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148 T. Warbrick and C. Jaeger
For all bipolar muscle electrodes, a ground electrode is needed to account for
external electrical noise. The purpose of the ground electrode is covered in Sect.
12.2.3. The ground electrode should be placed on a bony protrusion or cartilage with
al electrical activity.
minim
12.2.2.2 Peripheral Physiological Sensors
Data from other types of sensors can also be recorded alongside EEG that can
provi
de additional information on physiological responses. These sensors often
utilize different recording principles and require a conversion of the signal to an
electrical signal such that the peripheral signal can be measured alongside the EEG
recordings.
12.2.2.3 GSR
A sensor that utilizes electrical conductance modulated by physiological responses is
the
GSR sensor which measures galvanic skin response (GSR) or electrodermal
activity. The GSR sensor measures the skin’s electrical conductance, which is
modulated by sweat gland activity and the electrolyte concentration within sweat.
The conductance of skin increases during sweating. The sweat glands are regulated
by the autonomic nervous system, which was described in Chap. 4: Basic Anatomy:
eral Nervous System. The GSR sensors can measure skin conductance levels,
Periph
which is the baseline of skin conductance established over time. It can also measure
rapid skin conductance changes due to fear- or stress-induced stimuli, which is
known as the skin conductance response (Boucsein et al., 2012). A GSR sensor
sts of a pair of electrodes, placed on the middle phalanx of the pointer and
consi
middle finger (Fig. 12.3d). A small constant voltage is applied across the two
electrodes
to establish skin conductance, which changes over time or due to an
external stimulus. The electrical conductance is then converted to voltages and
recorded by an amplifier.
12.2.2.4 Respiration
ration belt can be used to monitor changes in breathing rates, which is also
A respi
associated with autonomic nervous system functions (Liu et al.,
tion
belt is a pneumatic sensor that measures pressure changes that correspond to the
2017). The respira-
rhythmic expansion and contraction of the chest during breathing. During inhalation,
the chest expands, and a positive flow of air induces pressure on the pneumatic
sensor. During exhalation, the chest contracts and air is expelled from the lungs,
which results in a negative airflow or vacuum within the respiration belt. A transducer is also required to convert pressure changes into an electrical signal. The
respiration belt can measure both breathing rate and amplitude of respiration,
correlating to the amount of airflow (Stern et al. (
2001).
12 Hardware for Recording EEG and Peripheral Physiology 149
12.2.2.5 Photoplethysmography (PPG)
PPG is often used to detect systemic changes to heart rate, blood pressure, and
vascul
ar tone. These peripheral responses are under autonomic nervous system
control and often correlate with physiological measurements linked to homeostasis
and stress responses (Allen, 2007). PPG is a volumetric measurement that detects
local
perfusion changes in oxygenated hemoglobin within the blood by using
infrared light. Infrared light is absorbed differently by oxygenated and deoxygenated
hemoglobin and thus can be used to measure blood oxygenation changes linked to
the cardiac cycle and blood pressure changes. The sensor is placed on a finger. One
side of the sensor emits infrared light through the finger. The blood vessels within
the finger then absorb the infrared light proportionally to the amount of oxygen
saturation within the blood. A photo diode on the opposite side of the finger then
records the amount of unabsorbed infrared light. Over time the PPG signal shows
fluctuations in oxygen levels within the blood. In combination with ECG recordings,
the PPG sensor is a useful tool for measuring heart rate variability linked to different
physiological conditions.
A combination of peripheral measurements alongside EEG recordings can be
to help characterize behavior linked to simultaneous neurophysiological
useful
changes recorded with EEG.
12.2.3 Measuring the Signal: Amplifiers
There are some basic concepts and parameters associated with EEG amplifiers that
influence the recorded data. We will cover the main features of an EEG amplifier to
describe how your signal is recorded.
The role of the amplifier is to amplify the signal, convert it from an analog to a
signal, and transfer it to a recording device. EEG is usually recorded using a
digital
differential amplifier; the measured signal is the difference between two electrodes.
In the case of EEG, this is usually a reference electrode and the EEG signal electrode
(see Fig.
bipola
between pairs of recording electrodes (see Fig. 12.4). We will focus on EEG signals
in
common mode rejection (CMR). The term common mode refers to identical signals
that appear in phase in all electrodes (e.g., electrical mains noise). Common mode
rejection refers to the attenuation of common mode noise. The ground electrode is
connected to the ground circuit of the amplifier and picks up common-mode
electrical noise. This noise is subtracted from the reference and signal EEG channels.
Rather than measuring a direct difference between the reference and EEG electrodes,
the difference between (EEG—ground) and (reference—ground) is measured.
12.4). For peripheral physiology such as ECG and EMG (Sect. 12.2.2) a
r amplifier is usually used, where each recorded signal is the difference
this section, but the same principles apply to peripheral physiology measurements.
The ground
electrode plays an important role in EEG recording, specifi cally, in
150 T. Warbrick and C. Jaeger
Fig. 12.4 Referential and bipolar recording schemes for EEG and peripheral physiology. (Part a)
Shows a referential recording scheme where the signal at each EEG electrode is recorded relative to
a reference electrode. Note that the distance differs between the reference electrode and each
recording electrode, this will influence the signal recorded. The position of the reference electrode
relative to the EEG electrodes is therefore important. (Part b) Shows a bipolar recording scheme for
EMG. The position of the recording electrodes relative to each other (rather than to a refence
electrode as in part a) will influence the recorded signal
Amplifier input imp edance also plays a role in noise suppression. Amplifier input
impedance and electrode impedance (Sect. 12.2.1) should not be confused. For
electrode impedance we want the signal to pass through, so a low impedance is
preferred. For amplifier input impedance we want to measure the signal, so a high
impedance is required. It may seem counterintuitive to want a low impedance in one
place and high impedance in another, but we have different goals for the signal at the
electrode and at the amplifier. The ratio of electrode impedance and amplifier
impedance is important (Shad et al.,
2020). The EEG signal is small therefore it’s
important to retain as much of the signal as possible and to minimize the effect of
noise. Modern ampli fiers have very high input impedance; this means that higher
electrode impedance can be tolerated than with early EEG systems. However, it is
worth remembering that not all types of noise are attenuated by the high input
impedance and low electrode impedance is generally recommended. For a detailed
explanation of amplifier impedance and its relation to electrode impedance please
see Jackson and Bolger (
2014).
Once the signal reaches the amplifier it passes through hardware filters to remove
frequencies outside of the desired range. The signal is then passed to the analog to
digital converter (ADC). An analog signal is perfectly resolved in time, but a digital
12 Hardware for Recording EEG and Peripheral Physiology 151
signal is a sampled signa l. How often and how many of those digital samples are
taken is referred to as the sampling rate. This determines how true to the original
analog signal the digital signal is and determines the temporal resolution (Fig.
12.5).
The analog signal also gets sampled at regular amplitude intervals (see Fig. 12.6).
The voltage intervals at which the continuous signal is sampled (or digitized) are
defined by the bit depth and amplitude resolution of the ADC. The bit depth
determines how many possible digital values a signal can have, and the amplitude
resolution determines the size of steps between values. The more possible values, the
more fine-grained the differentiation between voltage steps and the closer to the
original analog signal the digital signal will be (Fig.
12.6). The bit depth and the
amplitude resolution determine another important parameter: measurement range.
This is the maximum and minimum voltage values that can be recorded. For
example, a 24-bit amplifier has 2
24
(16,777,216) possible steps. With a resolution
of 0.0487 μV/bit, the measurement range is 409.6 mV.
At this point you might be
thinking that we should always use the highest
sampling rate and highest amplitude resolution possible. But at some point, higher
Fig. 12.5 Analog to digital conversion: temporal resolution. An analog signal perfectly resolved in
time is shown in the left panel. A digital signal is a sampled signal, obtained by sampling the analog
signal at discrete points in time. The higher the sampling rate, the better the time resolution. The
middle panel shows a signal sampled every 0.4 s and the right panel shows a signal sampled every
0.2 s. The signal in the right panel is closer to the original analog signal than the digital
Fig. 12.6 Analog to digital conversion: amplitude resolution. A continuous signal gets recorded (&
amplified) at regular amplitude intervals. The higher the bits per volt, the higher the amplitude
resolution. The left panel shows the original analog signal. The middle panel shows a signal
sampled with an amplitude of 0.4. The right panel shows a signal sampled with an amplitude of
0.2. The signal in the right panel has a more fine-grained resolution and is closer to the original
analog signal
152 T. Warbrick and C. Jaeger
resolution doesn’t bring much return with respect to the signal and the consequences
of going higher might not be acceptable: increases in file size and decreases in
processing speed. Consequently, we don’t want to push temporal and amplitude
resolution as high as possible. But what is enough and what is too low?
It might help to consider the consequences of too low resolution. If our sampling
rate is too low, we can potentially lose useful information and we run the risk of
aliasing. Aliasing is when different signals become indistinguishable when sampled
at an insufficient sampling rate. You can see a signal in the data that isn’t real, it’s an
alias. To prevent aliasing, we can implement anti-aliasing filters. This is done on the
hardware level and is often a fixed feature of the hardware. Even if we don’t choose
our antialiasing filters, we do need to choose a sampling rate and for this we need to
consider the Nyquist frequency, or folding frequency. This is the theoretical maximum frequency that can be recorded without aliasing artifacts. For a given sampling
rate, up to half of that frequency can still be represented in the recording, e.g., for a
sampling rate of 1000 Hz, you could expect usable data up to 500 Hz. In practice,
anti-aliasing filters are often considerably lower than the Nyquist frequency and it is
recommended that data are sampled at 5–10 times the frequency of interest
(Weiergraeber et al.,
If our amplitude resolution is too low, we run the risk of saturation, or clipping, in
the
data. In other words, the amplitude of the signal exceeds the measurement range
of the amplifier. For most applications a standard EEG amplifier offers a sufficient
measurement range. However, if you are working in an application where very high
amplitudes are expected, for example, scanner related artifacts in the MR environment or during TMS stimul ation, you need to be aware of the measurement range of
your amplifier. In both cases you want to record the full range of the artifacts and
these are much larger in amplitude than EEG, therefore you need to make sure your
amplifier is capable of the required measurement range.
Amplifier technology has progressed in recent decades, and it is now possible to
record
EEG in many different environments. In addition to standard, stationary,
laboratory ampli fiers, small mobile amplifiers and amplifiers integrated into electrode headsets are available. This means you can record high-quality data in realworld settings outside of the laboratory. It is also possible to combine EEG with
other measurement (e.g., fMRI) or stimulation (e.g., TMS) modalities, making
multimodal recording possible. These developments are significant in terms of
what EEG can be used for. Parts V and VI of this book cover EEG applications
and multimodal recordings, respectively, and showcase what applications benefit
from advanced EEG amplifier technology.
2016).
12.3 Conclusion
We’ve introduced EEG electrodes, periph eral physiology sensors, and EEG amplifiers. We’ve also covered the main features and explained how they affect your data.
It is important to keep in mind the aims of your study, the study population, and the
12 Hardware for Recording EEG and Peripheral Physiology 153
environment you will measure in when deciding what equipment to use. You should
also consider your planned data analysis when planning your measurement setup
because the recording decisions you make will influence the data you will analyze
later. The information in this chapter will help you to make informed decisions about
electrodes, sensors, and amplifiers that are suitable for your application and study
population.
References
Allen, J. (2007). Photoplethysmography and its application in clinical physiological measurement.
Physiological Measurement, 28(3), R1–R39. https://doi.org/10.1088/0967-3334/28/3/R01
Barold, S. S. (2003). Willem Einthoven and the birth of clinical electrocardiography a hundred
years ago. Cardiac Electrophysiology Review, 7(1), 99–104.
a:1023667812925
Boucsein, W., Fowles, D. C., Grimnes, S., Ben-Shakhar, G., Roth, W. T., Dawson, M. E., Filion,
D.
L., & Society for Psychophysiological Research Ad Hoc Committee on Electrodermal
Measures. (2012). Publication recommendations for electrodermal measurements. Psychophys-
iology, 49(8), 1017 – 1034.
Ferree, T. C., Luu, P., Russell, G. S., & Tucker, D. M. (2001). Scalp electrode impedance, infection
and EEG data quality. Clinical Neurophysiology, 112, 536–544.
risk,
Jackson, A. F., & Bolger, D. J. (2014). The neurophysiological bases of EEG and EEG measure-
ment:
Jasper, H. H. (1958). The ten twenty electrode system of the international federation. Electroen-
Keil, A., Debener, S., Gratton, G., Junghofer, M., Kappenman, E. S., Luck, S. J., Luu, P., Miller,
Laszlo, S., Ruiz-Blondet, M., Khalifian, N., Chu, F., & Jin, Z. (2014). A direct comparison of active
Liu, T. T., Nalci, A., & Falahspour, M. (2017). The global signal in fMRI: Nuisance or information?
López, A., Ferrero, F. J., Llopis, M. V., & Campo, J. C. (2022). Reference electrode placement in
Oostenveld, R., & Praamstra, P. (2001).
Shad, E., Molinas, M., & Ytterdal, T. (2020). Impedance and noise of passive and active dry EEG
Stern, R.M., Ray, W.J. And Quigley, K.S. (2001). Respiratory system. In: Psychophysiological
Weiergräber, M., Papazoglou, A.,
Zaheer, F.,
A review for the rest of us. Psychophysiology, 51, 1061–1071.
cephalography
G.
A., & Yee, C. M. (2014). Committee report: Publication guidelines and recommendations for
studies using electroencephalography and magnetoencephalography. Psychophysiology, 51,
1–21.
passive amplification electrodes in the same amplifier system. Journal of Neuroscience
and
Methods, 235, 298–307.
NeuroImage,
EOG-based
and applications (MeMeA) (pp. 1–5). Messina, Italy. https://doi.org/10.1109/MeMeA54994.
2022.9856469
and ERP measurements. Clinical Neurophysiology, 112, 713–719.
electrodes:
recording.
and related pitfalls in EEG analysis. Journal of Neuroscience Methods, 268, 53– 55.
decomposition. Journal of Neuroscience Methods, 207(2), 204–212. https://doi.org/10.1016/j.
jneumeth.2012.03.017
and Clinical Neurophysiology, 10, 371–375.
150, 213–229. https://doi.org/10.1016/j.neuroimage.2017.02.036
systems design. In 2022 IEEE international symposium on medical measurements
A review. IEEE Sensors Journal, PP(99), 1–1.
2nd ed.: Oxford University Press, pp.142–156.
De Luca, C. J., & Roy, S. H. (2012). Preferred sensor sites for surface EMG signal
https://doi.org/10.1111/j.1469-8986.2012.01384.x
The five percent electrode system for high-resolution EEG
Broich, K., & Müller, R. (2016). Sampling rate, signal bandwidth
https://doi.org/10.1023/
Chapter 13
Software for Recording EEG and
Peripheral Physiology
Tracy Warbrick and David Kadlec
Abstract In addition to hardware considerations, the choices made in the recording
softwar
e will influence the quality of the acquired data. This chapter covers the main
features of EEG recording software, important recording parameters, monitoring and
recording your data effectively, and troubleshooting tips for common recording
software problems.
Keywords EEG data format · EEG recording parameters · EEG software
figuration · EEG data monitoring
con
13.1 Purpose and Features
The main purpose of your recording software is to create a stored version of your
data. After analog to digital conversion, the data are transferred to the recording
device, which may be a desktop PC, laptop, tablet, or mobile phone.
Data Format EEG and peripheral physiology data are typically stored in binary or
format. Other relevant information, such as recording parameters, metadata, and
text
markers, can be stored alongside the EEG data. Because raw data and metadata
formats vary across the EEG community, the specific structure of the saved data
depends on the software and settings. To encourage standardisation of data storage
and description across neuroimaging studies, the Brain Imaging Data Structure
(BIDS) data format was implemented. BIDS facilitates sharing and reusing data,
the application of automatic pipelines, and using quality assurance protocols. It was
initially implemented for MRI Data (Gorgolewski et al.,
EEG
data (Pernet et al., 2019). The EEG-BIDS specification recommends that one of
two
data formats be used: The European Data Format (EDF) (an ongoing international effort to standardise EEG data format), and the BrainVision Core Data Format
(developed by Brain Products GmbH ) (Pernet et al.,
T. Warbrick (*) · D. Kadlec
Brain Products GmbH, Gilching, Germany
e-mail:
tracy.warbrick@brainproducts.com; david.kadlec@brainproducts.com
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026
T.
Warbrick (ed.), The EEG Handbook,
https://doi.org/10.1007/978-3-032-20450-9_13
2016) and later extended to
2019). It is also recommended
155
156 T. Warbrick and D. Kadlec
that additional metadata extracted from the manufacturer-specific data files are
stored in the ‘sidecar JSON’ file. Further details can be found in Pernet et al.
(
2019). Researchers are encouraged to use the BIDS format; it will bring order and
structure
to your data, and you can play your part in standardising EEG data formats.
Data Handling Once recorded, the EEG can be data stored locally or in cloudbased storage systems. Any personal data stored in the cloud must comply with
GDPR rules and it’s the researcher’s responsibility to know how their data are
handled. Also consider whether the raw data can be accessed or some processing
is applied before it’s displayed. Generally, it’s preferable to access the raw data so
you can asses s the quality and apply your own processing pipeline. If the data are
processed before you receive them, it’ s crucial to know what specific steps have been
applied.
Basic Features In addition to recording data, most EEG software includes impedance
measurement and data monitoring. Impedance measurement allows you to
establish good contact between the electrode and the scalp. Chapter
ts of EEG Data Acquisition provides advice on improving impedance. Note
Aspec
16, Practical
that some systems don’t include impedance measurements and, in this case, you
should rely on signal quality to decide whether to proceed with measurements. Data
monitoring allows you to check the signal before starting the recording and to
observe signal quality during the recording.
Advanced Features Some software packages offer advanced features that can
enhance
your experimental setup. For example, experimental control, synchronised
video recording, real-time access to the data, or Lab Streaming Layer (LSL) connection. Real-time access to the data and a straightforward LSL connection can be
helpful when you need to merge data streams from different systems. Not every
system is equipped with interfaces for sharing event markers or dedicated time
stamps or for registering these in the data stream. If this functionality is important
to your study, verify that the system supports LSL for synchronising signals and
event information.
Open Source Versus Proprietary Software EEG software can be open source or
etary; each has advantages and limitations, and their suitability depends on the
propri
circumstances.
Open-source
software (OSS) is released under a license that allows personal or
commercial use. Users have access to the source code and can modify it and
redistribute the original and/or modified versions, as OSS is typically categorised
as free software. However , be aware that not all free software is open source. In the
neuroscience community, OSS is usually developed and maintained by scientists
and academic institutions. It is developed to meet a scientific need, rather than a
commercial requirement. While the concept of OSS is positive and increasingly
relied upon in neuroscience (Poldrack et al.,
2019),
maintenance and support for
open-source packages are uncertain (Westner, 2024). Users should also verify that
OSS is standalone, or whether it requires another programme to run. For
free
example, some free EEG software requires MATLAB, which is a commercial
software that requires a license.
13 Software for Recording EEG and Peripheral Physiology 157
Proprietary, or commercial, software is protected by legal measures that restrict
its use, distribution, and modification. There will be an End-user License Agreement
(EULA) or Terms of Service (TOS) that outlines the terms and conditions under
which the software can be used and accessed. End users cannot access the source
code of commercial software, and it remains the intellec tual property of the company. Commercial software development is typically managed by a team of developers who take care of modifications, upgrades, and bug fixes. The software provider
is also responsible for ensuring that the implemented processes are correct. In
addition to the customer support that usually comes with commercial software,
this maintenance and accountability can justify the purchase price.
Operating Systems It is also necessary to consider the operating system on which
the software will run, and whether that is compatible with practices in your lab
(e.g. Windows Operating System, Linux, Apple’s macOS, Android, or iOS). It is
sometimes possible to emulate the required operating system within another operating system, but this can influence the functionality of the software and is generally
not supported by the software provider. Regardless of the operating system, the
recording software should be installed on a separate computer to other parts of the
experiment (e.g. stimulus programme) to avoid unwanted interactions.
13.2 Before Starting Your Study
The stored EEG data are directly influenced by hardware and software configuration.
This section covers key parameters that should be considered and their effects on the
data. Some parameters are related to the hardware concepts discussed in Chap. 12,
Hardwa
you also read that chapters as it provides complementary advice.
be
This ensures an informed choice of recoding parameters based on the study
requirements.
re for Recording EEG and Peripheral Physiology; it is recommended that
Even when the recording software is bound to the hardware, researchers should
familiar with the parameters available and how they relate to the recorded data.
13.2.1 General Parameters
The options available will vary depending on the hardware and software combination used, but there are some general parameters that should be considered.
Number
that they match the physical channels of the amplifier. For example, if your amplifier
is capable of recording 64 channels but your study is only using 32, confirm that the
correct ones are selected.
of Channels Make sure the correct number of channels is specified and
158 T. Warbrick and D. Kadlec
Channel Types If you are recording different types of data, for example, peripheral
physiology in addition to your EEG, ensure that your channels are set up correctly.
Signals such as electromyography (EMG) typically require a bipolar recording setup
while some measures, such as respiration, are recorded from sensors with different
parameters. Consult the manufacturer’s guidelines for optimal recording with different sensors.
Sampling Rate The sampling rate should be 5–10 times larger than the frequency
interest (Weiergräber et al., 2016). However, extremely high sampling rates create
of
large
data files, especially for longer recordings, so choosing the highest available is
not always the most sensible option. If multiple samp ling rates are available choose
the most appropriate for your study based on the planned analyses and recommendations in the literature.
Amplitude Resolution The measurement range of an amplifier is determined by
many bits it has and the amplitude resolution: in other words, how many
how
possible digital values the signal can have and the size of steps between values.
The number of bits is a fixed hardware feature but sometimes the amplitude
resolution is configurable. A smaller amplitude resolution will give you a more
fine-grained representation of the original signal but a smaller measurement range
for a given number of bits. For example, a 16-bit amplifier has 2
16
¼ 65,536 possible
steps. With a resolution of 0.1 μV/bit the measurement range is 3.28 mV and at
0.5 μV/bit it is 16.384 mV.
Filters
It’s important to make the distinction between hardware filters, software
filters, and display-only filters.
The hardware filters determine the frequency range reaching the analog to digital
er and include an antialiasing filter (see Chap. 12, Hardware for Recording
convert
EEG
and Peripheral Physiology).
Software filters are applied to your data after they have been digitised but before
they
are saved to disk. It is generally recommended to avoid using software filters as
the filtered frequencies are permanently removed. Instead, it’s better to record the
whole range determined by the hardware filter and then filter offline when needed.
Display filters are applied for visualisation purposes so you can view a ‘clean’
l during recording, but the full range of data determined by your hardware
signa
filters is preserved. For example, if you’re not able to remove all sources of mains
electrical noise from your recording environment, you will see a 50 Hz or 60 Hz
signal in your data. A display filter can be used to remove this noise so you can view
a cleaner signal without affecting the stored data.
Triggers and
Event Markers The recording software should be configured to
receive triggers and record event markers. There are several ways to register the
timing information of the main events of your experiments along with your EEG
data, for example using hardware triggers and software markers. Chapter
covers the different options in detail.
gers,
14, Trig-