M.Tech. (CS), Semester III, Course B50
Functional Brain Signal
Processing: EEG & fMRI
Lesson 4
Kaushik Majumdar
Indian Statistical Institute
Bangalore Center
kmajumdar@isibang.ac.in
Delta Band EEG
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0 – 4 Hz. Originates in frontal cortex,
hippocampus and thalamus.
Associated with slow wave sleep.
Associated with declarative memory
consolidation.
Implicated in attention during performance of
a complicated mental task like artihmatic
calculation.
Theta Band EEG
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4 – 8 Hz. Associated with Rapid Eye
Movement (REM) sleep.
Associated the mnemonic processes in our
brain, where step by step detail of a process
up to the sequential details has to be taken
care of.
Implicated in working memory.
Implicated in long-term potentiation (LTP)
leading to facilitation of long-term memory
and learning.
Alpha Band EEG
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8 – 12 Hz. Magnitude increases when eyes
closed. Diminishes when eyes are open.
Alpha power often inversely varies as theta
power in healthy brain. This indicates good
cognitive ability.
Alpha power is positively correlated with
brain maturity.
Alpha power is positively correlated with
good memory performance and speed of
information processing.
en.wikipedia.org
Mu Band EEG
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8 – 12 Hz. Originated from motor cortex and
morphology is distinct from alpha.
It selectively augments and diminishes
during motor related task demands.
Important for Brain Computer Interface
implementation.
Beta Band EEG
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12 – 30 Hz. Associated with waking stage in
general and cognitive and emotional
processes in particular.
Hyper beta activity at the sleep onset has
been associated with insomnia.
http://people.ece.cornell.edu/land/courses/ece4760/FinalProjects/s2012/cwm55/cwm55_
mj294/
Different EEG Bands
Gamma Band EEG
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30 – 80 Hz. 40 Hz is considered
representative (taken as 37 – 43 Hz).
Implicated in sensory information processing.
It has been implicated in formation of
memory, linguistic processing, internal
thoughts (apparently without any outside
stimuli) and behavior, particularly motor
actions and planning for the actions. It has
also been implicated in attention, arousal and
object recognition.
http://en.wikipedia.org/wiki/Event-related_potential
Event Related Potential (ERP)
http://cognitrn.psych.indiana.edu/busey/temp/eeglabtutorial4.301/maintut/data_averaging.html
ERP: Definition

When the scalp potential is evoked in
response to an event or stimulus
presentation in the environment the evoked
potential is called event related potential
(ERP). Of course in scalp EEG ERP may be
embedded in artifacts and neuronal activities
not evoked by the event of interest.
motorbehaviour.wordpress.com
Single Trial and Trial-Averaged
ERP
Time-Frequency Analysis

Short-time Fourier transform.
Welch Power Spectral Density Estimate
70
14000
65
12000
60
Power/frequency (dB/rad/sample)
Unsmoothed power spectrum
10000
Absolute value of FFT
on 100 time points
8000
6000
4000
55
Epileptic ECoG of 500
time points
50
45
40
35
30
2000
25
0
0
5
10
15
20
25
Time
30
35
40
45
50
20
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Normalized Frequency ( rad/sample)
0.8
0.9
1
Event Related Spectral
Perturbation (ERSP)

ERSP measures average dynamic changes
in amplitude in the broad-band EEG
frequency spectrum as a function of time.
That is, the ERSP measure the average time
course of relative changes in the
spontaneous EEG amplitude spectrum
induce by a set of external events.
Plot generated by Pradeep D. Prasad with EEGLAB
ERSP
ERSP Computation
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Choose a sliding temporal window with
suitable size and overlap for the selected
EEG epoch.
Do FFT in the window and determine the
power spectral density for a frequency bin
size of Fn (1 Hz or slightly larger).
Smooth by a moving average window of
suitable size in order to eliminate the random
jitters.
ERSP Computation (cont)

All smooth window-wise spectral estimates
obtained by 2 and 3 are to be normalized by
dividing by the spectral estimate of the first
window (EEG epoch to be selected such a
way prior to 1 that this first estimate will
correspond to a pre-stimulus or baseline
spectrum).
Measure of ERP Amplitude
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Peak amplitude measure.
Mean amplitude measure.
Peak Detection
s(m)  s(m  1)  0
s(m  1)  s(m)  0
B
( m, s ( m))
D
F
s' ' (m)  s(m  1)  s(m)  ( s(m)  s(m  1))  0
P(m)  s' ' (m)( s(m)  s(m  1))  0
A
(m  1, s (m  1))
C
P(m)  s' ' (m)( s (m  1)  s (m))  0
(m  1, s (m  1))
E
P ( m  )  0 & P ( m  )  0 & s ' ' ( m)  0
ERP Latency
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Peak latency.
Fractional area latency.
Peak Latency
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1.
2.
Latency at which the peak is occurring. Has
to be measured with following cautions:
Filter out the high-frequency noise in the
EEG.
Rather than taking the maximum peak
alone, take other local peaks also (possibly
with some threshold), because the
maximum peak may not always be due to
an ERP waveform.
Peak Latency (cont.)
3. When different waveforms are compared
they must have similar noise level.
Fractional Area Latency
References
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Majumdar, A brief survey of quantitative EEG
(under preparation), Chapters 1 and 4, 2013.
S. J. Luck, An Introduction to Event-Related
Potential Technique, MIT Press, Cambridge,
Massachusetts, 2005.
THANK YOU
This lecture is available at http://www.isibang.ac.in/~kaushik