Spectral Correlates of Arousal and Activation
Despite reports of electrical activity in animal brains
(Caton, 1895), the existence of brain potentials remained unknown
and unexplored in humans until Hans Berger (1930) published his
first report on electrical activity of the human brain, which he
designated the electroencephalogram, or EEG (Remond & Lairy,
1972; Gevins & Schaffer, 1980). Five years passed before his
findings were confirmed in the English-speaking world (Adrian &
Matthews, 1934; Remond & Lairy, 1972; Petsche, 1989).
Discovery and characterization of the alpha rhythm
In his first report, Berger (1930) characterized the
"waves of the first order" in human EEG, which for the sake of
brevity he designated the alpha rhythm. Alpha activity consisted
of large (up to 200 æV) sinusoidal waveforms 90 ms to 120 ms in
duration which appeared against smaller background waves, "waves
of the second order" or beta rhythm. Alpha waves were pronounced
in posterior regions but could also be recorded from central and
frontal areas. Most importantly, alpha activity diminished in
response to stimulation and mental effort.
Between 1929 and 1937 Berger published his investigation
of the relationship between mental processing and alpha activity
(Remond & Lairy, 1972). He described the phenomenon of alpha
blocking, an abrupt suspension of alpha waveforms in ongoing EEG,
which can readily be observed when an individual with eyes closed
in a relaxed state opens his or her eyes. Berger (1930) claimed
that alpha blocking was independent of respiratory, vascular, or
motoric responses (confirmed by Bohdanecky, Indra, Lansky, &
Radil-Weiss, 1984; Valentine, 1954; Ray & Cole, 1985) and
resulted when individuals attended to objects in their field of
view. When individuals open their eyes in a darkened room, there
is little or no change in alpha activity (Bohdanecky et
al., 1984) and, conversely, alpha suppression can occur to
stimulation or task demands when one's eyes are closed (Etevenon,
1986; Adrian & Matthews, 1934).
Berger (1930) concluded that directing attention toward
a stimulus was responsible for fluctuations in alpha activity and
the amount of alpha activity reflected the extent of mental
processing (Petsche, 1989). He speculated that interhemispheric
EEG synchrony was mediated by the thalamus and rejected the claim
that the alpha rhythm was generated in occipital cortex
exclusively (Remond & Lairy, 1972). Beyond these conjectures, he
remain curiously silent on the physiological mechanisms
responsible for the production of alpha activity.
Physiological mechanisms of EEG rhythmicity
Moruzzi and Magoun (1949) shed light on the origins of
rhythmicity in EEG. Stimulation of the reticular formation of the
brainstem produces fast low-amplitude waves in cortical
recordings, usually with concomitant behavioral arousal. They
concluded that the reticular formation regulated gross changes in
alpha activity (i.e., nonspecific arousal). Other experiments in
animals, particularly the dog, revealed that specific reactivity
observed in the alpha rhythm in response to task and stimulation
depended on thalamocortical networks. Alpha rhythms recorded
simultaneously from visual cortex and associated thalamic nuclei
(lateral geniculate, pulvinar) exhibit identical peak frequency
and bandwidth, high degrees of coherence, and similar responses
to stimulation (Lopes da Silva, 1978; Lopes da Silva, Van Lierop,
Schrijer, & Storm van Leeuwen, 1973). The biocircuitry of the
reticular thalamic nuclei (RTN) acts as a pacemaker, recruiting
thalamic nuclei into 8-13 Hz (alpha) rhythms by means of powerful
inhibitory postsynaptic potentials (Andersen & Andersson, 1968;
Steriade & Llinas, 1988; Steriade, Gloor, Llinas, Lopes da Silva,
& Mesulam, 1990). During information processing activation of
specific thalamic nuclei by sensory afferences, along with
localized basal forebrain excitation of RTN, which itself
inhibits thalamic nuclei, results in preferential activation of
certain thalamic cells. Visual perception, for example, activates
neurons in pulvinar nuclei, yielding diverse (desynchronized)
rates of oscillations for these cells while other thalamic nuclei
remain relatively synchronized. Nonspecific afferents from the
brainstem, namely the pedunculopontine tegmental and laterodorsal
tegmental nuclei, also act on thalamocortical networks, affecting
general tonic activity of these networks (Steriade et al.,
1990). Electrical potentials measured from the scalp are presumed
to be a function of postsynaptic potentials of millions of
pyramidal cells in cortical layers IV and V (Lopes da Silva,
1991; Gevins, 1986).
The physiology of rhythmic activity has been summarized
into a general model (Andersen & Andersson, 1968; Steriade &
Llinas, 1988; Sterman & Bowersox, 1981; Steriade et al.
1990; Lopes da Silva, 1991). According to this model, metabolic
characteristics of (isolated) neurons generate instrinic
oscillations usually between 1 to 20 Hz. In an intact brain,
activity of single neurons are regulated by pacemaking
biocircuitry (RTN) which incorporates actions of single cells
into larger ensembles, uniting random discharges of individual
cells into simultaneous uniform volleys. Synchronization of
cellular firing results in a summation of electrical potentials
that can be recorded at the scalp as high-amplitude oscillations
of slow frequencies (i.e., alpha rhythm). Desynchronization
arises when individual neurons or neural groups are recruited out
of these uniform volleys and become dedicated to specific
information processing. As neuronal ensembles are uncoupled from
synchronized firing into their own (diverse) oscillatory
frequencies, large, slower waveforms are replaced by faster
frequencies of lower amplitude.
In neuropsychological terms, intrinsic (alpha) rhythms
become partially or wholly desynchronized when sensory
information is anticipated, attended to, or otherwise processed
(Pfurtscheller, 1986; Pfurtscheller, 1989). Uncommitted cortical
areas can remain in an "idling" state while other areas are
desynchronized (Lopes da Silva, 1991; Pfurtscheller, 1992).
Desynchronization can be localized to a single electrode or
involve several electrodes and cortical areas (Pfurtscheller &
Klimesch, 1990; Sterman et al., 1994). Regional patterns
of simultaneous desynchronization and synchronization may
characterize specific cognitive or behavioral states such as
sensorimotor performance (Pfurtscheller & Klimesch, 1990;
Pfurtscheller, 1992). EEG may prove useful in identifying
processing strategies employed by different subjects (Tucker,
1976; Dunn & Reddix, 1991).
Topographic specificity of alpha attenuation
Alpha activity is sensitive to stimulus properties and
demonstrates topographic specificity associated with modality.
Visual stimulation activates occipital and parietal cortex and
acoustic stimulation activates temporal cortex (Grillon &
Buchsbaum, 1986; Pfurtscheller, Maresh, & Schuy, 1977).
Similarly, sensory and motor event-related potentials (ERPs) are
largest over corresponding primary and association areas (Gevins,
1986). Stimulus intensity, complexity, familiarity, and
meaningfulness can influence alpha activity, presumably due to
attentional factors in response to each of these properties (Gale
& Edwards, 1983; Berlyne & McDonnell, 1965; Baker & Franken,
1967; Remond & Lairy, 1972; Boiten, Sergeant, & Geuze, 1992). The
degree of alpha attenuation also shows habituation, diminishing
after repeated presentations of the same stimulus (Gevins &
Schaffer, 1980).
Baseline conditions
The most reliable and well-known finding in EEG research
occurs when an individual resting with eyes closed opens his or
her eyes. When eyes are opened in a lit room, alpha blocking
occurs: the dominant alpha rhythm is displaced by faster, lower
amplitude waveforms. This attenuation in alpha activity is
widespread, incorporating most or all cortical areas -- a good
example of nonspecific arousal (Legewie, Simonova, & Creutzfeldt,
1969; Etevenon, 1986; Etevenon et al., 1990; Pockberger,
Petsche, Rappelsberger, Zidek, & Zapotoczky, 1985; Sterman et
al., 1994).
Statistical results of topographic EEG lend themselves
to two general physiological characterizations. A significant
main effect occurs when one condition elicits a global change in
attention or resource allocation that is not localized to any
brain region (i.e., nonspecific arousal). Task by site
interactions indicate when specific cortical functions are
activated by one task but not others -- in other words, selective
attention.
Rationale for additional spectral parameters
Gross physiological rhythms such as ultradian and
circadian rhythms are well-known (e.g., Lavie, 1989) and may
affect EEG activity (Meneses-Ortega & Corsi-Cabrera, 1990).
Systematic fluctuation of shorter duration is also present in the
EEG signal. Consistent activation without brief alpha bursts or
similar disengagements is not seen for even brief periods of
time. Alpha periodicity was noted as early as Berger (1930) who
believed the phenomenon reflected attentional modulation. Tasks
can temporarily attenuate the alpha periodicity associated with
the "idling" or neutral state, but constant fluctuations are
never extinguished completely by even very high or very low task
demands. Periodicity in alpha activity is acknowledged by most
EEG scientists, yet nearly all studies ignore trend dynamics and
average across fluctuation. A significant fraction of information
within the EEG signal is thus lost. When an individual becomes
engaged in stimulus or task processing, the natural wax and wane
of attention is inhibited and the degree of inhibition is
reflected in alpha variability. Standard deviation of epoch
magnitude and residual variance of a linear regression are useful
measures of this task-induced variability.
Spectral parameters analyzed
The following four spectral parameters were analyzed in
this study: log mean magnitude (LMAGN), standard deviation of
epoch magnitude (SD), coefficient of a linear regression (SLOPE),
and residual variance of a linear regression (RV). LMAGN is
calculated by performing a natural logarithmic transformation of
the average epoch magnitude (Gasser et al., 1982). SD,
SLOPE, and RV assess temporal dynamics of alpha activity. SD
reflects mean (global) variability associated with a task. RV is
the error term of the linear regression and reflects variability
not accounted for by a linear trend. SLOPE reflects the best
linear trend of magnitude values across a number of seconds or
minutes.
SLOPE values depend on the amplitude level at condition
onset. For this reason, no demands were made of the subject for a
short interval (approximately 10 s) prior to each condition. This
"dead space" was used to reinstate a neutral attentional state,
but without allowing too much time to pass so that a subject
would become bored and restless. SLOPE values are also very
sensitive to small changes in task demands, making it most useful
for assessing brief intervals (e.g., 10 s) and least useful for
assessing long duration tasks (e.g., 5 min). Conditions in the
present study fall in between these extremes, so that SLOPE is
mostly valuable when alpha trends are consistent across a task
(i.e., RV is small).
LMAGN and SD are measured in microvolts (æV) and SLOPE
and RV are measured in mean microvolt difference per epoch (ë æV
per epoch). RV is always positive and is small when a strong
linear relationship exists between amplitude and time. When the
linear relationship is poor, RV is larger. Cortical engagement is
associated with low amplitude, low variability, negative trend,
and low trend instability.
LMAGN, SD, and RV reflect similar processes of
activation and thus act together as a test of reliability.
However, it must be kept in mind that SD can be influenced by
SLOPE values and may not replicate results found in LMAGN or RV.
On the other hand, SLOPE values do not reflect activation
differences between tasks per se, but whether the
attentional state varies within a task. Difficult tasks
and high interest stimuli will usually result in more
attentiveness with time (i.e., negative SLOPE values), but not
always. In general, negative trends indicate task or stimulus
habituation, or withdrawal from a task, and positive trends
indicate concentration on task.
Summary of predictions
Three sets of eyes closed (EC) and eyes open (EO)
baseline conditions were acquired to evaluate the four spectral
parameters proposed above. No differences between replications
are predicted for any spectral parameter or either condition.
Opening one's eyes will result in nonspecific activation but no
specific (topographic) attenuation in alpha activity compared to
the EC condition (Sterman et al., 1994). As a more
activated state, EO will show nonspecific reductions in
amplitude, variability, and trend instability compared to EC. EC
will consist of a stable state of relaxation and non-
attentiveness whereas a prolonged eyes open condition without
attentional demands will result in deteriorating subject
attentiveness and vigilance (Sterman et al., 1994).
Consequently, the slope for EO will be positive and higher than
the slope for EC (approximately zero).
METHOD
Subjects
EEG was acquired from 27 healthy right-handed subjects.
All subjects had mean alpha amplitude in EO conditions 10% to 50%
below EC values. Seven subjects were excluded from the study due
to excessive artifact or failure to meet the above criterion. EEG
values were analyzed for 20 subjects (10 male, 10 female) between
21 and 40 years of age (mean age 29.45 y) while they viewed
cinematic narratives and during baseline and control conditions.
Subjects reported moderate to strong right-handedness as assessed
by a modified Edinburgh Handedness Inventory (Appendix B),
preferring the right hand or both hands equally on seven manual
activities, with the right hand preferred on five or more manual
activities (Oldfield, 1971). Hand dominance was also evident in
writing samples from each hand. Subjects reported no history of
neurological problems, use of a controlled substance 24 hr prior
to the study, nor recent experiences which could alter one's
normal psychophysiology (e.g., disturbed sleeping habits,
atypical stress). Subjects were recruited through advertisements
posted at the University of California at Los Angeles (UCLA) and
the California State University, Northridge (CSUN) campuses.
Informed consent was obtained from each subject prior to his or
her participation, using a form approved by the Veterans'
Administration Human Study Committee. Subjects were presented
with a topographic image of their brain for participating in the
study.
EEG Acquisition
A Neurosearch-24 acquisition unit (Lexicor Medical
Technologies, Inc.) was used with an IBM-AT 486 compatible
personal computer running Lexicor version 1.51 EEG acquisition
software. Using a 12 bit A/D converter, EEG data were digitized
at 512 samples per s during acquisition and was recorded and
displayed 128 times per s, which resulted in 2 s epochs with 0.50
Hz frequency resolution. Lexicor software also performed an FFT
of raw data for each epoch. High and low pass filters were set at
2 Hz and 16 Hz with rolloffs of 12 and 48 Db/octave,
respectively. The common mode rejection ratio was greater than 90
dB at 60 Hz, notch filtering was applied at 60 Hz, and gain was
set at 32,000. Topographic EEG was recorded from the scalp using
an elastic lycra cap (Electro-Cap International Inc.) which
consisted of 20 electrode ports containing pure tin electrodes
arranged according to the international 10-20 placement system.
Preparation
After completion of a handedness questionnaire and film-
viewing survey (see Chapter 4), subjects were fitted with an
appropriate-sized Electro-Cap. Preparation of recording sites
involved cleaning the scalp at each site with gentle scraping of
a dowel (uncottoned Q-tip). Electrolytic gel was injected into
each electrode port using a syringe with a blunted needle.
Reference electrodes were attached to each ear lobe. All
electrode impedances were below 7 K. All preparation instruments
which came in direct physical contact with the subject were
discarded after use.
Procedure
EEG was acquired at the Sepulveda Veterans
Administration Medical Center Neuropsychology Laboratory.
Subjects were tested individually in a dimly lit, electrically-
shielded and sound-attenuated room approximately 2.5 by 3.5 m in
size. Subjects were informed that subjective and
psychophysiological reactions to film previews were being
studied. Subjects were seated in a padded lounge chair which they
could position for maximum comfort. A 21" screen television set
was situated approximately 2 m from the subject at eye level. A
small lamp controlled by the experimenter was used to illuminate
the room whenever light was required. Data were acquired and
stored to a hard disk situated in an adjacent room. Subject were
monitored via close circuit video and two-way intercom systems
during the study and EEG signals were monitored in real-time on a
computer screen. All recording sessions began between 12 pm and 4
pm.
Baseline procedures
EC and EO baseline conditions of approximately 2 min
were recorded three times during the study, spaced approximately
50 minutes apart (mean intervals of 51.1, 53.0 min,
respectively). Between recordings, subjects watched short films
(previews of coming attractions) and answered question about what
they watched. Continuous topographic EEG was recorded from 19
sites (Appendix A).
Data Analysis
All EEG records were visually inspected on a computer
monitor for artifacts. Any 2 s epoch contaminated by artifact was
discarded prior to analysis. If artifact was present at any
recording site, data from all 19 recording sites were rejected
for an epoch.
For each condition, 50-64 epochs (100-128 s) were
analyzed depending on the number of contaminated epochs. No
record was used which had less than 80 percent of its epochs
artifact-free. Each epoch underwent cosine tapering using a
Blackman-Harris 4-term function which effectively reduces the
frequency contributions from one-sixth of either end of the
epoch.
EEG analysis focused on the 8-12 Hz frequency band. Four
spectral parameters were calculated for each condition: amplitude
(LMAGN), variability (SD), trend (SLOPE), and trend instability
(RV).
RESULTS
Univariate Analyses of Variance (ANOVA) were used to
assess topographic differences between replications and between
baseline conditions. For initial comparisons, site values were
pooled into six regions: frontal pole (Fp1, Fp2), frontal (F7,
F3, Fz, F4, F8), central (C3, Cz, C4), temporal (T3, T4, T5, T6),
parietal (P3, Pz, P4) and occipital (O1, O2) regions (cf.
Klimesch et al., 1990, for similar treatment). Data were
pooled to minimize computational complexity. The Huynh-Feldt
correction of degrees of freedom were used to compensate for data
sphericity. Individual site comparisons within an area were
performed only if topographic interactions were significant.
Functional laterality was assessed by comparing differences
between homologous recording sites from each hemisphere (Fp1-Fp2,
F7-F8, F3-F4, T3-T4, C3-C4, T5-T6, P3-P4, O1-O2).
Replication differences
Main effects were found between replications in both
conditions for all spectral parameters. A topographic interaction
between EC replications was found in SLOPE. No other topographic
differences were found.
The first eyes closed replication (EC1) was lower in
LMAGN, SD, and RV, compared to the second eyes closed replication
(EC2) [FLMAGN(1,19)= 12.211, p<.01; FSD(1,19)=
11.097, p<.01; FRV(1,19)= 9.589, p<.01].
Similar differences were found between EC1 and the third eyes
closed replication (EC3) [FLMAGN(1,19)= 13.011,
p<.01; FSD(1,19)= 11.680, p<.01;
FRV(1,19)= 13.249, p<.01]. SLOPE was higher in EC1
than in either EC2 and EC3 [FSLOPE(1,19)= 10.045,
p<.01; F(1,19)= 15.145, p<.01, respectively]. No
significant main effect was found between EC2 and EC3.
EO1 exhibited lower values in all parameters compared to
EO3 [FLMAGN(1,19)= 10.162, p<.01; FSD(1,19)=
11.247, p<.01; FSLOPE(1,19)= 7.937, p<.01;
FRV(1,19)= 11.022, p<.01]. Similar differences were
observed between EO2 and EO3, though SLOPE did not differ
[FLMAGN(1,19)= 20.517, p<.01; FSD(1,19)=
10.840, p<.01; FSLOPE(1,19)= 0.783, ns;
FRV(1,19)= 13.630, p<.01]. No differences were
found between EO1 and EO2 (see Table 3.1).
Table 3.1. Mean spectral values for replicated baseline conditions (19 sites, n=20)
LMAGN SD SLOPE RV
--------------------------------------------------------
EC1 1.91* 3.12* 0.014* 0.021*
EC2 2.03 3.51 -0.025 0.023
EC3 2.06 3.56 -0.024 0.025
--------------------------------------------------------
EO1 1.36 1.70 0.004 0.012
EO2 1.38 1.89 0.012 0.013
EO3 1.50* 2.17* 0.016+ 0.016*
--------------------------------------------------------
* p<.01, all other replications
+ p<.01, first replication only
Topographic differences in EC replications were found in
SLOPE [F(2,34)= 8.381, p<.001]. SLOPE values for six out
of seven posterior sites (all except Pz) were higher in EC1 than
EC2 [p<.01]. Fifteen of 19 sites (all except Fp1, Fp2, F7,
and T4) had higher slopes in EC1 compared to EC3 [p<.01].
No slope differences were found between EC2 and EC3
[p>.01].
Task-induced laterality did not differ between
replications in any parameter for EC or EO conditions
[p>.05].
Baseline differences
Replication data were averaged for each spectral
parameter and baseline condition (see Table 3.2). Main effects
and topographic interactions were found between EC and EO.
Table 3.2. Mean spectral values for baseline conditions, averaged across replications (19 sites, n=20)
LMAGN SD SLOPE RV
--------------------------------------------------------
EC 2.00* 3.40* -0.012* 0.023*
EO 1.41 1.92 0.011 0.014
--------------------------------------------------------
* p<.01
Compared to EC, EO exhibited lower LMAGN, SD, and RV
[FLMAGN(1,19)= 111.550, p<.01; FSD(1,19)=
103.384, p<.01; FRV(1,19)= 78.680, p<.01].
EC SLOPE was negative and smaller than EO SLOPE which was
positive [FSLOPE(1,19)= 7.410, p<.01]. No laterality
differences were found between conditions for any parameter
[p>.01].
Topographic differences between EC and EO involved
multiple sites from disparate functional areas, essentially
duplicating main effects. Recording site by condition
interactions were found in LMAGN, SD, and RV, and consisted of
significant lower values at all 19 sites for EO [p<.01].
DISCUSSION
Subjects were more aroused during the first eyes closed
replication and less aroused during last eyes open replication.
The eyes open condition was also more aroused than the eyes
closed condition.
Eyes closed replications
Contrary to predictions, subjects in the first eyes
closed baseline were more aroused than during later conditions,
probably due to residual attentiveness and low level anxiety
displayed by the subjects. Subjects may not have been acclimated
to the experimental setting and procedure during the first eyes
closed condition (Rebert & Mahoney, 1978). Many subjects
demonstrated low level anxiety over the first recording period by
asking questions abruptly at any time during the condition. The
lack of functional differences between replications parallel
findings reported by Sterman et al. (1994) and Suyenobu
(1994). Etevenon et al. (1989) and Etevenon et al.
(1990) reported functional differences between eyes closed
replications, but these results may not be reliable as neither
study corrected for spatial dependence (Vasey & Thayer, 1987).
Eyes closed conditions appeared to represent two related
psychological states: onset of relaxation, characterized by
moderate activation that declines consistently over time (EC1);
and thorough relaxation, a highly deactivated state characterized
by fluctuations and intermittent increasing activation (EC2 and
EC3; see Figure 3.1). However, no behavioral measures were
analyzed to confirm these characterizations.
Figure 3.1. Trend of mean magnitude values for three EC
replications (n=20).
Extreme deactivation was not maintained during any EC
resting condition. In fact, magnitude values for each replication
converged across time. Unlike task conditions, attentional and
cognitive processes during eyes closed baselines are elicited by
the individual's internal state and are not contingent upon
external stimulation. The psychological state associated with
intermediate magnitude may be self-reinforcing and most relaxing.
Measures of variability in eyes closed baselines also
demonstrated a similar convergence to intermediate values.
Eyes open replications
Nonspecific differences were found between eyes open
replications. All replications exhibited a positive trend, but
the rate of deactivation for the third eyes open condition was
especially extreme. An abrupt shift in subject's mind set or
attentiveness seemed to occur during the third replication. Most
subjects may have assumed (correctly) that no other demands were
to be made upon them and they relaxed completely. An onset of
fatigue cannot explain this finding in that the third eyes closed
condition, recorded immediately after, resembled earlier
conditions. In contrast to eyes closed conditions, eyes open
replications diverged across time and a drop in motivation likely
occurred in the final replication.
Suyenobu (1994) also found significantly less activation
in later EO replications compared to earlier conditions. However,
Dolce & Waldeier (1974) and Sterman et al. (1994) reported
no differences between three sets of eyes open baselines in alpha
activity, though Dolce & Waldeier (1974) did note differences in
other spectral bands. Fernandez, Harmony, Rodriguez, Reyes,
Marosi, & Bernal (1993) found topographic differences between
eyes open replications (limited to four sites) which they
attributed to anxiety during the first recording session. No
topographic differences were found between eyes open replications
in the present study.
As shown in Figure 3.2, eyes open replications
represented two mental states: nominal attentiveness,
characterized by moderate activation that declines regularly over
time (EO1, EO2); and inattentiveness, characterized by modest
activation that deteriorates rapidly and unevenly over time
(EO3). Again, these characterizations are not confirmed.
Figure 3.2. Trends of magnitude values for three EO
replications (n=20).
Comparison between baseline conditions
Replications were averaged to derive condition means.
Although an argument can be made for averaging only like
conditions (e.g., EC2 and EC3 only), increased variance was
tolerated in light of expected substantial differences between
eyes closed and eyes open conditions (Adrian & Matthews, 1934;
Gevins & Schaffer, 1980; Sterman et al., 1994), and
because arousal heterogeneity is suspected in most studies which
employ similar baseline conditions (Gevins, 1984; Freeman &
Maurer, 1989).
As expected, opening the eyes resulted in nonspecific
arousal, a finding consistent with the literature (e.g., Legewie
et al., 1969; Etevenon, 1986; Sterman et al.,
1994). Evidence of nonspecific arousal was also reflected in
reduced variability and trend instability. As shown in Figure
3.3, the eyes open baseline is comprised of a moderate arousal
which deteriorates steadily over time and the eyes closed
baseline is an very low arousal state that settles to a less
extreme state of arousal. This is accomplished by severe and
erratic fluctuations of attention.
Figure 3.3. Trends of magnitude values for replicated
baseline conditions (n=20).
The differences between baselines is found not only in amplitude,
but all spectral parameters. It is unfortunate that few EEG
scientists analyze these additional parameters. Measures of
variance, normality, and other indices of dispersion and
asymmetry have proven reliable and meaningful in other
disciplines (Cacioppo & Dorfman, 1987).
Cerebral asymmetries did not differ between baselines
conditions for any spectral parameter. Butler & Glass (1974) also
found no evidence of functional asymmetries in alpha power
between baseline conditions. However, it should be noted that
individual differences in functional laterality during baseline
conditions have been reported (Tomarken, Davidson, & Henriques,
1990).
"Macrostate" assumption
An assumption underlying this experiment is the
conception of psychological or physiological "macrostates".
According to this model, the various perceptual and cognitive
operations associated with a mental or behavioral condition is
thought to constitute a single distinguishable neurophysiological
state with a distinct and reliable spectral pattern (Gevins,
1984; Gevins, 1986). Eyes closed and eyes open resting conditions
are very good tests of this model. Both conditions are
uncontrolled, self-paced, and may include dissimilar processes
between subjects. Assuming that EEG is sensitive enough to
measure functional differences between conditions, the fact that
replications of these conditions did not differ topographically
for four spectral parameters is evidence which supports the
macrostate assumption.
|
