Proceedings of International Joint Conference on Neural Networks, Dallas, Texas, USA, August 4-9, 2013
Spatial Alignment of Scalp EEG Activity
during Cognitive Tasks
Mark H. Myers, Charlotte A. Joure, Carley Johnston, Aaron Canales, Akshay Padmanabha, Robert
Kozma
Abstract—Electrocorticogram (ECoG) analysis of human
subjects demonstrated that beta-gamma oscillations carry
perceptual information in spatial patterns across the cortex
when the subjects were engaged in task-oriented activities. A
hypothesis was tested that similar patterns could be found in
the scalp EEG of human subjects during visual stimulation.
Signals were continuously recorded from scalp electrodes and
band-pass filtered. The Fast Fourier transform provides the
phase, which is used to obtain directional phase information
relating to cognitive tasks. Spatial patterns of EEG phase
modulation were identified and classified with respect to
stimulus. The obtained results suggest that the scalp EEG can
yield information about the timing of episodically synchronized
brain activity in higher cognitive function, so as to support
mechanisms of brain–computer interfacing.
Index Terms— EEG, Phase Propagation, Phase Velocity,
Spatial Pattern Classification, Cognitive Activity.
I. INTRODUCTION
Research in neural correlation as it relates to cognition has
been investigated experimentally in animals [10]. Through
this work, it has been found that a neural “code” is used by
sensory cortex to express the content of a percept.
This synchrony is not necessarily zero-lag (i.e., the
oscillations are not necessarily perfectly in-phase) but during
this time period the phase relationships between different
spatial sites remain constant. Spatial activity, captured in the
electrodes across frontal and parietal areas of the cortex,
demonstrate
synchronized
neural
activity.
Electrocorticography (ECoG) records from animals trained
to respond to conditioned stimuli (CS) have found this
“code” to be identified in the olfactory bulb [12][13], and
neocortex of rabbits [2], monkeys [13], and humans [20].
The brain activity patterns have been classified with respect
to conditioned stimuli using animal ECoG signals [10].
________________________________________________
*
Manuscript received February 28, 2013.
M. H. Myers is with University of Memphis, TN 38152 USA (e-mail:
mhmyers@memphis.edu)
R. Kozma is with University of Memphis, TN 38152 USA (e-mail:
rkozma@memphis.edu)
C. A. Joure, C. Johnston, A. Canales, A. Padmanabha are with the Center
for Large Scale Integrated Optimization & Networks, University of
Memphis, Memphis, TN, USA
978-1-4673-6129-3/13/$31.00 ©2013 IEEE
In every species, the classification to conditioned stimuli
was maximal when all ECoG channels were used.
Synchronized neural activity sustaining the AM pattern was
uniformly distributed with respect to information density.
This finding also held for experiments in which the channels
were divided into sub-arrays that were fixed on the auditory,
entorhinal, somatic, and visual cortices and the olfactory
bulb [12] showing that the intermittent synchrony
encompassing all areas of cortex examined. The spatial
phase patterns identified in this work had a sparse spatial
density, determined by the relative spacing of nine recording
electrodes on the surface of the cortex. Nonetheless, the
multicortical distribution seen engendered the hypothesis
that a similar “code” of intermittently synchronized neural
activity may occur at a sufficiently large spatial scale to
make AM patterns accessible from scalp EEG, despite the
distance from each electrode and subsequently from cortex
to scalp. Three existing lines of evidence supported this
hypothesis. (a) Suggestions that multiple modules perform
cognitive functions by coordinating their dynamics
[1][5][6][8][17]. (b) Multichannel recordings showing
widespread intermittent synchrony of oscillations in the beta
and gamma range of the EEG (20 Hz and 80Hz respectively)
[21] and EMG [3]. There have been numerous reports on the
correlation of EEG oscillations, particularly in the gamma
range, with a variety of cognitive functions [4][14].
II. PROCEDURE
A. Data Collection Procedure
This study was approved by the University of Memphis
Institutional Review Board. Data was collected from
BioInfinity’s EEG and bend sensor software. EEG was
recorded using the Flax/Pro-comp InfinityTM amplifiers
with a 10-20 electrode cap using Ag/AgCl electrodes. The
sampling rate was 2048 Hz. Continuous records were taken,
with the times of various sorts of visual stimuli via a
protocol given in Table 1.
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Fig. 1. Evoked-related potentials (ERP) were captured via an EEG cap using nine electrodes. Additional sensors were utilized to capture finger movement
via a bend sensor. Grounding and negative connections were facilitated via ear lobe connections. Positive, negative, and grounding connections were
inserted into an impedance sensor which connected to the Flex/Pro-comp InfinityTM amplifier. EEG data capture was accomplished through BioInfinityTM
where data was exported in order to enable EEG analysis of cognitive states.
TABLE 1
Experimental Protocol
Period
Time (sec)
Activity
1
1-30
Rest period
2
30-35
Eye blinking
3
35-40
Rest period
4
40-45
Eye blinking + finger movement
5
45-65
Rest period
6
65-70
Eyes open + finger movement
7
70-80
Rest period
8
80-85
Eyes closed + finger movement
The experimental design and recording procedure have been
documented [40] as seen in Fig. 1. The visual stimuli
consisted of a set of directions that incorporated the protocol
steps listed above. After data was collected, a notch filter
was applied to remove 60Hz line noise prior to the
extraction of specific data epochs from the continuous
recording. Low-pass filtering was applied to remove higher
frequency ranges using an infinite impulse response (IIR).
Notch filter is applied using the Parks- McClellen
Filter to remove ambient noise.
.
B. Fast Fourier Transformations
Fast Fourier analysis breaks down a signal into constituent
sinusoids of different frequencies and is extremely useful for
data analysis [2]. For sampled vector data, Fast Fourier
analysis was performed using the Discrete Fourier
Transform (DFT). To compute the DFT of a sequence the
Fast Fourier Transform (FFT) was used. The FFT is
practically useful in frequency analysis to power spectrum
estimation and provides an efficient algorithm for converting
data from the time domain into the frequency domain. ECoG
frequency data is typically displayed in one of two ways:
amplitude spectrum or a power spectrum. In this study,
power spectrum analysis was employed.
C. Location of the Phase Patterns
The following steps were required to calculate and display
phase directionality during cognitive tasks:
Step 1: EEG Data was pre-processed using a notch filter to
remove 60 Hz ambient machine noise, and low-pass filtered
to remove unnecessary frequencies above 100Hz since lower
frequencies will be focused on in this study.
Step 2: The Fast Fourier Transform was applied to the
filtered signal in order to obtain the real and imaginary parts
of the signal. Phase angles, in radians, for each element of
complex array Z were calculated using the formulae:
(1)
where z is a complex array. The angles lie between . The
phase between two channels was unwrapped across all
channels to make it a “continuous” function.
Step 3: The mean of the unwrapped phases for each channel
was plotted. The slope of the mean was calculated per
reference channel.
Step 4: The slope values were plotted on a two-dimensional
display of the 10-20 EEG system in order to determine phase
directionality in reference to each channel.
Step 5: Directionality is determined by the slope of the mean
of the unwrapped phases, whereas the sign of the slope
indicates whether the phases are positively or negatively
correlated. The thickness of the arrow is determined by the
magnitude of the slope (i.e. small magnitudes are signified
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by thin arrows, whereas large magnitudes are signified by
thick arrows).
When two non-referential signals have positive
correlation, the correlation and phase-synchrony values of
the two referential signals can monotonically increase to one
(or monotonically decrease to some positive value and then
monotonically increase to one) as the amplitude of the
reference signal varies in [0, +•). Additionally, when two
non-referential signals have negative cross-power, the two
referential signals can monotonically decrease to zero and
then monotonically increase to one as reference signal power
varies in [0, +•) [15]. Given two time series x(t) and y(t),
the correlation of x(t) and y(t) is defined as:
(2)
where x and y are assumed to have zero mean and E[·] is the
expected value of one random variable. Magnitude squared
coherence is calculated between reference and neighboring
electrodes to demonstrate phase alignment during cognitive
tasks. Phase propagation velocity is calculated through the
phase component of the cross power spectral density
(CPSD) [18]:
(3)
The phase component is:
(4)
where τ corresponds to the time delay of the signal, and f as
the frequency component of the signal. The time delay can
also be defined as:
2
(5)
Where α can also be derived from the slope of the
unwrapped phase over the frequency range. Equation 5 can
be rewritten as:
(6)
Phase propagation velocity is calculated is follows:
∆
∆
2
(7)
In the actual measurements, there are large uncertainties in
the measured time delay.
Additionally, the linear
approximation of the slope seems to have only limited use.
Therefore on the present work, we provide the evaluated
phase slope values. The evaluation of actual propagation
velocities remain the objective of future studies.
III. RESULTS
Synchronous neural activity as it pertains to cognitive task
activity is found in the unwrapped phases in Fig 2. As the
individual moves the bend sensor, their EEG signal
transitions from nonlinear neural activity to synchronous
activity. The unwrapped phases per channel in respect to a
reference channel (C3) move in different directions above
and below the y-axis. Reference electrode C3 was selected
as the most dominate electrode pair between electrodes to
demonstrate phase directionality as compared to other
reference electrodes. As an individual engages in cognitive
activities, the phase of the channels broadly aligns in a
specific direction. Fig. 2 (a-e) represents the five states of (a)
rest, (b) eye blink, (c) eye blink and bend sensor movement,
(d) eyes open and bend sensor movement, (e) eyes closed
and bend sensor movement for all pairs of electrodes. The
unwrapped phases per channel align across the x-axis during
rest and eye blinking states Fig. 2 (a, b, c). The unwrapped
phases per channel appear to move above the zero x-axis
during bend sensor movement in Fig. 2 (d, e), which
coincides with positive slope values.
Table 2 illustrates the calculated slopes per unwrapped
phase per channel within each state of the protocol. The
phase values can relate to signal propagation and some
effective propagation velocity between different channels;
detailed evaluation of the underlying effect is in progress.
Fig. 3(a-e) displays mean squared coherence values per
each channel as well as the C3 reference channel. We can
see observe high coherence values over the frequency region
except for occasional drops. Overall such high coherence
values indicate that the phase between these channels is
meaningful.
Fig. 4(a-e) demonstrates phase magnitude and
directionality per each reference electrode. Phase
directionality in Fig. 3 (a-c) appears to move in varying
directions in reference to electrode C3. During the states
where the patient is either just blinking or the patient is
blinking and moving their finger at the same time (b, c),
there appears to be a shift from one common direction – a
positive/negative phase correlation, to various phase
directionality.
This activity may be due to high
electromyography artifacts due to the rate of eye blinking
and subsequent movement of the patient’s forehead, thereby
overcoming the readings from the EEG. Phase magnitude
and directionality become more pronounced during eyes
open and eyes closed with finger movement (d, e), whereas
phases between electrode C3 and its neighboring channels
move from one area of the cortex to propagate to other areas
in the cortex. We see phases align in one general direction
during cognitive activities. Phase alignment can occur as
positive phase (dark grey arrows) or negative phase
correlation (light grey arrows).
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(a)
(b)
(c)
(d)
(e)
Fig. 2. Figures (a-e) display the unwrapped phase per channel. Each figure represents the five states of (a) rest, (b) eye blink, (c) eye blink and bend sensor
movement, (d) eyes open and bend sensor movement, (e) eyes closed and bend sensor movement.
1200
(a)
(b)
(c)
(d)
(e)
Fig. 3. Figures (a-e) display the magnitude squared coherence. Each figure represents the five states of (a) rest, (b) eye blink, (c) eye blink and bend sensor
movement, (d) eyes open and bend sensor movement, (e) eyes closed and bend sensor movement.
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TABLE 2
PHASE SLOPE VALUES (RADIANS) DETERMINED FROM THE UNWRAPPED PHASE
Electrodes
Rest
Eye Blink
Eye Blink +
Finger
Movement
Eyes Open +
Finger
Movement
Eyes Closed + Finger
Movement
C3-F3
-0.344
-0.3245
-0.257
0.3916
0.1208
C3-Fz
-0.072
-0.2386
-0.0746
-0.1057
0.2694
C3-F4
-0.199
0.1105
-0.1861
0.3302
0.307
C3-Cz
-0.167
0.2754
-0.2074
0.294
0.0532
C3-C4
-0.182
-0.062
0.0288
-0.0578
0.3192
C3-P3
0.0591
0.0673
0.1334
0.0876
0.1245
C3-Pz
-0.108
-0.0483
0.0158
0.4948
0.2414
C3-P4
-0.145
-0.0315
-0.0781
Participant 2
0.2049
0.2051
C3-F3
0.019
-0.038
0.185
-0.277
-0.23
C3-Fz
-0.02
-0.136
0.243
-0.358
-0.214
C3-F4
-0.195
-0.19
-0.17
-0.168
-0.149
C3-Cz
0.067
-0.039
0.077
0.027
-0.003
C3-C4
-0.229
-0.202
-3149
-0.176
-0.166
C3-P3
-0.151
-0.173
-0.172
-0.201
-0.208
C3-Pz
-0.207
-0.107
-0.174
-0.168
-0.184
C3-P4
-0.233
-0.132
-0.1
Participant 3
-0.197
-0.178
C3-F3
0.184
0.216
0.215
-0.107
-0.215
C3-Fz
0.169
0.21
-0.017
-0.038
-0.017
C3-F4
-0.389
-0.162
-0.392
-0.216
-0.392
C3-Cz
-0.136
-0.194
-0.257
-0.199
-0.257
C3-C4
-0.17
0.026
-0.291
-0.063
-0.291
C3-P3
-0.249
-0.196
-0.293
-0.183
-0.293
C3-Pz
-0.164
-0.169
-0.285
-0.19
-0.285
C3-P4
-0.247
-0.185
-0.312
-0.087
-0.312
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(a)
(b)
(c)
(d)
(e)
Fig. 4. Figures (a-e) demonstrate phase magnitude and directionality from reference electrode C3. Dark grey lines represent negative slope values and light
grey lines represent positive slope values of the mean of the unwrapped phases between channels. The magnitude of the vector is illustrated by the thickness
of the vector.
IV. DISCUSSION AND CONCLUSIONS
The calculated phase of the EEG signal provides a mean
to determine the dominant direction of electrical activity in
the brain. This direction moves in varying orientations
during the resting state of the cortex. Phase directionality
and magnitude of the electrical activity of the cortex appear
to align in a dominant direction during cognitive activities.
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Fig. 5 displays cognitive states ranging from negative
correlation (a), mixed correlation (b and c), and the
emergence of positive correlation (d) due to Event- Related
Potentials (ERP) and finger movement.
Fig. 5e
demonstrates that the phases across all channels are
positively correlated. Both theory and experimental
findings have led to the concept that the brain is a selforganized system, which continuously reorganizes its
activity due to the influence of internal and external stimuli
[5][9][17][19]. Previous work on EEG analysis has modeled
the stable states or frames, which carry the AM patterns
related to cognition [2][13]. Each state transition begins with
an abrupt change in phase, followed by synchronization at a
new frequency and the stabilization of a new AM pattern.
Abrupt phase resetting has also been observed in the scalp
EEG analysis [11]. Although these phase re-settings were
not simultaneous over a large numbers of channels, they
were clustered in time, suggesting that the phase
discontinuities necessary for the emergence of brain activity
patterns related to cognition can be studied using EEG
signals, despite the corruption by electromyographic noise
(i.e. eye blinking) caused by scalp interference.
In previous work, all subjects were exposed to either a
single stimulus in order to familiarize them to that stimulus
or to two stimuli in order to perform a discrimination task
[16]. In the present study, visual stimuli is being used to
engage areas of the brain in order to measure cortical
alignment through task engagement, as revealed by the work
of Dumenko [7]. The original experiment [21] was designed
to investigate perceptual or cognitive differences that might
emerge when subjects experienced task engagement. The
remarkable finding was that scalp EEG data could be
classified with respect to a stimulus type.
The second remarkable result was that all electrodes
contributed information to the spatial organization, which
served to classify the EEG epochs in time with respect to the
stimuli type, regardless of amplitude or variance. Electrical
neural activity displayed during engaged activity
demonstrated that cortical information density was uniform
over the entire electrode array. Our finding is consistent with
the evidence from widespread intermittent synchronization
of ECoG patterns in rabbits and cats, which involved
intermittent synchronization of EEG patterns from a 1D
array extending over 189mm of the scalp [11]. The primary
method used is the Fast Fourier Transform to calculate the
phase of spatial alignment found during cognitive tasking.
Our algorithm could provide new data inexpensively and
non-invasively for modeling the global cerebral dynamics of
learning, and it may enable new advances in brain-computer
interfaces. Our results also highlight the major impediment
to further advances, which is scalp EMG. Some preliminary
results suggest that scalp EMG can be attenuated by lowpass spatial filtering, using presently available arrays of 256
electrodes [21] and foreseeable arrays with an exceptionally
high density of recording with sampling rates of 5000/s or
more.
Acknowledgements: Authors (RK and MM) appreciate
useful advice by Dr. E. Tognoli (Florida Atlantic University)
at the initiation of the EEG experimental setup.
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