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SRI International
Final Report (Rev.)-Task 6.0.2 . January 1990
OBSERVATION OF NEUROMAGNETIC FIELDS IN RESPONSE TO
REMOTE STIMULI
Prepared by:
Edwin C. May
Wanda W. Luke
Virginia V. Trask
Thane J. Frivold
SG1J
Prepared for:
Contracting Officer's Technical Representative
SRI Project
1291
...
Approved by:
Murray J.Baron, Director
Geoscience and Engineering Center
.....
333 Ravenswood Avenue . Menlo Park, CA 94025-3493 . (415) 326-6200 · FAX: (415) 326-5512 · Telex: 334486
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ABSTRACT
We have conducted a conceptual replication of an SRI/Langley Porter study in which a single subject's
central nervous system (CNS) responded to a remote, and isolated flashing light. The CNS activity of
eight remote viewers was monitored by a seven-channel magnetoencephalograph (MEG). Visual stimu-
li were randomly presented to an isolated individual who acted as a "sender" while MEG data were col-
lected from a viewer (receiver). The stimuli were 5-cm square, linear, vertical, sinusoidal gratings lasting
100 ms (remote stimuli). Time markers were randomly inserted into the data stream as control points
(pseudo stimuli). The dependent variable was the root-mean-square (RMS) average phase shift of the
dominant alpha frequency. Using a Monte Carlo technique to estimate p-values, we observed signifi-
cant (combined across all viewers) RMS phase shifts resulting from the remote stimuli (2; = 1.99, p ≤
0.024, effect size = 0.599). Similarly, the combined statistic for the pseudo stimuli was also significant (Z.
= 2.92, p < 0.002, effect size = 0.924). The phase shifts from the remote and the pseudo stimuli are
independently not characteristic of the data at large. This result was unexpected, and suggests that we
may have observed a CNS response to an unintended stimulus (i.e., electromagnetic interference, EMI,
from the computing hardware). However, in the SRI/Langley Porter study, EMI had been eliminated,
thus, it remains possible that the CNS changes resulted from an anomalous form of information transfer.
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TABLE OF CONTENTS
ABSTRACT
ii
LIST OF TABLES
iv
LIST OF FIGURES
V
I
INTRODUCTION
1
1.
Physiological Correlates to Psychoenergetic Functioning: A Brief History
1
2.
Technological Background
1
II
METHOD OF APPROACH
4
1.
General Description
4
2.
Protocol
4
3.
Data Analyses
6
4.
Monte Carlo Calculations
6
III
RESULTS
8
1.
Calculations
8
2.
Monte Carlo Estimates of Significance
14
3.
Results: Button Presses
15
IV
DISCUSSION AND CONCLUSIONS
16
1.
Root-Mean-Square Phase
16
2.
Viewer Dependencies
18
3.
Pseudo Stimuli
19
REFERENCE
21
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LIST OF TABLES
1.
Results of Monte Carlo Calculation for RMS Phase
15
2.
Data Schema for Interval Conditions
15
3.
Button Pressing Results
15
4.
Comparison Between Monte Carlo Phases and Theory
17
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LIST OF FIGURES
1. Schematic Timing Protocol-Single Run
5
2.
Sensor Position Relative to the Inion (0,0) for Viewwer 002
5
3.
Idealized Results for a Single Stimulus
6
4.
Viewer 2: Date 8/25/88: Session 1: Time Average
8
5.
Viewer 2: Date 8/25/88: Session 1: Power Spectra of Time Average (RS)
9
6.
Viewer 2: Date 8/25/88: Session 1: Average Power Spectra (RS)
10
7.
Viewer 2: Date 8/25/88: Session 1: Average Power Gain (RS)
10
8.
Viewer 2: Date 8/25/88: Session 1: RMS Phase (RS)
11
9. Viewer 2: Date 8/25/88: Session 1: Time Average (PS)
11
10. Viewer 2: Date 8/25/88: Session 1: Power Spectra of Time Average (PS)
12
11.
Viewer 2: Date 8/25/88: Session 1: Average Power Spectra (PS)
12
12.
Viewer 2: Date 8/25/88: Session 1: Average Power Gain (PS)
13
13.
Viewer 2: Date 8/25/88: Session 1: RMS Phase (PS)
13
14.
Viewer 2: Date 8/25/88: Session 1: RMS Phase: Sensor: 2: RS = 118
14
15.
Idealize Distributions for Relative Phase Shifts
17
16. Phase Distributions for Viewer 002: 8/25/88
18
17.
Phase Distributions for Viewer 007: 3/29/89
18
18. Phase p-values for Viewer 002: 8/25/88
18
19. Sequence of Events for Stimuli Generation
19
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combination of electrical signals and chemical in-
teractions. It is beyond the scope of this report to
describe the cellular physiology involved, but is
sufficient to say that this activity produces mag-
netic fields (predominantly dipole) that can be
sensed externally.
The sensing device of a MEG is a cryogenic super-
conducting quantum interference device
(SQUID) coupled with a gradiometer. SQUIDs
currently being used are cooled by liquid helium.
At a few degrees above absolute zero, an electri-
cal current can flow through a superconductor
with no applied voltage. The material of the
SQUID consists of superconducting loops with
two sections of thin insulating material connect-
ing them (Josephson Junctions). This configura-
tion is referred to as a DC SQUID. Some
electrons can tunnel through this insulation. The
presence of a weak magnetic field produces a
phase difference for the wave function of the
magnetic field [and] produces a phase difference
for the wave function of the electrons across this
barrier. The resulting interference pattern pro-
duced by the two different wave functions on each
side of the barrier can be used to indicate the
strength of these extremely weak magnetic fields.
The neuronal magnetic fields from the human
brain are only about 10-13 tesla, while the earth's
magnetic field is 10-4 tesla and normal urban
noise is about 10-7 tesla. Care must be taken,
therefore, to assure that the signal-to-noise ratio
is favorable. This has been taken into considera-
tion by the manufacturer of MEG equipment
(BTi of San Diego, California), who has designed
highly shielded sensors that use a second-order
coupled gradiometer to reduce the environ-
mental noise by about 106. The use of an alumi-
num and u-metal magnetically shielded room can
further reduce the noise by a factor of 103. If used
together, these two precautionary measures can
reduce the ambient noise by a factor of about
109-equivalent to the internal SQUID noise.
Because a MEG responds best to neuronal cur-
rents that are parallel to the skull (i.e., currents
producing magnetic fields oriented tangentially to
the skull), neuronal currents perpendicular to the
skull may be missed. In reality, however, few
neuronal electrical currents are exactly perpen-
dicular to the skull, so some tangential compo-
nent is almost always available to the SQUID.
Searching for a closely packed group of neurons
can be a slow and tedious process. Due to techno-
logical restraints, a maximum of seven sensors can
be used simultaneously to gather MEG measure-
ments. Sensors on a seven-channel MEG are lo-
cated on a 2-cm equilateral triangular grid
forming the center and vertices of a regular hexa-
gon. A subject wears a spandex cap with grid
marks lined up with his nasion, inion, and earlobes
to serve as a head-centered coordinate system. To
identify the location of a neuronal-equivalent
current dipole, many measurements have to be
taken. Isocontour maps of field strength are used
to represent the amplitude and polarity distribu-
tion of the magnetic fields. A least-squares proce-
dure is applied to the observed fields to estimate
the location of neuronal sources and orientation
of the equivalent current dipole.8 The estimated
location of the neuronal source can then be iden-
tified anatomically with a magnetic resonance im-
age scan of the head. Developments in technology
may soon allow for enough channels to cover the
whole head at once, thereby reducing data collec-
tion time and increasing precision.
MEG technology is based on a cryogenic SQUID
operating in liquid helium. Because the Dewar
flask cannot exceed a 45-degree angle, subjects
must lie prone beneath the apparatus. MEG sen-
sors are not attached to the head, but are lowered
into position over the skull; the subject cannot
move his head during monitoring without disturb-
ing the measurement. For these two reasons,
MEG equipment is not suited for long-term
monitoring of a subject. These problems may be
solved in the near future as new technology, such
as high-temperature SQUIDs, develops.
A response from the MEG is a complex waveform
consisting of a series of negative and positive
peaks or components. Specific components of this
waveform can be correlated with perceptual and
cognitive processes. The most commonly ob-
served response to a visual or auditory stimulus,
for example, is a large component occurring ap-
proximately 100 ms after the onset of the stimu-
lus. One hundred milliseconds appears to be the
average latency period between stimulus and the
first correlated neuronal activation in the brain.8
The earlier EEG technology measures electric
potential, or event-related potentials (ERPs) pro-
duced by the electrical activity of the brain. A
MEG measures the magnetic fields, or event-re-
lated fields (ERFs) produced by the electrical ac-
tivity of specific groups of active neurons in the
cortex. An EEG and a MEG, therefore, reveal
different aspects of the electrical activity of the
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brain and are often used as complementary tech-
nologies. In some areas, however, the MEG tech-
nique has definite advantages over the EEG:
(1) ERPs taken from the scalp provide little in-
formation regarding the precise three-
dimensional distribution of the neuronal sites
producing the electrical activity. Brain tissues
of unknown electrical conductivity and thick-
ness, individual variations in skull thickness
and geometry, and proximity to openings in
the skull all make obtaining such detailed in-
formation difficult. The same is not true
when using a MEG. Neuronal magnetic fields
can travel through brain tissues without being
significantly altered; this property, coupled
with the dipole model, results in high spatial
resolution of the neuronal activity.
(2) EEG procedures are occasionally costly and
can be invasive: EEG electrodes must be at-
tached directly to the skull or to the brain of
the subject, whereas MEG sensors are ex-
tracranial and are simply lowered into posi-
tion against the skull.
(3) There is much controversy over the appropri-
ate reference electrode in EEG work (a ref-
erence electrode is required with electric
potential measurements, because only differ-
ences in electric potential are measured).
There is no such problem with a MEG, be-
cause the measurement of magnetic fields is
absolute.
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II METHODS OF APPROACH
Our goal was to conduct a conceptual replication
of the earlier SRI/Langley Porter experiments.
Our basic hypothesis is that a viewer's CNS would
respond to a remote light stimulus.
1. General Description
Using a seven-sensor MEG in a shielded room,
we investigated the occipital-cortex neuronal
magnetic activity that might occur in response to a
remote "visual" stimulus.
The following definitions may be helpful:
· Viewer-An individual who attempts extrasen-
sorimotor communication with the environ-
ment (e.g., the perception of remote stimuli).
· Direct Stimuli (DS)-Visual stimuli occurring
within the normal visual sensory channels.
· Sender-An individual who, while receiving di-
rect stimuli, acts as a putative transmitter to a
remote individual (i.e., viewer) who is attempt-
ing to receive the same information via ex-
trasensorimotor communication.
· Remote Stimuli (RS)- Visual stimuli occurring
outside the normal range of known sensory
channels.
· Pseudo Stimuli (PS)-A time marker in the
data stream with no associated stimuli.
In this report, a direct stimulus to the sender is
also considered as a remote stimulus to the view-
er.
2. Protocol
2.1 General Considerations
To begin a session, a sender is isolated in a room
while a viewer is monitored by a MEG in a
shielded room about 40 m away. Only the sender
is presented with a number of direct visual stimuli
at random intervals within a 120-second period,
the length of one run. One session usually consists
of 10 runs.
2.1.1 Viewers
Eight viewers were selected for this experiment.
Four were known to be good remote viewers, and
four were staff members with unknown viewing
ability. Each viewer contributed a minimum of
one and a maximum of three independent ses-
sions.
2.1.2 Senders
The senders in all sessions were either various
staff members who were well known to the view-
ers or they were spouses.
2.1.3 Dependent Variable
The dependent variable is the root-mean-square
(RMS) phase shift of the primary alpha activity as
a result averaged over all RS.
2.2 Specific Protocol Details
2.2.1 Stimuli
Remote stimuli consisted of a standard video en-
coded blank screen with a 5-cm square, linear,
vertical, sinusoidal grating lasting about 100 ms.
These stimuli (DS to the sender) subtended 2 de-
grees in the lower left visual field of the sender.
This was maintained by asking the sender to focus
his visual attention on a permanent mark on the
monitor. During the experiments described in
this report, no attempt was made to monitor the
sender in any way. Pseudo stimuli consisted of the
blank screen without the superimposed grating,
and were included as a putative within-run con-
trol.
2.2.2 Run Timing
Figure 1 shows a schematic timing diagram for
one run. No two stimuli of any type were allowed
to occur within a 3-second period of each other.
A stimulus may occur, however, any time within a
4.5-second window thereafter. The sender was
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presented with a minimum of 9 and a maximum of
15 DS occurring at random intervals within a
120-second period. In all but the first session, a
random number of pseudo stimuli (i.e., random
time markers with no concomitant stimuli-PS)
were added as a within-run control. A viewer was
never presented with direct stimuli except in lo-
cating the maximal response to the visual areas
(see Section II.2.2.4).
Pseudo Stimuli
0
120 s
Remote Stimuli
Figure 1
Schematic Timing Protocol-Single
Run
2.2.3 Instructions to Viewers
In all sessions, the viewers were completely in-
formed about the details of the experiments.
Prior to their placement on the MEG table, they
were shown the location of the RS display moni-
tor, and were instructed to place their attention
upon it or the sender during the session.
For some sessions, the viewer was instructed to
press a fiber-optic-coupled button when he felt
that he perceived stimuli. Each button press was
marked in the data record. Button pressing was
retained in this protocol as part of the conceptual
replication.
2.2.4 Sensor-array Placement and Callbra-
tion
We selected the location for the sensor array by
optimizing the viewer's response to direct visual
stimuli. Inherent in this choice is an assumption
that may not be valid: namely, that neurons par-
ticipating in a reaction to RS are the same as those
that respond to DS. The sensor locations were
then marked on an acetate transparency to allow
for accurate repositioning of the sensors in later
sessions. One such placement (right occipital-
minus centimeters from the inion indicate the
right hemisphere) is shown for viewer 002 in Fig-
ure 2. It should be noted that MEG sensor place-
ments do not necessarily correspond to
conventional EEG electrode placement.
For a calibration, the viewer was fitted with a
spandex cap with grid marks aligned with his in-
ion, nasion, and earlobes (i.e, head-centered co-
ordinate system). The viewer was then placed as
comfortably as possible on an observation table
beneath the MEG. He must lie face down and
look though a hole in the table to view the DS via a
system of mirrors. These stimuli were displayed by
a projector located outside the entrance to the
shielded room. The sensors of the MEG were
lowered from above to touch his head over the
right occipital lobe. In this configuration, the sen-
sor array was moved at the end of 30 DS to a posi-
tion that optimized his response to the DS. Once
found, the array position was marked on the cap
for subsequent repositioning.
41
6
31
2
i
4
1
ż
Distance
(cm)
01
3
-1
-2.
-1-2-3-4-5-6-7-8-9-10
Distance (cm)
Figure 2 Sensor Position Relative to the Inion
(0,0) for Viewer 002
2.2.5 Sequence of Events for a Session
The following is the schedule of events for a ses-
sion:
. Collect approximately 10 minutes of back-
ground data with no viewer or sender present
and the MEG in full operation.
. Isolate the sender with the stimulus display de-
vice.
· With the viewer on the table, position the sen-
sor array at the calibration point.
· At time = 0, start the monitoring of data with
computer-generated trigger. Data are col-
lected the entire 120 seconds at a rate of 200
samples per second.
· At time < 120 seconds, present 9 to 15 remote
and 9 to 15 PS to the sender.
· At time > 120 seconds, allow the viewer to re-
lax for about 2 to 5 minutes without leaving the
table. This break generally consists of the send-
er entering the shielded room to engage the
viewer in conversation.
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· Collect nine additional runs with the same pro-
cedure while the viewer remains positioned on
the table under the MEG.
3. Data Analyses
If our initial assumption about sensor positioning
is true, and if the earlier results are replicated, we
expect to see a change in alpha production as a re-
sult of the RS. We might also expect an evoked re-
sponse similar to visual ERFs. Figure 3 is an
idealized illustration of these expected results in
the time-series data. Times less than zero are
prestimulus; times greater than zero are poststi-
mulus. The stimulus lasts 100 ms.
Evoked Response
Decreased
Alpha
Amplitude
1000
-1000
-500
0
500
Time (ms)
Figure 3 Idealized Results for a Single
Stimulus
For each session, the following was computed for
each RS and PS, respectively:
(1) Five hundred ms of pre- and post-stimulus
time-series data were separately detrended
and filtered (40 Hz lowpass).
(2) The power spectrum was computed for each
500-ms pre- and post-stimulus period.
(3) The relative phase change of the dominant
alpha frequency from pre- to post-stimulus
period was computed as the arctangent of the
ratio of the imaginary and real component of
the transfer function. The transfer function
is defined as the ratio of the FFT of the post-
stimulus period divided by the FFT of the
pre-stimulus period.
(4) One thousand ms of time-series data (i.e.,
500 ms pre- and post-stimulus) was sepa-
rately detrended and filtered (40 Hz lowpass).
In addition, the following averages were com-
puted across all RS and PS, respectively:
(5) The average power pre- and post-stimulus.
(6) The root-mean-square (RMS) average
phase shift.
(7) The 1000-ms time average of the pre- and
post-stimulus periods taken as a single re-
cord.
(8) The "power spectra" of the pre- and post-
stimulus time averages were computed. (We
recognize that a power spectrum of a time av-
erage is not an accurate representation of the
average power spectrum, however it is an in-
dicator of phase shift.)
4. Monte Carlo Calculations
The analysis of CNS activity has always been prob-
lematic, because alpha bursts lasting from 0.1 to a
few seconds occur at random intervals. From a
statistical point of view, the data fail to satisfy at
least two underlying assumptions of the usual sta-
tistical methods (e.g., ANOVA and MANOVA).
Most standard statistical tests assume that all
samples of the data are independent. MANOVA
can be configured to remove this particular as-
sumption, nonetheless, it and the other tests as-
sume that the process under study is stationary;
that is, whatever the statistical properties are,
they remain constant over time. In other words,
the measured properties should not depend upon
when the activity is sampled. CNS time series data
do not satisfy either of these assumptions.
To avoid these difficulties, and to obtain probabil-
ity estimates of the observed RMS phase shifts,
we adopted a simple Monte Carlo approach. In
the usual statistical analysis, the phase shift is
compared to an ideal distribution, or its likelihood
of occurrence is computed using some nonpara-
metric technique. Both techniques attempt to de-
termine the degree to which the observed phase
shift is exceptional, given the universal set of all
possible data. The Monte Carlo method that we
used, however, can only determine the degree to
which the observed phase shift is exceptional,
given the available data sample. Thus, a new
Monte Carlo estimate must be computed for each
individual data set.
The general Monte Carlo procedure is as follows:
(1) Using the same timing algorithm to create
the original RS, generate N sets of M stimuli,
where M is the number of original RS.
(2) For each pass (1 ... N), compute the RMS
phase shift averaged over M remote stimuli.
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(3) Sort the resulting N values to form the RMS
phase shift distribution in the given data sam-
ple.
this p-value is not the probability that the
measure is as large, given a different data
sample.
(4) Compute the probability that the observed
value would be as large (or larger), given a re-
peated random sample of the data. Note that
We have used this technique to compute p-values
for the RMS phase shifts throughout this report.
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III
RESULTS
Eight viewers (002, 007, 009, 372, 374, 389, 454,
and 531) from SRI International participated in
the effort. Viewers 002, 009, 372, and 389 were ex-
perienced, with strong track records. Viewers
007, 374, and 531, had not previously participated
in remote viewing experiments. Viewer 454 had
participated in novice remote viewing training
and has produced significant evidence of remote
viewing ability.
1. Calculations
To illustrate the reduction of the raw data, we use
the 25 September 1988 session from viewer 002.
Figure 4 shows the time average over all RS of the
amplitude (femto Tesla) of the magnetic CNS ac-
tivity of viewer 002's response to RS. The data
from all seven sensors are displayed in a pattern
that is similar to the physical sensor array. Each
sensor is labeled in a highlighted box. The number
of stimuli comprising the average (118) is shown in
the key. The onset of the 100-ms stimulus is rep-
resented at time = 0, so negative time represents
the pre-stimulus period and positive time repre-
sents the post-stimulus period. The total time pe-
riod shown is 1 second. Because the stimuli are at
random times relative to any uncorrelated CNS
activity, averaging has reduced random single-sti-
mulus amplitudes by Vn where n is the number of
stimuli. Sensor 7 shows a clear change from a
slow, regular alpha rhythm during the pre-stimu-
lus period, to one of higher frequency, post-
stimulus.
2
3
Remote Stimuli
118
150
7
4
Apllitude (fT)
75 -
0 -
-75
1
-150 :
6
-600
-300
0
300
600
Time (ms)
Figure 4 Viewer 2: Date 8/25/88: Session 1: Time Average
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Figure 5 shows this change of alpha in the fre-
quency domain. For each sensor, the power spec-
trum of its corresponding time series is displayed
from 0 to 40 Hz. The power spectra are shown in-
dependently for the pre- and post-stimulus peri-
ods (separated by a dashed vertical line). Sensor 7
shows a strong 10-Hz peak pre-stimulus that van-
ishes post-stimulus Similar alpha reductions can
be seen in all of the other six sensors.
The power spectrum of a time series average is not
an indicator of the average power spectrum of the
CNS activity, because time averages are phase
sensitive and power spectra are not. Figure 6 illus-
trates this by showing the average power spectra
(i.e., calculated on a stimulus-by-stimulus basis
and then averaged) for the pre- and post-stimu-
lus periods.
2
13
Remote Stimuli
118
9.07
7
4
Power (IT2) x 102
4.5 -
0.0 I
A
6
0 10 20 30 40
Frequency (Hz)
Figure 5 Viewer 2: Date 8/25/88: Session 1: Power of Time Average
Figure 7 shows the ratio of the post- to pre-stimu-
lus power. A dashed horizontal line is shown to in-
dicate a gain of 1 (i.e., no change across the
stimulus boundary). In this example, there is little
change of CNS power across the stimulus bound-
ary throughout the frequency range.
Because a time average is sensitive to relative
phase and a power spectrum is not, these data sug-
gest that a relative phase shift occurs between
pre- and post-stimulus periods. Figure 8 shows
this relative RMS phase shift computed from 0 to
40 Hz for all sensors. As was the case for the time-
series data, the RMS average was computed over
n=118 RS. In accordance with the protocol (Sec-
tion II.3), the dependent variable was the RMS
phase only at the dominant @-frequency.
At this point we are unable to determine if the
variations seen in Figures 4 through 8 are mean-
ingful. Toward that end, the identical quantities
for the PS are shown in Figures 9 through 13. The
"power" of the time averages for the remote stim-
uli differ markedly from those of the PS spectra
(Figures 5 and 10).
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Remote Stimuli
118
6
4
Power (fT2) x 104
3
0 10 20 30 40
Frequency (Hz)
Figure 6 Viewer 2: Date 8/25/88: Session 1: Average Power
2
3
Remote Stimuli
118
2
Gain
1
+
0
16.
5
An
0
10
20
30
40
Frequency (Hz)
Figure 7 Viewer 2: Date 8/25/88: Session 1: Average Power Gain
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2
3
Remote Stimuli
118
200
4
RMS Phase (deg)
150
100 +
50
6
S
0
10
20
30
40
Frequency (Hz)
Figure 8 Viewer 2: Date 8/25/88: Session 1; RMS Phase
2
3
Pseudo Stimuli
74
150
7
1
Amplitude (fT)
75F
of
-75
-1501
6
-600
-300
0
300
600
Time (ms)
Figure 9 Viewer 2: Date 8/25/88: Session 1: Time Average
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3
Pseudo Stimuli
74
M
1.0 T
7
4
Power (fT2) x 102
0.5
0.0 V
6
0 10 20 30 40
Frequency (Hz)
Figure 10 Viewer 2: Date 8/25/88: Session 1: Power of Time Average
Pseudo Stimuli
74
6
7
4
Power (IT2) x 104
3
1
-
0.
6
0 10 20 30 40
Frequency (Hz)
Figure 11 Viewer 2: Date 8/25/88: Session 1: Average Power
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3
Pseudo Stimuli
74
2
4
Gain
1
0
5
0
10
20
30
40
Frequency (Hz)
Figure 12 Viewer 2: Date 8/25/88: Session 1: Average Power Gain
2
3
Pseudo Stimuli
74
200
1
+
RMS Phase (deg)
150
100
50
6
0
10
20
30
40
Frequency (Hz)
Figure 13 Viewer 2: Date 8/25/88: Session 1: RMS Phase
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2. Monte Carlo Estimates of
Significance
To determine if the changes that are seen qualita-
tively are exceptional, we analyzed the data by the
Monte Carlo procedure outlined in Section II.4.
We simulated the RS by generating 500 sets of
Monte Carlo stimuli using the same random tim-
ing algorithm and number as in the original data.
For each set, the RMS phase was calculated as de-
scribed in Section II.3. The resulting 500 Monte
Carlo RMS phases were sorted as a descending
array, and the fraction of phases equal to or larger
than the observed RS value was represented as a
p-value. (The p-value is bounded on the low end
by 1/500.) Figure 14 shows a histogram of one such
Monte Carlo run, again using the data from
viewer 002 as an example. The values of the RMS
phase for the remote and pseudo stimuli are
marked by vertical lines (see the key in Figure 14).
In accordance with the earlier study6 in which we
observed changes in alpha power, we established
a single criterion for the selection of a sensor for
analysis: the pre-stimulus average alpha power
above background is larger than it is in any other
sensor. Table 1 shows the viewer identification,
date, sensor chosen for analysis, and the p-value
(as defined above) for the RMS phase shift for the
remote and pseudo stimuli, respectively.
The p-values shown in Table 1 are all single tailed
(i.e., the area in the upper tail). Because the distri-
bution of means is approximately normal, we have
converted the empirical p-values to their respec-
tive two-tailed z-scores. If the p-value was less
than 0.5, the z-score shown in Table 1 was com-
puted from the inverse normal distribution as-
suming a p-value twice the one shown. If the
p-value was more than 0.5, we subtracted it from
1.0, doubled the result, and computed the z-score
as above. To test the null hypothesis that the com-
bined RS phase shifts are characteristic of the
data, we computed a standard Stouffer's Z (Z;)for
the 11 sessions shown in Table 1. There is statisti-
cal evidence that the data within ± 0.5 seconds of
the RS are not characteristic of the data at large
(Zs = 1.99, p < 0.024, effect size = 0.599). Simi-
larly, the combined statistic for the PS indicates
that these data are also not characteristic (Z, =
2.92, p ≤ 0.002, effect size = 0.924). Therefore,
there appears to be some statistical anomaly asso-
ciated with the RMS phase shifts for both stimuli
types.
Key
0.50
Passes:
P-Values
500
Real:
0.002
-
Pseudo: 0.846
0.40
0.30
Density
T
0.20
0.10
TTTT
0.00
40
64
88
112
136
160
RMS Phase (deg)
Figure 14 Viewer 2: Date 8/25/88: Session 1: RMS Phase: Sensor: 2: RS = 118
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Table 1
Results of Monte Carlo Calculation for RMS Phase
I.D.
Date
Sensor
Remote
Pseudo
Remote
Pseudo
009
06/24/88
6
0.650
-
-0.524
002
08/25/88
2
0.002
0.848
2.653
0.513
08/26/88
6
0.904
0.966
0.871
1.491
372
10/19/88
7
0.094
0.168
0.885
0.423
374
03/29/89
6
0.154
0.810
0.501
0.305
007
03/29/89
7
0.970
0.180
1.555
0.358
389
05/23/89
4
0.288
0.040
-0.191
1.405
05/24/89
5
0.260
0.016
-0.050
1.852
05/25/89
4
0.120
0.922
0.706
1.011
531
05/24/89
4
0.814
0.134
0.274
0.619
454
05/25/89
4
0.732
0.052
-0.090
1.259
3. Results: Button Presses
In the early SRI study6, significant changes in al-
pha production were observed in response to an
RS. The statistical evidence, however, did not in-
dicate that the viewer was able to recognize an RS
cognitively (i.e., the viewer's button presses rela-
tive to the RS did not exceed mean chance expec-
tation).
In the current experiment, viewers 002, 009, and
372 were asked to press a button whenever they
"perceived" an RS. The total number of stimuli
during a session of 10 runs was not known in ad-
vance because of the randomization procedure.
The null hypothesis is that the probability of a
time interval having a stimulus is the same for
those intervals with a button press as for those
without a button press. In other words, the pres-
ence or absence of a stimulus is independent of
the presence or absence of a button press. We
tested this null hypothesis to determine if a viewer
is cognitively aware of the RS.
In Table 2, the fractional hitting rate is p1 =
A/(A +B), and the fractional missing rate is p2 =
C/(C+D). The total number of 1-second inter-
vals is N = (A+B+C+D), and the total stimulus
rate is po = (A+ C)/N.
Table 2
Data Schema for Interval Conditions
Stimulus
Yes
No
Response
Yes
A
B
No
C
D
Then, under the null hypothesis, the following
statistic is approximately normally distributed
with a mean of 0 and a variance of 1:
2 =
(P1-P2)
po ( 1 - Po ) (A+ B ) +
( C + D )
Table 3 shows N, Po, P1, P2, 2, p-value, and the effect
size, r, for the three sessions for which button-
press data were collected. As in the earlier SRI
study, there is no indication that the viewers were
cognitively aware of the RS.
Table 3
Button Pressing Results
Viewer
N
Po
P1
P2
Z
p
r
002
1210
0.167
0.198
0.164
0.951
0.163
0.027
009
1280
0.091
0.068
0.094
-0.978
0.836
-0.027
372
1089
0.157
0.119
0.160
-0.996
0.840
-0.030
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2
P-Value (1-tail)
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IV DISCUSSION AND CONCLUSIONS
We have found statistical evidence that the rela-
tive phase shift from -0.5 to 0.5 seconds of an RS
are not characteristic of the data at large (Z, =
1.99, p ≤ 0.024, effect size = 0.599). The com-
bined statistic for the PS indicates that the relative
phase shift from -0.5 to 0.5 seconds of a PS are
also not characteristic of the data at large (Zs =
2.92, p ≤ 0.002, effect size = 0.924). Averaged
across all viewers, the magnitude of the results, as
indicated by their effect sizes of 0.599 and 0.924,
respectively, is considered robust by accepted be-
havioral criteria defined by Cohen.9*
1. Root-Mean-Square Phase
Searching for a change of phase as a result of an
RS is a natural extension of results quoted in the
literature. For example, Rebert and Turner5 re-
port an example of photic driving (i.e., an extreme
example of phase locking) at 16 Hz. In their work,
a subject was exposed to a 16-Hz visual DS ran-
domly balanced with no stimulus during 4-second
epochs. The average power spectra showed ap-
proximately 10-Hz alpha activity during the no-
light epochs, and a strong 16-Hz and no 10-Hz
peak during the 16-Hz epochs.
One interpretation of their result is that the alpha
rhythm was blocked, and the CNS "locked" on to
the flashing stimulus. Eason, Oden, White and
White, 10 report a phase-shift phenomenon when
a rare stimulus, which is random relative to the in-
ternal alpha activity, is presented as a DS:
" ... when a stimulus flash is presented, the
resulting primary evoked response acts as a trigger
stimulus which temporarily synchronized a
certain percentage of the neural elements
normally under the influence of an internal
pacemaker. ... Desynchronization of the elements
participating in the evoked response would occur
as the elements are brought back under the
influence of an internal pacemaker or are affected
by neurons not involved in the response."
In other words, the internal alpha is momentarily
interrupted by an external stimulus, and, in the
absence of continuing external stimuli, returns
back to its original frequency, but at a random
phase relative to its pre-stimulus state.
To understand what would be expected in our ex-
periment for the distribution of RMS phases dur-
ing the Monte Carlo simulations, we examine a
hypothetical case. Suppose that the viewer's alpha
activity was a continuous wave at a single fre-
quency. A phase change is computed between 500
ms before and 500 ms after each Monte Carlo
"stimulus." Therefore, regardless of the entry
point, the relative phase change would be zero,
and the RMS phase over many such "stimuli"
would also be zero.
Real alpha activity, however, is not continuous,
Rather, it appears in bursts lasting from 100 to
5000 ms. Random Monte Carlo "stimuli" would
sometimes occur within such bursts and some-
times near the edges. Thus, we would expect a
nonzero RMS phase over many such "stimuli,"
but the individual relative phases would not be
uniformly distributed. Depending upon the view-
ers' alpha characteristics, the distributions would
be enhanced near zero RMS phase.
If we assume that Eason, et al., are correct, and
that a phase shift is expected as a result of an RS,
then the expected distribution of RMS phases is
uniformly distributed on [-TT, TT]. In this case, the
phase change is related to the relative timing be-
tween the external stimulus and the internal al-
pha-a completely random relationship. Thus,
the variance of the RMS phases in the experimen-
tal condition should be larger than those com-
puted during the Monte Carlo runs. Figure 15 is a
schematic representation of these models.
* Values of 0.1, 0.3, and 0.5 correspond to small, medium, and large effects, respectively.
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Continuous Alpha
Remote Stimulus
Monte Carlo
-TT
0
TT
Phase (radians)
Figure 15 Idealized Distributions for Relative
Phase Shifts
As a first step in testing these models, we com-
puted the expected variance for the RMS phase,
given that the individual phases are uniformly dis-
tributed on [T, TT]. Using a Taylor Series expan-
sion for RMS phase, the variance is given by:11*
1 -
1
30n
F
(rad2) , or
2160
= 210 (deg2),
where n is the number of individual phases.
Table 4 shows the viewer identification, the two-
tailed z-score from Table 1, the number of RS, the
theoretical variance for the RMS phase, the ob-
served variance from the Monte Carlo runs of 500
passes each, and the X2 and its associated p-value
for a variance-ratio test.
Combining the X2 across all 11 sessions gives an
overall significant result (X2 = 5121.5, df = 5489,
p < 0.0002). This indicates that the Monte-
Carlo-derived variances are significantly smaller
than the theoretical variances based on uniformly
distributed phases. The two viewers who demon-
strated the largest z-scores (002 and 007) also
show sharply reduced Monte Carlo variances.
Table 4
Comparison Between Monte Carlo Phases and Theory
I.D.
Z-Score
(RS)
Number of
RS
Variance of RMS Phase
Theoretical
Observed
x2
df = 499
P-Value
009
-0.524
96
22.50
25.46
564.6
0.978
002
2.653
118
18.31
13.63
371.5
4.9×10-6
0.871
76
28.42
24.43
428.1
0.010
372
0.885
90
24.00
23.25
483.4
0.316
374
0.501
102
21.18
18.64
439.2
0.025
007
1.555
93
23.23
18.66
400.8
4.6×10-4
389
-0.191
97
22.27
23.35
523.2
0.780
-0.050
92
23.48
22.29
473/7
0.214
0.706
98
22.04
20.22
457.8
0.093
531
0.274
101
21.39
21.05
491.1
0.408
454
-0.090
52
41.54
40.48
487.3
0.363
We must conclude that a uniform distribution for
the phase is not a good assumption. To determine
what the phase distribution was for the RS, we
constructed histograms from the raw data.
Figure 16 shows the distribution of phases for the
RS and Monte Carlo stimuli for viewer 002.
While the RS distribution is enhanced near =180
degrees and suppressed near 0 degrees compared
to the Monte Carlo distribution, the differences
are small (X2 = 10.62, df = 8, p ≤ 0.224) and,
therefore, the random-phase model does not ap-
pear to be a good fit to the data for viewer 002 on
his 25 September session.
Figure 17 shows the same distributions for viewer
007. In this case, the RS distribution is nearly uni-
form on [-180,180] degrees, but it differs only
* We thank Professor Jessica M. Utts, Statistics Department, University of California, Davis, California, for suggesting this
approach.
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slightly from the Monte Carlo distribution
(X2 = 9.47, df = 8, p ≤ 0.304).
From the data shown in Table 4, we see that the X2
indicates significant overall differences between
the theoretical and observed phase distributions.
However, Figures 16 and 17 show that the differ-
ences between RS and Monte Carlo distributions
are small. It is most probable, therefore, that the
RS coupling to the CNS is weak, in general, and
that the position of the sensor array is not neces-
sarily optimized to sense the phase changes.
2. Viewer Dependencies
Viewers 002, 009, and 372 have produced consis-
tent remote viewing results for many years- since
1972 for viewers 002 and 009, and since 1979 for
viewer 372. Viewer 389 is a recent addition, and
has produced examples of excellent remote view-
ing in the only experiment in which he has partici-
pated; however, he has produced significant
results in another laboratory. Whereas viewer 002
produced the largest z-score (Zs = 2.653), viewer
009 produced the smallest (Zs = - 0.524). The
combined effect size for the experienced viewers
is 0.621, and is 0.559 for the inexperienced view-
ers. The difference is not significant.
There are two considerations that prevent draw-
ing conclusions about the viewer dependence of
the data. The number of independent samples is
small, but the most compelling argument against
drawing conclusions is that placement of the sen-
sor array is a seriously confounding factor. As
stated in Section II.2, we positioned the array in a
location that maximized the response to a DS.
This may not be the appropriate positioning for
everyone. Indeed, it might not be optimal for any-
one.
To determine if there were any "obvious" spatial
dependencies that might indicate a more optimal
array placement, we computed a complete set (all
sensors) of Monte Carlo distributions for one ses-
sion for viewer 002. Figure 18 shows the single-
tailed p-values for the RMS phases for the RS
and PS. They are displayed in the standard sen-
sor-array configuration. The pattern for the RS
suggests that a more optimal positioning of the ar-
ray would be in the sensor 2-7 direction as indi-
cated by an arrow in Figure 18.
30
Remote Stimuli
Monte Carlo Stimuli
25
20
Density
15
10
5
0
-160-120-80 -40 0
80 120 160
Relative Phase Shift (deg)
Figure 16 Phase Distributions for Viewer 002:
8/25/88
30
Remote Stimuli
Monte Carlo Stimuli
25
20
Density
15
10
5
0
-160-120-80 -40
40 80 120 160
Relative Phase Shift (deg)
Figure 17 Phase Distributions for Viewer 007:
3/29/89
Remote Stimuli
Pseudo Stimuli
0.002
0.126
3
0.848
2
0.710
0.036
0.128
0.184
0.924
0.854
0.668
5
0.572
0.238
0.684
0.700
Figure 18 Phase p-values for Viewer 002: 8/25/88
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3. Pseudo Stimuli
It was initially thought that the PS would act as a
within-run control. The results indicate, how-
ever, that there was, on the average, a larger re-
sponse to the PS than to the RS. While the
difference was not significant, it is important to
note that both of the responses are considered
statistically robust (effect sizes of 0.599 and 0.924
for the RS and PS, respectively). A number of
viewers' responses appear to produce phases on
opposite sides of the Monte Carlo distributions
(e.g., viewers 002 and 007), but there is no overall
correlation between the RS and PS p-values.
A brief description of the hardware and software
that is responsible for stimulus generation may
help in understanding this outcome. The stimuli
and their timing are imitated by an HP computer,
but are controlled by an IBM PC. Each stimulus
type has its own frame buffer within the PC. Our
RS consists of a pattern of 1s and Os that represent
a sinusoidal grating in the center of an otherwise
blank field. The PS pattern, a blank field that con-
sists of all Os, resides in a separate buffer. An in-
terface board between the PC and a standard
video monitor has its own internal frame buffer,
which is automatically and continuously scanned
at 30 Hz to provide a standard interleaved video
signal. See Figure 19.
Main HP
IBM PC
Monitor
Stimulus
Type RS/PS
Stimulus Initiation
RS
Frame
Buffer
30 Hz Inter-
leaved Video
Output
Frame
Buffer
PS Frame
Buffer
Figure 19 Sequence of Events for Stimuli Generation
When the HP computer signals the PC to provide
the appropriate stimulus, the following sequence
of events are followed (see Figure 19):
(1) Phase locked to 60 Hz, the interface frame
buffer is loaded with a copy of the appropriate
stimulus frame buffer (either RS or PS).
(2) The interface board automatically sends this
pattern interleaved at 30-Hz.
(3) After a preset time, approximately 100-ms in
our experiment, the PC resets the interface
frame buffer to zero (blank screen), and waits
until another stimulus signal is received.
At the video monitor, the PS are indistinguishable
from the between-stimuli blank screens. At the
PC, however, the PS are distinguishable from the
blank screen background, because the PC must
copy a frame buffer (albeit all Os) into the output
frame buffer.
In our experiment, the RS and PS results were
statistically identical, and independently, both
were significantly different from the Monte Carlo
distributions. This raises the question as to what
constitutes the target stimulus. Our result is un-
expected given the target was considered to be
what was displayed on the remote monitor.
It is conceivable that the internal activity of the
PC, or its companion computer, was acting as an
unintended target. If this were true, then there
might be an electromagnetic (EM) coupling be-
tween the viewer's CNS and the internal elec-
tronic activity of the computers. It is well known
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that computers radiate EM energies at relatively
high frequencies; for frequencies above 100 Hz,
the shielded room is transparent. Analysis of the
background runs (i.e., data collected in the ab-
sence of a sender or viewer) showed no EM cou-
pling into the MEG electronics; therefore, it
remains possible that the statistical effects we
have seen are due to CNS responses to remote
bursts of EM energy.
Let us assume that the overall RS and PS effects
are meaningful. Since the PSs are indistin-
guishable at the monitor from the between-stim-
uli background but are distinguishable at the IBM
PC, then the present experiment demonstrates
that the source of stimuli is the IBM PC.
During the SRI/Langley Porter study in 1977, SRI
developed an entirely battery operated stimulus
generator as a special precaution against the pos-
sibility of system artifacts in the form of EM
pickup. They reported significant CNS responses
to remote stimuli, nonetheless.6 Therefore, it re-
mains possible that we have observed an anoma-
lous information transfer.
Before further research is conducted, it is impor-
tant to measure the EM radiation, and to see if it
is of sufficient strength to be detected (by the ap-
propriate hardware) in the shielded room.
By adjusting the PC program, the PS internal ac-
tivity can be eliminated. It would be interesting to
see if the similarity between the RS and PS results
persists.
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