Monday, 12 November 2012

Kaiser QEEG


What is Quantitative EEG
David A. Kaiser, Ph.D.
Rochester Institute of Technology

What determines the characteristic rhythms of the EEG?
Moruzzi and Magoun (1949) were the first to shed light on the origin of EEG
rhythms. Working in cats they determined that stimulating the reticular formation of the brainstem aroused the animal behaviorally and any dispersed high-amplitude EEG rhythms at the cortex. This work eventually led to a general physiological model of rhythmic activity (Andersen & Andersson, 1968; Steriade et al. 1990). According to the model, isolated thalamocortical neurons fire rapidly at their own pace due to metabolic characteristics but in an intact brain, a sheath of cells known as the reticular thalamic nucleus (RTN) inhibits intrinsic or random firing of thalamocortical neurons and unites
individual discharges into simultaneous volleys. These volleys propagate to the cortex and synchronize pyramidal cell activity, whose synchronization can be detected at the scalp as high-amplitude oscillations (e.g., alpha bursts, sleep spindles). Corticothalamic feedback influences these volleys by inhibiting RTN's inhibitory action so that neuronal ensembles may break free of reticular thalamic influence and fire in response to specific processing demands. When this occurs, large slow waveforms (theta, alpha) are replaced
by faster frequencies of lower amplitude (beta, gamma), a process originally called alpha blocking and now called EEG desynchronization. Desynchronization may be localized to a single electrode as uncommitted cortical areas remain "idling" or synchronized, or it may involve several brain areas or electrodes (Pfurtscheller, 1992; Sterman et al. 1994). Regional patterns of simultaneous desynchronization and synchronization characterize
specific cognitive and behavioral states (Pfurtscheller & Klimesch, 1990) and it is bymeasuring the mix of slow and fast rhythms across the head that we identify the nature and extent of cortical engagement.

How is QEEG Related to Human Behavior?
EEG as a crude measure of mental state ( eg eyes open (attention, alert, aroused or eyes closed (inattention and inactivity) has been documented extensively and over a considerable period of time.

Behavioral and mental states such as mathematical processing, reading, or relaxed wakefulness are assumed to be distinct and uniform in nature, consisting of similar perceptual and cognitive operations whenever they occur. It is also assumed that distinct mental operations present distinct EEG and biochemical profiles which are reproduced reliably whenever a task or mental state occurs. These assumptions lay the foundation to functional MRI as well as EEG assessment and are the rationale for EEG normalization
training.

The most reliable finding in EEG research occurs when an individual
resting with eyes closed opens his or her eyes in a well-lit room: alpha blocking occurs. The alpha rhythm is replaced by fast low-amplitude waveforms, or beta rhythm. (When eyes are opened in a dark room, alpha blocking does not generally occur; Bohdanecky et al., 1984.) The degree and localization of blocking or desynchronization is associated with stimulus intensity, complexity, novelty, and meaningfulness (Gale & Edwards,1983; Berlyne & McDonnell, 1965; Baker & Franken, 1967; Boiten, Sergeant, & Geuze,1992; Gevins & Schaffer, 1980). Topographic analysis reveals whether EEGdesynchronization is nonspecific (many or all sites) or selective (few sites). Nonspecific arousal is modulated by drugs, drowsiness, drive, and time of day, whereas sensory and strategic demands activate specific brain areas such as parietal and occipital cortex to visual stimulation and temporal cortex to acoustic stimulation (e.g., Grillon & Buchsbaum, 1986; Pfurtscheller, Maresh, & Schuy, 1977; Chapotot, Jouny, Muzet,
Buguet, & Brandenberger, 2000).

Table 4. Cortical gyrus below each electrode position, based on Mokotoma et al

LOBE
GYRUS
BRODMANN AREA
SITE (left/right)
Frontal
Superior
10
Fp1/2

Inferior
47
F7/8

Medial
9
F3/4

Medial
8
Fz

Precentral
6
C3/4

Superior
6
Cz
Temporal
Medial
21
T3/4

Medial
37
T5/6
Parietal
Inferior
7
P3/4

Precuneus
7
Pz
Occipital
Medial
19
O1/2

Figure 3. International 10-20 system for electrode placement on the scalp.

Coherence and Co modulation
Coherence analysis
quantifies phase consistency between signals and comodulation analysis quantifies
magnitude consistency (Goodman, 1957; Kaiser, 1994). Two signals are said to be
coherent when their phase relationship is stable, even if signals are entirely out of phase
with each other. Two signals are said to comodulate when their magnitude relationship is
stable, regardless of absolute difference between signals.

Development before an adult rhythm at 10 Hz is established (Niedermeyer, 1987).
The alpha rhythm emerges as a slow 3-4 Hz rhythm in infancy, and it takes a decade

Table 5. Rhythm Maturation: Alpha & Sleep Spindle Frequency Range by Age Group
(modified from Niedermeyer, 1987)

Rhythm
Newborn
Infant
Toddler
Preschooler
Preteen
Alpha
Not present
4-6
5-8
7-9
9-10
Sleep spindle
Not present
12-14
12-14
12-14
12-14

 BooksPfurtscheller G, & Lopes da Silva FH. (1999). Event-related EEG/MEG synchronization and
desynchronization: basic principles. Clinical Neurophysiology, 110, 1842-57.

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Endnotes:

Thursday, 8 November 2012

Antonenko & Niederhauser


Antonenko, P.D., and Niederhauser, D.S. (2010)
The influence of cognitive load and learning in a hypertext environment
Computers in Human Behavior

Participants
Researchers have suggested that gender, handedness, and age can differentially affect brain wave activity (Andreassi, 2007; Fisch, 1999). Further,
EEG patterns can be influenced by brain disorders, or medications to treat brain disorder conditions (Andreassi, 2007). Thus, the subject pool was limited to right-handed, 18–23 year-old females with no known brain disorders.

Materials
 descriptions of 4 different learning theories (500 words + or – 5) . 7 hypertext links at each node (90 + or – 5 words). identical grammatical and syntactical structures were used across all texts (e.g., sentence length, number of clauses, stylistic devices). Also checked for readability and matched conceptual difficulty.

The hypertext presentation system was designed using guidelines
suggested by current web usability research (Nielsen, 2006).
Nodes were presented as a single frame using Georgia serif font,
with black lettering on a white background and no tracing images
or watermarks. Font size was set to 100 percent of the default
browser font size, which translated to approximately 16 point font
on the monitor used for the study.

EEG data
Electroencephalogram. EEG data were acquired using a Biopac
MP30 connected to a Macintosh G4 MiniMac computer. Electrode
placement was confined to one set of electrodes over the
pre-frontal cortex (F7) and one-over the parietal lobe (P3) in the
left hemisphere of the right-handed subjects. EEG data were collected
at a sampling rate of 500 Hz as each subject read each of
the four hypertexts. Electrode placement followed the Modified
Combinatorial Nomenclature expanded 10–20 system, as proposed
by the American Clinical Neurophysiology Society (Jasper, 1958).
The EEG software recorded brain wave rhythms as separate channels,
allowing identification of the following wave components: (a)
raw EEG signal, (b) alpha rhythm, (c) beta rhythm, and (d) theta
rhythm.

Event-Related Desynchronization percentage (ERD%) for alpha,
beta, and theta rhythms were used as online measures of brain
activity (Pfurtscheller & Lopes de Silva, 1999). Increased cognitive
load is associated with higher brain wave desynchronization for alpha
and beta rhythms, and higher brain wave synchronization for
the theta rhythm, when subjects move from a relaxed, eyes-closed
state (baseline) to an eyes-open, active-reading state (Basar, 2004;
Klimesch, 2005). Consequently, ERD%, which compares brain wave
power in the test condition with the brain wave power in the baseline
condition, is represented by a positive number for the subjects’
alpha and beta rhythms (reflecting wave desynchronization), and a
negative number for the theta rhythm (reflecting synchronization).
Thus, larger desynchronization percent values for alpha and beta
waves, and larger synchronization percent values for theta waves
indicate increased cognitive load.
The following formula was used to compute ERD% (Pfurtscheller
& Lopes de Silva, 1999):
ERD% =baseline interval band power-test interval band power
baseline interval band power x 100

Band power values of the subject’s alpha, beta, and theta brain
waves were estimated with the Biopac psychophysiometrical system
software. The Area function was used to calculate alpha, beta,
and theta wave power. This function computes the total area of
waveform under a straight line drawn between the endpoints.
Thus, this measure takes into account both wave frequency (Hz)
and amplitude (microV). Area under the curve for alpha, beta, and theta
brain wave rhythms was computed for a 20-s segment of the baseline
condition, which was obtained before a given subject started
reading each of the four texts. This yielded a unique baseline interval
band power of brain activity for the time period immediately
preceding reading each text that was used in the ERD% formula.
Area was then computed for the test interval band power. Using
the marker placed on the EEG recording as a referent, a 20-s test interval was established which included the 10 s spent reading the
primary passage immediately before selection of given link to a subordinate
node, and the 10 s spent reading immediately after clicking
the link to the subordinate node. Test interval band power was calculated
using this 20-s interval because subjects read and processed
lead information before they clicked on the link to open the subordinate
node in the lead-augmented hypertext condition. ERD% values
were computed for alpha, beta, and theta brain wave rhythms
on each of the seven links within each of the four hypertexts.
Average ERD% value was then computed for each of the three
brain wave rhythms under each of the two experimental conditions.
For example, the ERD% values for the 20-s intervals for the
alpha rhythm were averaged for each subject, yielding one alpha
ERD% value for each text. The alpha ERD% values for the two nolead
texts were then averaged, as were the alpha ERD% values for
the two lead-augmented texts. This yielded one grand alpha
ERD% value for the lead-augmented hypertext condition, and a second
alpha ERD% value for the no-lead condition. This procedure
was then used to compute grand averages for lead and no-lead
conditions for beta and theta rhythms. Since cognitive load is reflected
by more negative ERD% for the theta rhythm (Klimesch,
2005), and more positive ERD% for alpha and beta rhythms, we
used absolute values for measures of theta waves to help make
interpretation of results more intuitive. Grand average ERD% values
for alpha, beta, and theta rhythms served as dependent measures.

Procedure
Each sat at a table with an adjustable-height high-back chair with armrests.
Subjects faced a blank white wall, which extended beyond
peripheral vision on both sides. A full-size 110-key keyboard, wireless
mouse, and 17-inch LCD monitor were on the table in front of
the subject. The same computer and monitor were used for all
subjects.

The monitor surface was approximately 50 cm from the subject, and was set to 1280 by 854 pixels (the highest available resolution) and maximum level of brightness. Luminescence was measured at eye-level and at a distance of approximately 50 cm from the computer screen using a Tenma Digital Lux meter. Luminosity of hypertext displayed on the LCD monitor was equal across text conditions.

With the subject comfortably seated at the computer, the researcher
read a scripted verbal overview of treatment procedures to begin the session. He then attached disposable vinyl electrodes (Ag/AgCl) to two recording sites on the subject’s skull: (a) pre-frontal lobe (F7) to collect data on the power of beta and theta waves and (b) parietal lobe (P3) to collect data on the power of alpha waves. Measurements were referenced to the left mastoid with
the earlobe serving as ground. Electrode impedance was below
10 kX. The EEG signal passed through an Infinite Impulse Response
(IIR) bandpass filter to remove unintended artifacts of movement,
allowing us to retain only the frequency components that were
of interest in the present study: theta (4–7 Hz), alpha (8–13 Hz),
and beta (14–30 Hz). Two more sets of electrodes were attached
to collect the subject’s electrooculogram and electromyogram of
the dominant (right) hand, which was used to filter artifacts associated
with eye movement, blinking, hand movement and mouse clicking. Subjects were instructed to minimize unnecessary movement
during the hypertext reading and browsing task.

After all electrodes were placed and the EEG equipment was
activated, the subject sat in a relaxed state with her eyes closed until
an extended alpha pattern was noted. At that point the 20-s baseline brain wave rhythm sample was recorded and the subject was instructed to open her eyes (blocking the alpha rhythm) and read the first experimental hypertext. When the subject had finished reading she said ‘‘done,” completed a self-report of mental effort measure, and closed her eyes and relaxed until her brainwave patterns returned to the baseline condition (extended alpha).
Returning to baseline helped circumvent carryover effects from one treatment to the next.

The researcher brought up the next experimental text while the
subject was returning to baseline. After establishing and recording
the next baseline brain wave sample, the subject was instructed to open her eyes and read the second text, complete the mental effort
scale, and close her eyes until she returned to the baseline condition.
This procedure was repeated for the remaining two texts. The sequence of presenting texts to subjects was counterbalanced. When a subject had finished reading the fourth hypertext, she was instructed to close her eyes to return to an alpha state, which provided an end point for the brain wave
recording.

Analysis
A 2 x 5 repeated measures MANOVA was conducted to determine
the effect of leads on learners’ cognitive load. Presence of
leads (Lead vs. No-lead) served as a within-subject factor, and
the five measures of cognitive load: (a) reading time; (b) self-reported
mental effort; and Event-Related Desynchronization percentages
of (c) alpha; (d) beta, and (e) theta brain wave rhythms were used as dependent measures.

Results
The MANOVA for cognitive load measures was significant
(F(5,12) = 58.94, p < 0.01), prompting further analysis through a series
of univariate repeated measure ANOVAs. For each of the ensuing
ANOVAs, presence of leads served as the independent variable,
with each of the cognitive load measures serving as a dependent
measure. A main effect was found for reading time (F(1,16) = 5.55,
p < 0.05, MSE = 427.63). Subjects spent more time reading in the
lead condition than in the no-lead condition (X = 587.23,
SD = 116.06 and X = 571.83, SD = 126.20 s, respectively). Main effects
were also found for alpha, beta, and theta ERD%
(F(1,16) = 103.47, p < 0.01, MSE = 7.12; F(1,16) = 35.71, p < 0.01,
MSE = 15.34; F(1,16) = 252.56, p < 0.01, MSE = 1.03, respectively). Table
1 shows that mean alpha, beta, and absolute value of theta
ERD% in the no-lead condition was higher than in the lead condition.
These findings reveal lower cognitive load in the Lead condition.
No other results reached significance (p > 0.08).

Discussion
The aim of this study was to determine the influence of leads on
cognitive load and learning in hypertext. The study produced several
important findings. First, EEG-based cognitive load measures
showed that subjects’ brain wave activity was less intense when
they were accessing hypertext nodes via leads. Conversely, the
self-report of mental effort measure did not detect significant differences
in cognitive load between the two experimental conditions,
and subjects tended to spend more time reading lead augmented
hypertext. Second, use of leads appeared to produce a
positive effect on learning outcomes relative to domain and structural
knowledge acquisition.

The discrepancy between the results of EEG-based measures of
cognitive load, self-report of mental effort,  ie EEG refelects what goes on in real time whereas self report is retrospective