-registration of the MEG
and MRI results. To measure the head position in the MEG
system, four calibration coils were bilaterally mounted on thesubject’s temporal skin (two for the superior superciliary and twofor anterior subauricular regions). Electromagnetic calibration of
the coil positions was conducted before and after each MEG
recording run by applying alternating currents to the coils.
MRI recording. Three Tesla MR scanners (MAGNETOM
Trio 3T for three subjects and MAGNETOM Verio 3T for theothers; Siemens, Germany) were used to obtain the structural andfunctional MRI data.
The following are the acquisition parameters for the T1weighted images: repetition time 2250 ms, time of echo 3.06 ms,
flip angle 9u, slice thickness 1 mm, field of view 256|256 mm,
imaging matrix 256|256 and 192 slices.
The following are the acquisition parameters for the echoplanar images (EPIs): repetition time 3 s, time of echo 30 ms, flip
angle 80u, slice thickness 3 mm, field of view 192|192 mm, andimaging matrix 64|64 with 50 slices for the three subjects and 47
slices for the others.

Materials and Methods
Subjects
Nine healthy subjects participated in this study. All gave written
informed consent for the experimental procedures, which wereapproved by the ATR Human Subject Review Committee. All
had normal or corrected-to-normal visual and auditory acuity.
One subject was excluded owing to too many wrong responses(about 30%) in the Go trials of the Go/NoGo task. All analyses

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Revealing Time-Unlocked Brain Activity

Figure 1. Experimental design. Each trial began with a warning beep. Following the beep, a white cross was presented for a variable duration of
1–1.5 s. Subjects were instructed to fixate on it. Then a cue stimulus, ‘‘.’’ or ‘‘,’’, was presented for 1 s to instruct subjects to initiate the task.
Following the cue stimulus, a gray cross was presented for a variable duration of 3–4 s. Subjects were allowed to blink only during this period.
doi:10.1371/journal.pone.0098014.g001

Data Analysis
MEG data. Preprocessing. To remove artifacts and noisefrom the MEG data and reshape them for the common waveform
estimation, the MEG data were passed through a low-pass finite
impulse response (FIR) filter with a cutoff frequency of 40 Hz,
sampled at 100 Hz, and passed through a high-pass FIR filter witha cutoff frequency of 2 Hz. Using the reference sensor data,
environmental noise was removed by time-shift Principal Component Analysis (PCA) [15]. The MEG data were segmented into
5-s epochs from 2 s before the stimulus onset to 3 s after it. For
each sensor, EOG artifacts were removed by generating a multiple
linear regression model to predict eye-movement-related components in the MEG epochs by the EOG data and then removing theprediction from the MEG epochs. RTs were defined as theintervals between the stimulus onsets and the button-push signal(response) onsets. We excluded the trials in the SRT task and the
Go condition with RTs either shorter than 0.1 s or longer than
0.8 s. Trials were also excluded if subjects made button-push
responses in the Passive task and the NoGo condition. If the
maximum value of the MEG epoch from 0.5 s before the stimulus
onset to 1 s after it exceeded 2|10212 T absolute value, we
excluded that trial. Cardiac artifacts and sensor noise were
removed by Independent Component Analysis (ICA) [16] using
MATLAB code provided by Makeig et al. [17]. We obtained
99.960.4 trials for the Passive task, 97.464.3 trials for the SRT
task, 100.669.7 trials for the Go condition, and 98.163.9 trials for
the NoGo condition. The mean RTs for the SRT task and Go
conditions were 0.2960.05 s and 0.3860.04 s, respectively.
Common Waveform Estimation. To estimate unlocked waveforms, we applied CWE to the preprocessed 400-channel MEG
epochs of the Go condition.
CWE is a general method for estimating waveforms that arecommon across trials from MEG/EEG epochs. When waveforms
spatiotemporally overlap, the averaging procedure cannot estimate
exact waveforms because they are mutually contaminated [18–23]. Furthermore, when the delays of waveforms are variable andunknown, the averaging procedure cannot be used [3,24]. CWE
was proposed to overcome these limitations and provide a way towork in more general and severe situations, where we do not know
the number of waveforms common across trials, their delays inindividual trials, and all of the waveforms. In CWE, a MEG epochof a channel is assumed to consist of waveforms common acrosstrials and is expressed by

PLOS ONE | www.plosone.orgyn(ch) (t)~

K
P

k~1

(ch)
x(ch)
k (t{tn,k )zvn (t),ð1Þwhere yn(ch) (t) represents a MEG epoch of channel ch in trial n,xk(ch) (t) represents the k-th waveform of channel ch, tn,k represents(ch)the delay of x(ch)k (t) in trial n, vn (t) represents the noise ofchannel ch in trial n, and K represents the number of waveforms.
Only from yn(ch) (t), CWE estimate K, xk(ch) (t), and tn,k . In the
Fourier domain, they are iteratively searched for, with the aim ofminimizing the residual error between the observed and reconstructed MEG epochs. Using CWE, we can obtain exact
waveforms, which are not contaminated with each other, without
any knowledge of their shapes and delays in individual trials.
In this experimental design, we assumed three waveforms in a
MEG epoch: a stimulus-locked waveform, a response-locked
waveform, and an unlocked waveform. We set the delays of thestimulus-locked waveforms to 0 s and the delays of the responselocked waveforms to the RTs. That is, we simplified theassumption from Eq. (1) toyn(ch) (t)~s(ch) (t)zr(ch) (t{rtn )zu(ch) (t{tn )zv(ch)n (t),

ð2Þ

where s(ch) (t) represents the stimulus-locked waveform of channelch, r(ch) (t) represents the response-locked waveform of channel ch,u(ch) (t) represents the unlocked waveform of channel ch, rtn
represents the RT in trial n, and tn represents the delay of theunlocked waveform in trial n. By CWE, we estimated s(ch) (t),r(ch) (t), u(ch) (t), and tn from yn(ch) (t) and rtn . We searched for thedelays of the unlocked waveforms by setting the initial delays toGaussian random numbers (0.3560.20 s) and the range of thedelay to 60.25 s. After the search, it was necessary to adjust the
averages across the trials of the delays. This is because the delays
are defined as the interval between the stimulus onsets and theonsets of the unlocked waveforms, and the onsets of the unlocked
waveforms are arbitrarily determined when searching for thedelays. We adjusted them so that the delays represented theinterval between the stimulus onsets and the maximum peak of theunlocked waveforms.
Comparing Estimated Waveforms with Averages across Trials.
To examine the validity of the estimation, the estimated stimuluslocked waveforms, response-locked waveforms, and unlocked3

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Revealing Time-Unlocked Brain Activity

the response-locked waveforms, and 20.25 s and 0.25 s after the
peak for the unlocked waveforms. Powers over 0.3 of the
maximum value across the dipoles are shown.
Relation between Unlocked Waveform and Alpha Oscillations.
The unlocked waveforms have alpha oscillations at 8–10 Hz (seeFigure 4A). Therefore, we examined the relation between theunlocked waveform and alpha oscillations originally included in
the MEG epochs during the Go condition.
The alpha oscillations in the MEG epochs were extracted byfiltering the MEG epochs with a FIR bandpass filter of 8–10 Hz
and then aligning the filtered MEG epochs to the estimated delaysof the unlocked waveform.
We compared amplitude distributions over space between theunlocked waveform and the alpha oscillations of the original MEG
epochs. The amplitude of the unlocked waveform was calculatedbywaveforms were compared with the averages across the trials of
the MEG epochs that were triggered on the stimulus onsets(stimulus-triggered averages), the response onsets (responsetriggered averages), and the estimated delays of the unlocked
waveforms (estimated delay-triggered averages), respectively.
Theoretically, the stimulus-triggered average should be more orless different from the stimulus-locked waveform. According to Eq.
(2), the stimulus-triggered average is expressed by
N
N
1X
1X
yn(ch) (t)~s(ch) (t)zr(ch) (t{rtn )
N n~1
N n~1

z

N
N
1X
1X
u(ch) (t{tn )zv(ch) (t),N n~1
N n~1 nvffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u
t2
X
u
1
t
DHu (t)D2 ,t2 {t1 z1 t~t

where N represents the number of trials. This includes not only
the stimulus-locked waveform but also contamination by the otherwaveforms (the second and third terms on the right side). This isalso true for the response-triggered average. Indeed, the stimulus/
response-locked waveforms are different from the stimulus/
response-triggered averages to some extent (see Figures 2A and
3A). The differences are attributed to contamination by the
response/stimulus-locked waveforms in the stimulus/responsetriggered averages [18–23]. To confirm this assumption, we
simulated the contaminated waveforms and compared them withthe stimulus/response-triggered averages. Stimulus-locked waveforms contaminated by response-locked waveforms were calculatP(ch)(t{rtn ). Response-locked waveed by s(ch) (t)z1=N N
n~1 r
forms contaminated by stimulus-locked waveforms were calculatedP(ch)(tzrtn ).
by r(ch) (t)z1=N N
n~1 s
Current Source Estimation of Waveforms. To examine thecurrent sources of the estimated waveforms, we applied Variational Bayesian Multimodal EncephaloGraphy (VBMEG) [14] tothe estimated waveforms of the 210 axial sensors.
From MEG data, VBMEG estimates distributed currents using
fMRI data as the prior information on the current variancedistribution. The reliability of VBMEG has been confirmed byboth computer simulation [14,25] and experiment [26–32].
For each subject, a polygon model of the cortical surface wasconstructed based on MR structural images using FreeSurfersoftware [33]. We assumed about 3,000 single-current dipoles
equidistantly distributed on and perpendicular to the corticalsurface. The fMRI information from the Go condition was used
for the prior information on the current variance distribution. The
variance magnification parameter was set at 100 [26]. The
confidence parameter was set at 10 [26]. A spatial smoothness
constraint on the current distribution along the cortical surface
was incorporated into the estimation (6 mm full-width at halfmaximum). We assumed that the pattern of the cortical activity
changes with time. Therefore, we divided the estimated waveforms
into time windows (100-ms long with 50-ms overlap). Then, we
separately calculated an inverse filter for each time window.
Cortical currents were estimated every 10 ms (100 Hz) from theestimated waveforms using the filter. In the overlap periods, they
were averaged between two time windows.
Here, we show the power distribution over the cortex of theestimated currents. The power was obtained by calculating!
Pt2
2
t~t1 DHcur (t)D (t2 {t1 z1), where Hcur (t) is the Hilbert transform of the estimated current at a dipole, and t1 and t2 are,respectively, 0 s and 0.5 s after the stimulus onset for the stimuluslocked waveforms, 20.25 s and 0.25 s after the response onset for
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1

ð3Þ

where Hu (t) is the Hilbert transform of the unlocked waveform
at a sensor, and t1 and t2 are 20.5 s and 0.5 s, respectively, after
the peak of the unlocked waveform. The amplitude of the alphaoscillations in the MEG epochs was calculated by
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiP
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
qffiffiffiffiffiffiffiffiffiffiP
t2
2
1=N N
1=(t
{t
z1)
2
1
t~t1 DHa (t,n)D , where Ha (t,n) is
n~1
the Hilbert transform of the alpha oscillation in the MEG epoch
at a sensor in trial n, and t1 and t2 are 20.5 s and 0.5 s,
respectively, after the peak of the unlocked waveform.
To focus on the similarity and difference between the two
amplitude distributions over space (Figure 5A and B), we selected
three channels (CHs 1–3). At CHs 1 and 2, the unlocked
waveform and the alpha oscillations in the MEG epochs havelarge amplitudes in common. At CH 3, the alpha oscillations in the
MEG epochs have large amplitudes but the unlocked waveform
does not have these.
To show the reasons for the similarity and difference visually,
we prepared time-trial images [16] for CHs 1–3. In these images,
the alpha oscillations in the MEG epochs are shown as colorcoded horizontal lines.
To confirm the reasons for the similarity and difference
quantitatively, we calculated phase-locking values .
(PLVs) forPN
CHs 1–3. The PLV was defined as D n~1 exp (jwn (t))D N, wherewn (t) is the angle of the Hilbert transform of the alpha oscillationin the MEG epochs in trial n [34].
Properties of Unlocked Waveforms. We examined the properties of the unlocked waveforms in the following three ways.
First, we examined the amplitude distributions over space of theunlocked waveforms. The amplitude was calculated by Eq. (3),where t1 and t2 are 20.5 s and 0.5 s, respectively, after the peak of
the unlocked waveform. The similarity of the amplitude distributions across the subjects was quantified by calculating thecorrelation coefficient. For each subject, we calculated thecorrelation coefficient between his/her own and the mean of theothers’ amplitude distributions. A two-tailed sign test was
conducted to assess the null hypothesis that the medium value ofthe correlation coefficients was 0.
Second, we examined the power spectra of the unlocked
waveforms. The power spectrum was obtained by taking thediscrete Fourier transform of h(t)u(ch) (t), where h(t) is the Hanningwindow.
4

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Revealing Time-Unlocked Brain Activity

Figure 2. Stimulus-locked waveform (subject 8). A: Time course of stimulus-locked waveform. Blue and red lines represent stimulus-locked
waveform and stimulus-triggered average. Black lines represent stimulus-locked waveform contaminated by response-locked waveform. Vertical lines
represent stimulus onset. Framed plots are enlarged on the right (a and b). B: Spatial patterns of stimulus-locked waveform at 0.13 s (upper) and
0.2 s (lower) after stimulus onset. C: Current sources estimated from stimulus-locked waveform. Powers averaged across time from 0 to 0.5 s after
stimulus onset are shown. Other subjects show similar results.
doi:10.1371/journal.pone.0098014.g002

prior knowledge of the source. The spatial filter to separate thesource was constructed using the prior knowledge as follows.
1. A data matrix of size ½C,T,N $ (channeltimetrial) during the
Go condition was prepared.
2. The data matrix was reorganized by concatenating the trials
along the time dimension to obtain a matrix of size ½C,TN $.
3. Using PCA, the reorganized data matrix was whitened, i.e.
the variance for each channel was normalized, and a whitening
matrix W was obtained.
4. The whitened data matrix was reorganized again by trials toform a 3D matrix of size ½C,T,N $.
5. By CWE, the reorganized matrix was decomposed into three
2D matrices of size ½C,T $: stimulus-locked, response-locked, andestimated delay-locked.
6. A second PCA was performed on the third decomposed
matrix, i.e. the estimated delay-locked matrix, and a rotation
matrix R was obtained.
7. The first column of RW, which corresponds to the maximum
variance component, was used as the spatial filter.
The above procedure finds the spatial filter to extract thecomponent (½1,T,N $), which includes the large temporal pattern(½1,T $) time-locked to the delays of the unlocked waveform. The
variance of the temporal pattern over time is maximum under the

Finally, we examined the correlations between the RTs of the
Go condition and the estimated delays of the unlocked waveforms.
For each subject, the correlation coefficient between the RTs andthe estimated delays of the unlocked waveform was calculated.
Role of Unlocked Waveform. We examined the role of theunlocked waveform. The roles of MEG/EEG waveforms, such as
P300, have been examined by comparing their amplitudes acrossdifferent conditions, implicitly assuming that the compared
waveforms reflect the same brain function. However, thisprocedure cannot be used for unlocked waveforms because
unlocked waveforms estimated from different conditions may notreflect the same brain function.
In this study, to examine the role of the unlocked waveform, we
attempted to estimate the strength of the unlocked waveform’s
brain activity occurring under the various conditions. Using the
Denoising Source Separation (DSS) framework [35,36], weconstructed a spatial filter to extract the component reflecting
the brain activity of the unlocked waveform. In the DSS
framework, spatial filters for separating desired sources can beconstructed using prior knowledge about the sources. In this case,the brain activity reflected by the unlocked waveform wasregarded as the source. The latencies of the source, which are
the estimated delays of the unlocked waveform, were regarded as

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Revealing Time-Unlocked Brain Activity

Figure 3. Response-locked waveform (subject 8). A: Time course of response-locked waveform. Blue and red lines represent response-locked
waveform and response-triggered average. Black lines represent response-locked waveform contaminated by stimulus-locked waveform. Verticallines represent response onset. Framed plots are enlarged on the right (a and b). B: Spatial patterns of response-locked waveform at 20.1 s (upper)and 0.02 s (lower) after response onset. C: Current sources estimated from response-locked waveform. Powers averaged across time from 20.25 s to
0.25 s after response onset are shown. Other subjects show similar results.
doi:10.1371/journal.pone.0098014.g003

assess the null hypothesis that the medium value of the relative
powers was 1 with Bonferroni correction.
To examine the relation between the powers and the variabilityof the RTs in the Go condition, for each subject we obtained
normalizedbycalculating#powers
hP
i
Pt2
t2
2
2
where
max
t~t1 DHcompGo (t,n)D
t~t1 DHcompGo (t,n)D ,condition that the variance of the component over time and trials
is one. Therefore, the spatial filter enhances the brain activity that
occurred at the estimated delays of the unlocked waveform in thesame manner across the trials, i.e. the brain activity of theunlocked waveform. By applying the spatial filter to the MEG dataof the various conditions, the brain activity of the unlocked
waveform can be extracted from the various conditions. We
compared the powers of the extracted component across theconditions, assuming that the powers indicate the strengths of thebrain activity reflected by the unlocked waveform.
The time course of the power was .
calculated by taking thePN
2moving average ofDH(t,n)DN, where the window
comp
n~1

n

HcompGo (t,n) is the Hilbert transform of the component of the
Go condition in trial n, t1 and t2 are 0 s and 0.5 s, respectively,
after the stimulus onset, and max½An $ is the maximum value of Annacross n. According to the normalized powers, the trials in the Go
condition were divided into two groups: smaller power group andlarger power group. For each group and subject, the SD of theRTs was calculated. A one-tailed Wilcoxon signed-ranks test was
conducted to assess the null hypothesis that the SDs of the twogroups did not differ.
fMRI data. To obtain the prior information used inestimating the current sources from the estimated waveforms[14], we analyzed the fMRI data during the Go condition by
SPM8 (Welcome Department of Cognitive Neurology, UK).
The head motion and slice-timing were corrected, and theimages were smoothed using an 8-mm full-width at half-maximum

width of the moving average is 0.2 s and Hcomp (t,n) is the Hilberttransform of the component in trial n. To compare the powers
across the conditions,
we obtained relative powers by calculating
.Xt
Xt2
2
P(t)
PGo (t), where PGo (t), P(t) are respectively
t~t
t~t
1

1

the time courses of the powers of the Go condition and anothercondition, and t1 and t2 are 0 s and 1 s, respectively, after the
stimulus onset. The relative power is .1 if the power of thecomponent of a condition is larger than that of the Go condition,and ,1 if it is smaller. A two-tailed sign test was conducted to

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Figure 4. Unlocked waveform (subject 8). A: Time course of unlocked waveform. Blue and red lines represent unlocked waveform andestimated delay-triggered average. Vertical lines represent peak time of unlocked waveform. Framed plots are enlarged on the right (a and b). B:
Spatial patterns of unlocked waveform at 20.02 s (upper) and 0.03 s (lower) after peak of unlocked waveform. C: Current sources estimated from
unlocked waveform. Powers averaged across time from 20.25 s to 0.25 s after peak of unlocked waveform are shown. Other subjects show similar
results.
doi:10.1371/journal.pone.0098014.g004

triggered on the stimulus onsets (red lines). Around the occipitalareas, the stimulus-locked waveform has as large an amplitude as
the stimulus-triggered average (Figure 2Ab, blue and red lines).
Around the central and left temporal areas, where the responselocked waveform is large (Figure 3A, blue lines), and around themean response onset, the stimulus-locked waveform is different
from the stimulus-triggered average (Figure 2Aa, blue and redlines). This suggests that the difference is due to the contaminationby the response-locked waveform in the stimulus-triggered average[18–23]. To confirm this conjecture, the stimulus-locked waveform contaminated by the response-locked waveform was calculated (Figure 2A, black lines). This made the difference disappear(Figure 2Aa, black and red lines). Therefore, CWE successfully
separated the stimulus-locked waveform from the contaminationof the response-locked waveform, while the stimulus-triggered
averaging procedure did not.
Figure 2B shows the spatial patterns of the stimulus-locked
waveform at 0.13 and 0.2 s after the stimulus onset. The stimuluslocked waveform is large around the occipital areas.
Figure 2C shows the powers of the current sources estimatedfrom the stimulus-locked waveform. The currents have large
amplitudes at the right superior occipital gyrus (indicated byarrows).

(FWHM) Gaussian filter. The time series of all voxels were highpass filtered to 1/128 Hz.
Statistical analyses were performed for each subject in the eventrelated design. The time course of the events, which is the stimulus
onset, was convolved with a canonical hemodynamic response
function to yield regressors in a general linear model. A parameter
was estimated for each regressor, and a t-value of the parameter
was calculated for each voxel. The resulting t-values were used forthe prior information on the current variance distribution [14].

Results
Estimated Waveforms
By using CWE, three types of waveforms were estimated from
the MEG epochs of the Go condition: stimulus-locked, responselocked, and unlocked. First, we show the three waveforms. Then,
we show the relation between the unlocked waveform and thealpha oscillations originally included in the MEG epochs during
the Go condition. Finally, we show the properties of the unlocked
waveforms.
Stimulus-locked waveform. Figure 2A shows the stimuluslocked waveform estimated by CWE (blue lines) at 19 axial
sensors. For comparison, we also show the stimulus-triggered
average, which was obtained by averaging the MEG epochs
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Figure 5. Relation between unlocked waveform and alpha oscillations in MEG epochs during Go condition (subject 8). A: Amplitude
distribution over space of unlocked waveform. B: Amplitude distribution over space of alpha oscillations in MEG epochs. C: Selected channels: CHs 1–
3. At CHs 1 and 2, unlocked waveform and alpha oscillations in MEG epochs have large amplitudes in common. At CH 3, alpha oscillations in MEG
epochs have large amplitudes but unlocked waveform does not. D: Time-trial images of alpha oscillations in MEG epochs at CHs 1–3. Vertical dottedlines represent peak time of unlocked waveform. E: PLVs at CHs 1–3. In D and E, time 0 corresponds to peak of unlocked waveform. Other subjects
show similar results.
doi:10.1371/journal.pone.0098014.g005

Response-locked waveform. Figure 3A shows the responselocked waveform estimated by CWE (blue lines) at the 19 axial
sensors. For comparison, we also show the response-triggered
average, which was obtained by averaging the MEG epochs
triggered on the response onsets (red lines). Around the central andleft temporal areas, the response-locked waveform has as large anamplitude as the response-triggered average (Figure 3Aa, blue andred lines). Around the occipital areas, where the stimulus-locked
waveform is large (Figure 2A, blue lines), and before the response
onset, the response-locked waveform is different from theresponse-triggered average (Figure 3Ab, blue and red lines). Thissuggests that the difference is due to the contamination by thestimulus-locked waveform in the response-triggered average [18–23]. To confirm this conjecture, the response-locked waveform
contaminated by the stimulus-locked waveform was calculated(Figure 3A, black lines). This made the difference disappear(Figure 3Ab, black and red lines). Therefore, CWE successfully
separated the response-locked waveform from the contaminationof the stimulus-locked waveform, while the response-triggered
averaging procedure did not.
Figure 3B shows the spatial patterns of the response-locked
waveform at 20.1 and 0.02 after the response onset. The
response-locked waveform is large around the central and left
temporal areas.

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Figure 3C shows the powers of the current sources estimatedfrom the response-locked waveform. The currents have large
amplitudes at the left precentral gyrus and the central sulcus(indicated by arrows). This is believed to reflect the right finger
movement of pushing the button.
Unlocked waveform. Figure 4A shows the estimatedunlocked waveform (blue lines) at the 19 axial sensors. To ourknowledge, no method other than CWE can simultaneously
estimate the unlocked waveform along with the stimulus- andresponse-locked waveforms. The unlocked waveform has largealpha oscillations at 8–10 Hz (see Figure 6B) around the temporal
areas. The estimated delays of the peak in the unlocked waveformare 0.4760.15 s. To confirm that the unlocked waveform is not an
artifact generated by CWE, we compared the unlocked waveformwith the estimated delay-triggered average, which was obtained byaveraging the MEG epochs triggered by the estimated delays(Figure 4A, red lines). The unlocked waveform and the estimateddelay-triggered average do not show clear differences (Figure 4A,
right). This indicates that, at the estimated delays, there is aspatiotemporal pattern that resembles the unlocked waveform,
confirming that the unlocked waveform is not an artifact.
Figure 4B shows the spatial patterns of the unlocked waveform
at 20.02 s and 0.03 s after the peak of the unlocked waveform.
Prominent activities are observed around the temporal areas.

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Revealing Time-Unlocked Brain Activity

Figure 6. Properties of unlocked waveforms. A: Amplitude distribution over space of unlocked waveforms. Amplitudes are averaged acrosssubjects. B: Power spectrum of unlocked waveforms. Powers are averaged across sensors and subjects.
doi:10.1371/journal.pone.0098014.g006

because it does not take into consideration the phase relation
across channels.
Properties of unlocked waveforms. Figure 6A shows theamplitude distribution over space of the unlocked waveforms. In
this figure, the amplitude distributions are averaged across the
subjects. Strong amplitudes are observed around the temporal
areas. The amplitude distributions of the individual subjects
resemble each other (r~0:75+0:08, pv0:05, two-tailed sign test).
Figure 6B shows the power spectrum of the unlocked
waveforms. In this figure, the power spectra are averaged acrossthe sensors and subjects. Strong powers are observed at the alphaband (8–10 Hz).
The RTs of the Go condition and the estimated delays of theunlocked waveforms do not show significant correlations for mostof the subjects (r~0:04+0:12, pw0:05 for 7 of 8 subjects).

Figure 4C shows the powers of the current sources estimatedfrom the unlocked waveform. The currents have large amplitudes
at the left and right superior temporal gyrus (STG) as indicated by
the arrows.
Relation between unlocked waveform and alphaoscillations in MEG data. The unlocked waveform has largealpha oscillations at 8–10 Hz (Figure 4A). Here, we examine therelation between the unlocked waveform and alpha oscillations
originally included in the MEG epochs during the Go condition.
Figures 5A and B show the amplitude distributions over space of
the unlocked waveform and the alpha oscillations in the MEG
epochs, respectively. At CHs 1 and 2 (Figure 5C), the alphaoscillations in the MEG epochs show large amplitudes as well as
the unlocked waveform (Figure 5A and B). At CH 3 (Figure 5C),
the alpha oscillations in the MEG epochs show large amplitudes,
but the unlocked waveform does not have these (Figure 5A and B).
To elucidate the reasons for the similarity and the difference, weconstructed time-trial images of the alpha oscillations in the MEG
epochs at CHs 1–3 (Figure 5D). For CHs 1 and 2, we can observe
oscillations parallel to the peak of the unlocked waveforms
indicated by vertical dotted lines (black dotted lines in
Figure 5D). This indicates that the alpha oscillations at CHs 1
and 2 are time-locked to the peak. In contrast, for CH 3, wecannot observe such oscillations, indicating that the alphaoscillations at CH 3 are not time-locked to the peak.
We can quantitatively confirm these observations from
Figure 5E, which shows the time courses of the PLVs. The PLVs
are large around the peak of the unlocked waveforms for CHs 1
and 2, but not for CH 3. This indicates that the alpha oscillations
are phase-locked to the estimated delays for CHs 1 and 2 but not
for CH 3.
These results show the reason why the amplitudes of theunlocked waveform are large for CHs 1 and 2 but not for CH 3.
The MEG epochs include various sets of alpha oscillations
synchronized across channels. The calculation of the unlocked
waveform aligned one of these sets, which is dominant at CHs 1
and 2. However, it could not simultaneously align another set,
which is dominant at CH 3. As a result, the alpha oscillations at
CHs 1 and 2 remain, but those at CH 3 are cancelled in theunlocked waveform.
In conclusion, the unlocked waveform is considered a set ofalpha oscillations synchronized across channels. The timefrequency analyses [1–3] cannot extract such a set of activities
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Role of Unlocked Waveform
Here, we show the procedure for examining the role of theestimated unlocked waveform.
The conventional procedure used to examine the roles of
MEG/EEG waveforms, such as P300, is to compare theamplitudes of the waveforms across different conditions. If theamplitude is larger in one condition that needs a specific brain
function, the waveforms are assumed to reflect this function. In
this procedure, the compared waveforms are assumed to reflect
the same brain function based on the fact that the waveforms
occur with the same delay after the same event, such as stimulus
onset.
In the case of unlocked waveforms, however, this assumption is
not valid. Unlocked waveforms are not time-locked to observable
events, so it is not clear whether unlocked waveforms estimatedfrom different conditions reflect the same brain function.
Therefore, we cannot use the above procedure.
In this study, we intended to examine the strength of theunlocked waveform’s brain activity occurring in the various
conditions. To do this, we constructed a spatial filter that works as
the extractor of the component reflecting the brain activity of theunlocked waveform (see the ‘‘Data Analysis’’ section for detailed
method). By applying the spatial filter to the MEG data during thevarious conditions, the components reflecting the brain activity of
the unlocked waveform can be extracted from the various
conditions. Comparisons between the conditions were carriedout by computing the powers of the extracted components,
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Figure 7B shows the powers of the components relative to thoseof the Go condition for each subject. For the NoGo condition, therelative powers are around 1 (pw0:05, two-tailed sign test,
Bonferroni-corrected), indicating that the powers of the NoGo
condition are not different from those of the Go condition. For the
Passive task, the relative powers are ,1 for all of the subjects(pv0:05, two-tailed sign test, Bonferroni-corrected), indicatingthat the powers of the Passive task are smaller than those of the Go
condition for all subjects.
These results are consistent with our expectation, suggestingthat the unlocked waveforms are related to attention.
Relation to variability of RTs. We examined whether theunlocked waveforms reflect the inhibition of the task-irrelevant
activities. The task-irrelevant activities are considered sources ofnoise for the task-relevant brain process. Therefore, the inhibitionof the task-irrelevant activities is believed to reduce noise. If noiseis reduced, the brain process for the Go response will be lessperturbed and the motor output will be stabilized, i.e. thevariability of the RTs will be small. Therefore, the hypothesis
expects that the variability of the RTs will be small when the
powers of the unlocked waveforms are large.
Figure 8A shows the scatter plot of the powers and the RTs
during the Go condition. In this figure, the powers are normalized
for each subject so that their maximum value becomes one. The
variability of the RTs seems to be small when the powers are large.
To confirm this observation quantitatively, for each subject, thetrials were divided into two groups according to the normalized
powers: the smaller power group and the larger power group. For
7 out of 8 subjects, the SDs of the RTs in the larger power groupare smaller than those in the smaller power group (pv0:05, onetailed Wilcoxon signed-ranks test) (Figure 8B). This indicates thatthe variability of the RTs is small when the powers are large.
This result is consistent with our expectation, suggesting that theunlocked waveforms reflect the inhibition of the task-irrelevant
activities.

assuming that the powers indicate the strengths of the brain
activities of the unlocked waveforms.
We first present our hypothesis on the role of the unlocked
waveforms and then test it by using the powers.
Hypothesis. The unlocked waveforms have large alphaoscillations at 8–10 Hz around the temporal areas (Figures 4A
and 6B). It is generally believed that the alpha oscillations reflect
the inhibition of task-irrelevant activities [8–13]. Based on theseprevious studies, we hypothesized that the unlocked waveforms
reflected the inhibition of the task-irrelevant activities in thetemporal regions while the subject attends to the visual stimulus.
Indeed, the estimated delays of the unlocked waveforms are notcorrelated with the RTs of the Go condition, suggesting that theunlocked waveforms are not related to the execution of the Go/
NoGo task, such as decision making.
Using the powers of the components reflecting the brain
activities of the unlocked waveforms, we tested the above
hypothesis in the following three ways. First, to examine whether
the unlocked waveforms are related to attention, we compared thepowers across conditions where attention is required or not.
Second, to examine whether the unlocked waveforms reflect the
inhibition of the task-irrelevant activities, we examined the relation
between the powers and the variability of the RTs in the Go
condition. Finally, to examine whether the unlocked waveforms
are specifically related to ‘‘visual’’ attention, we conducted an
additional experiment of an ‘‘auditory’’ Go/NoGo task andcompared the powers between the ‘‘visual’’ and ‘‘auditory’’ Go/
NoGo conditions.
Comparison across conditions where attention isrequired or not. We examined whether the unlocked wave-

forms are related to attention. The Go and NoGo conditions
require that attention be given to the stimulus, but the Passive task
does not. Therefore, the hypothesis expects that the powers of thecomponents will be large for the Go and NoGo conditions but notfor the Passive task.
Figure 7A shows the time courses of the powers of thecomponents. In this figure, the powers are averaged across the
subjects. The powers clearly increase after the stimulus onset for
the Go and NoGo conditions (blue and green lines) but not for the
Passive task (black line).

Additional
experimentwithauditory
Go/NoGotask. We examined whether the unlocked waveforms are

specifically related to ‘‘visual’’ attention. Parts of the task-irrelevant
brain regions during visual attention, such as the auditory cortex,
are task-relevant during auditory attention. Therefore, theFigure 7. Powers of components reflecting brain activities of unlocked waveforms. A: Time courses of powers of components. Powers areaveraged across subjects. Time 0 corresponds to stimulus onset. B: Relative powers of components to those of Go condition. Each line corresponds toone subject.
doi:10.1371/journal.pone.0098014.g007

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Figure 8. Relation between powers of components reflecting brain activities of unlocked waveforms and variability of RTs in Go
condition. A: Scatter plot of normalized power and RTs. Each dot corresponds to one trial of one subject. B: SDs of RTs in smaller and larger powergroups. In both figures, each color corresponds to one subject.
doi:10.1371/journal.pone.0098014.g008

hypothesis expects that the powers will be smaller in the case of theauditory Go and NoGo conditions.
To test this expectation, we conducted an additional experimentof an ‘‘auditory’’ Go/NoGo task and compared the powers
between the ‘‘visual’’ and ‘‘auditory’’ conditions. In this experiment, nine healthy subjects participated, who gave written
informed consent for the experimental procedures that wereapproved by the ATR Human Subject Review Committee. We
conducted visual and auditory Go/NoGo tasks. The visual Go/
NoGo task is the same as the Go/NoGo task described in the‘‘Experimental Design’’ section. The auditory Go/NoGo task isthe same as the visual Go/NoGo task except for the cue stimulus;
an auditory cue stimulus (1500- or 2000-Hz pure tone) waspresented for 1 s instead of the visual stimulus (‘‘.’’ or ‘‘,’’). The
subjects immediately pushed the button with their right index
finger after the presentation of the 2000-Hz pure tone (auditoryGo) but not for the 1500-Hz pure tone (auditory NoGo). During
the tasks, MEG data were recorded as described in the ‘‘Data
Acquisition’’ section. The powers were calculated also as describedin the ‘‘Data Analysis’’ section: the MEG data were preprocessed,
unlocked waveforms were estimated from the preprocessed MEG
epochs during the visual Go condition, spatial filters wereprepared to extract the brain activities of the unlocked waveforms,
the spatial filters were applied to the MEG epochs during theindividual conditions, and the time courses of the powers were
calculated from the components extracted by the spatial filters.
Figure 9A shows the time courses of the powers. In this figure,the powers are averaged across the subjects. For the visual Go and
NoGo conditions (blue and green lines), the powers increaseduring the task period (from 0 to 1 s after the stimulus onset),
which are consistent with the previous results (Figure 7A, blue andgreen lines). For the auditory Go and NoGo conditions (black andred lines), on the contrary, the powers show transient decreases
after the stimulus onset. We averaged the powers from 0 to 1 s
after the stimulus onset for each condition and subject. The
averaged powers of the auditory conditions are significantly
smaller than those of the visual conditions (pv0:05, two-tailed
Wilcoxon signed-ranks test).
This result is consistent with our expectation, suggesting that theunlocked waveforms of the visual Go condition are specificallyrelated to the ‘‘visual’’ attention.
Further, Figure 9A indicates that the brain activities reflected by
the unlocked waveforms of the visual Go condition do not occur
strongly in the case of the auditory conditions. Therefore, if CWE
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were also applied to the MEG epochs during the auditory
conditions, it is expected that unlocked waveforms with different
properties would be estimated. To confirm this expectation, we
estimated unlocked waveforms from all of the individual conditions, and compared their amplitude distributions over spaceacross the conditions.
The unlocked waveforms were estimated as described in the‘‘Data Analysis’’ section except that the response-locked waveforms were not assumed for the NoGo conditions. The amplitude
distributions over space of the unlocked waveforms were
calculated by Eq. (3) as described in the ‘‘Data Analysis’’ section.
Figure 9B shows the amplitude distributions over space of theunlocked waveforms. In this figure, the amplitudes are averaged
across the subjects. For the visual conditions (top figures), large
amplitudes are observed around the temporal areas, which areconsistent with the previous results (Figure 6A). For the auditory
conditions (bottom figures), in contrast, large amplitudes areobserved around the occipital areas. To examine the similarity of
the amplitude distributions over space between the visual Go
condition and another condition, for each subject, we calculated
the correlation coefficient between their amplitude distributions
over space. The correlation coefficients between the visual Go andauditory conditions are significantly lower than those between thevisual Go and NoGo conditions (r~0:84+0:11 for the visual Go
and auditory Go conditions, r~0:83+0:12 for the visual Go andauditory NoGo conditions, r~0:92+0:04 for the visual Go andvisual NoGo conditions, pv0:05, two-tailed Wilcoxon signedranks test). This indicates that the amplitude distributions overspace of the unlocked waveforms of the auditory conditions aredifferent from those of the visual Go condition.
This result is consistent with our expectation, supporting theresults of Figure 9A.

Discussion
In this study, we showed the usefulness of CWE for revealing
cognitive unlocked brain activities. By using CWE, we successfully
estimated the unlocked waveforms from the MEG data during the
Go trials of the visual Go/NoGo task. The unlocked waveforms
have large alpha oscillation at 8–10 Hz around the temporal
areas. Based on the properties of the unlocked waveforms and theprevious studies [8–13], we hypothesized that the unlocked
waveforms reflected the inhibition of the task-irrelevant activities
in the temporal regions while the subjects attend to the visual11

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Revealing Time-Unlocked Brain Activity

Figure 9. Results of additional experiment with visual and auditory Go/NoGo tasks. A: Time courses of powers of components reflecting
brain activities of unlocked waveforms of visual Go condition. Powers are averaged across subjects. Time 0 corresponds to stimulus onset. B:
Amplitude distributions over space of unlocked waveforms estimated from individual conditions. Amplitudes are averaged across subjects.
doi:10.1371/journal.pone.0098014.g009

stimulus. We verified this hypothesis by comparing the powers of
the components corresponding to the unlocked waveforms acrossthe various conditions. Without using CWE, this cannot be found.
The unlocked waveform and their delays estimated by CWE
were verified by comparing the unlocked waveform with theestimated delay-triggered average, which was obtained byaveraging the MEG epochs triggering on the estimated delays.
The similarity between them (Figure 4A) indicates that, at theestimated delays, there is a spatiotemporal pattern that resembles
the unlocked waveform and that the unlocked waveform is not theartifact generated by CWE.
The amplitude distribution over space of the unlocked
waveforms (Figure 5A) is different from that of the alphaoscillations in the MEG epochs during the Go condition(Figure 5B), although both have the same frequency oscillations
and originate in the same data. The time-trial images and the
PLVs show that the difference comes from the phase relation
between channels (Figure 5D and E). Alpha oscillations have beenreported as being generated by several generators, such as thevisual cortex (e.g. [13]). In the MEG epochs they are mixed, while
the unlocked waveforms only include oscillations synchronizedacross channels. Therefore, CWE successfully separated a set ofalpha oscillations synchronized across channels from desynchronized ones.
Based on the previous studies on alpha oscillations [8–13], we
hypothesized that the unlocked waveforms reflect the inhibition of
the task-irrelevant activities in the temporal regions while thesubject attends to the visual stimulus. We then showed four results
consistent with this hypothesis. First, the delays of the unlocked
waveforms are not correlated with the RTs. If the unlocked
waveforms were related to the task execution, such as decision
making, the delays of the unlocked waveforms would be correlatedwith the RTs. Second, the powers of the components corresponding to the unlocked waveforms increase during the task period for
the Go and NoGo conditions but not for the Passive task (Figure 7).
This result suggests that the unlocked waveforms are related toattention. Third, when the powers of the components are large,
the RTs of the Go condition have small variability (Figure 8). This
result suggests that the unlocked waveforms reflect the inhibitionof the task-irrelevant activities. Finally, the powers of thecomponents do not increase during the task period for the‘‘auditory’’ Go and NoGo conditions (Figure 9A). This result
suggests that the unlocked waveforms are specifically related to‘‘visual’’ attention.
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Our results and hypothesis are consistent with previous fMRI
and positron emission tomography (PET) studies showing crossmodal deactivations [37–41]. These studies demonstrated that
attention to a single sensory modality decreases activity in thecortical regions that process information from an unattended
sensory modality. For example, using fMRI data and a cued
detection paradigm, Mozolic et al. [40] showed the deactivation of
the auditory/visual cortex during visual/auditory attention. Basedon these studies, the inhibition of the brain activities in the taskirrelevant regions, such as the auditory cortex, may occur when
the subject attends to the visual stimulus in the visual Go/NoGo
task.
Furthermore, our results and hypothesis are consistent withprevious EEG and MEG studies showing cross-modal modulationof alpha oscillations [13,42–46]. These studies demonstrated that
attention to a single sensory modality increases alpha oscillations in
the cortical regions that process information from an unattended
sensory modality. For example, using EEG data, Pfurtscheller [13]showed that there is an increase in the alpha band over thesensorimotor cortex during reading with a simultaneous decreasein the alpha band over the visual cortex, and vice versa during
movement. The increased alpha oscillations were assumed toreflect the idling state of cortical networks where the transfer of
specific information is reduced. Based on these studies, theincrease in the alpha oscillation may occur in the task-irrelevant
temporal regions when the subject attends to the visual stimulus in
the visual Go/NoGo task, and CWE might extract the increasedalpha oscillations as unlocked waveforms.
To our knowledge, although relations to visual attention werereported for the visual alpha (e.g. [47]) and sensorimotor mu
oscillations [13], this has not been reported for the temporal alphaoscillations or so-called tau rhythms [48,49], except in a monkey
study [50]. In this study, for the first time, CWE could reveal the
relation between the temporal alpha oscillations and visual
attention in human subjects, although we did not expect thisbefore applying CWE. This demonstrates the powerful ability of
CWE to reveal new findings on cognitive brain functions without
making any hypothesis in advance.

Conclusions
By using CWE, we examined the unlocked waveforms in the
MEG epochs during a cognitive stimulus-response task. In
estimating the unlocked waveforms, we did not hypothesize their
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Revealing Time-Unlocked Brain Activity

shapes or roles. After the estimation, based on the properties of theestimated waveforms, we generated a hypothesis on their roles andtested it. Consequently, CWE worked as a hypothesis generator.
Generally speaking, we believe that CWE is a useful tool for datadriven and hypothesis-generating research. By applying it to
MEG/EEG data during various cognitive stimulus-response tasks,
we can obtain new scientific hypotheses and findings on cognitive
brain functions.

Acknowledgments
We thank Dr. Mitsuo Kawato, Dr. Yasushi Naruse, Dr. Masayuki Hirata,
Dr. Masao Matsuhashi, and Dr. Tatsuya Mima for their helpful comments
and suggestions.

Author Contributions
Conceived and designed the experiments: YT KY NY MS. Performed theexperiments: YT KY NY. Analyzed the data: YT. Contributed reagents/
materials/analysis tools: YT KY NY MS. Wrote the paper: YT KY NY
MS.

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May 2014 | Volume 9 | Issue 5 | e98014