The document details the components and processes involved in EEG equipment and signal acquisition, highlighting the challenges due to the small size of EEG signals and the need for amplification, filtering, and digitization. It outlines standard electrode placement systems, specifically the international 10-20 and 10-10 systems, and provides insight into the nature of EEG signals as time series, discussing concepts like sampling theory and noise sources. Additionally, it covers methods for reducing noise, such as using driven right-leg circuits and active electrodes to enhance signal quality.

1. EEG equipment and signal acquisition
Human Cognition and Neural Dynamics Lab
Western Washington University

2. EEG components
Amplifier and ADC (Analog to
USB converter
Digital Converter)
Active electrodes
Analog Response Analog Input Box Computer storage and
Device display

3. Basic Acquisition
• Signals on scalp are very small - microvolt range
(1/1,000,000 volts).
• Presents some challenges for acquisition
• Acquisition involves
– Amplification
– Filtering
– Digitizing (sampling)
– Storage
• Results in one time series per channel (64 in our lab).

4. Basic Acquisition
• EEG signals are a measure the potential difference
between two electrodes.
• Just like the voltage at a battery is the difference
between positive and negative poles.
• Thus you always need at least 2 recording electrodes
to get a signal.
• In practice we use many electrodes but each EEG
signal is always the difference between the signal
from 2 or more electrodes.

5. Electrode placement
• Typically adopt an accepted placement scheme for applying electrodes to
the scalp.
• The International 1020 placement system is the most widely adopted.
• It uses a set of measurements relative to landmarks on the head.
• Name reflects the fact that electrodes are placed at intervals that are 10%
or 20% of the distance between landmarks.

6. Electrode placement
• Requires distance from front
to back of head and distance
from left to right.
• Front to back defined as
distance from nasion to inion.
• Nasion - intersection of the
frontal bone and two nasal
bones
• Inion - the most prominent
projection of the occipital
bone at the posterioinferior
(lower rear) part of the skull

7. Electrode placement
• Requires distance from
front to back of head and
distance from left to
right.
• Left right defined as
distance between pre-
auricular points.
• Pre-auricular point- root
of the zygomatic arch
anterior to the tragus

8. Electrode placement
• Electrode placement begins at 10% from these landmarks.
• Electrodes are placed at 20% intervals.
• Allows for 19 recording electrodes
• Electrode names reflect location.
– Even number right/ odd left; z = midline
– C = central; F = frontal; P = parietal; T = temporal; O = occipital
– Larger numbers are farther from the midline
The 10-20
placement
system.

9. Electrode Placement
• Extensions of this placement
system include greater
numbers of electrodes.
• 10/10 electrode placement
places electrodes at 10%
intervals.
• 10/5 electrode placement
put electrodes at 5%
intervals.
• Most labs are using some
variant of this system and use
the associated electrode
names.
The 10-10 placement system

10. EEG as a time series
• EEG can be considered as a signal that changes over
time.
• A simple example is a sine wave that oscillates at a
single rate.
• Below are 3 sine waves oscillating at 8 times / sec
(Hz).

11. EEG as a time series
• Waveforms can also be represented in terms of amplitude
over frequency –
• And amplitude at different phases.
• Can transform data back and forth with no loss of
information.
phase frequency

12. EEG as a time series
• EEG is a more complex signal than a simple
sine wave
• In theory, any time series – no matter how
complex - can be decomposed into individual
sine waves of specific frequency and
amplitude.
• EEG can be treated in the same way

13. EEG as a time series
Amplitude x time Amplitude x frequency

14. EEG as a time series
• The EEG signal is
recorded together
with noise that
stems from a
number of sources.
• Essentially anything
that is not the
signal of interest is
considered noise.
• Noise amplitude is
usually larger than
the signal of
interest.

15. Sampling theory
• Digital recording of EEG requires sampling brain signals at
discrete time points.
• The sample interval (T) is the time between samples
expressed in seconds.
• The sample frequency or rate (fs) is the number of samples
collected each second expressed in hertz (Hz.)
• fs = 1/T; T = 1/fs
• fs(500 Hz) = T(.002 s)

16. Sampling Theory
• The sampling theory must be adequate for
representing the signal of interest.
• Too low results in aliasing
• Too high results in redundancy and unnecessarily
large data files.
• If you have to err – always choose to oversample
rather than undersample.
• You can always downsample later (lower the
sample rate of the digital signal) but you cannot
increase the sample rate of a digital signal.

17. Sampling Theory
8 Hz sine wave sampled at different rates

18. Sampling Theory
• Nyquist–Shannon sampling theorem:
– A signal with maximum frequency f can be
reconstructed using a minimum sampling rate of
1/(2f).
– Given a sampling rate fs, the highest frequency (f)
that can be represented is f = fs/2 also known as
the Nyquist frequency.
– In practice the sample rate is usually at least 4
times the highest frequency of interest.

19. Sampling Theory
• Data should not contain frequencies higher than the Nyquist.
• Results in aliasing: when a signal appears in the EEG as a
lower frequency
Actual signal (blue) = 20 Hz Undersampling results in aliasing at 2 Hz

20. Sampling Theory
• Filters must be set to reduce contribution of
signal above the Nyquist frequency.
– Sample Rate = 250 Hz
– Nyquist frequency = 125 Hz
– Must low pass filter at 125 Hz.
• High pass filter – allows high frequencies to pass
• Low pass filter – allows low frequencies to pass
• Notch filter – filters specific range of frequencies
• Band Pass – filters all but a range of frequencies

21. Digital Filtering
cutoff cutoff
frequency frequency
High Pass Filter LowPass Filter
Filtered frequencies
Filtered frequencies
amplitud
amplitud
e
low high e low high
fc frequency frequency fc

22. Digital Filtering
Band Pass Filter Notch Filter
amplitud
amplitud
Pass band
e
low fcLOW fcHIGH high e low fcLOW fcHIGH high
frequency frequency

23. Sources of Noise In EEG
• Capacitive coupling
– the electrodes and cables are coupled to signals such as lights,
computers, cell phones, etc. can induce voltage in the leads.
– Theoretically this is the same for all leads so should be removed by
common mode rejection.
– In practice, however, this is not always the case so it is best to keep
distance between leads and electrical sources.
• Induction
– Loop created between body and equipment allows for the formation
of a magnetic field that can induce current flow in wires.
– The best solution is to wrap the cables around each other so that
opposing magnetic fields will cancel each other.

24. Reducing Noise with Biosemi
• Driven Right Left Circuit
– Biosemi uses a driven right leg circuit to reduce common mode
signals.
– Uses two electrodes (CMS & DRL) in a feedback loop to drive the
voltage of the patient to be the same as the common mode voltage -
thereby reducing the effect of external noise.
– CMS used to detect to common mode signal or background noise
– DRL used as part of feedback circuit to eliminate difference between
participant and common mode.
– Other systems have only a single ground electrode that grounds the
participant for safety reasons.

25. Reducing Noise with Biosemi
• Active Electrodes
– Each electrode has an amplifier attached.
– Amplify recorded signals at the electrode where transduction is
occurring.
– Increases size of signal traveling down leads
– Reduces susceptibility to noise in the environment.