Non-invasive canine electroencephalography (EEG): a systematic review

Abstract

The emerging field of canine cognitive neuroscience uses neuroimaging tools such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to map the cognitive processes of dogs to neural substrates in their brain. Within the past decade, the non-invasive use of EEG has provided real-time, accessible, and portable neuroimaging insight into canine cognitive processes. To promote systematization and create an overview of framings, methods and findings for future work, we provide a systematic review of non-invasive canine EEG studies (N=22), dissecting their study makeup, technical setup, and analysis frameworks and highlighting emerging trends. We further propose new directions of development, such as the standardization of data structures and integrating predictive modeling with descriptive statistical approaches. Our review ends by underscoring the advances and advantages of EEG-based canine cognitive neuroscience and the potential for accessible canine neuroimaging to inform both fundamental sciences as well as practical applications for cognitive neuroscience, working dogs, and human-canine interactions.

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Kulgodetal. BMC Veterinary Research (2025) 21:73
https://doi.org/10.1186/s12917-025-04523-3
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BMC Veterinary Research
Non-invasive canine
electroencephalography (EEG): asystematic
review
Akash Kulgod1, Dirk van der Linden2, Lucas G. S. França2, Melody Jackson3 and Anna Zamansky4*
Abstract
The emerging field of canine cognitive neuroscience uses neuroimaging tools such as electroencephalography (EEG)
and functional magnetic resonance imaging (fMRI) to map the cognitive processes of dogs to neural substrates
in their brain. Within the past decade, the non-invasive use of EEG has provided real-time, accessible, and port-
able neuroimaging insight into canine cognitive processes. To promote systematization and create an overview
of framings, methods and findings for future work, we provide a systematic review of non-invasive canine EEG studies
(N=22), dissecting their study makeup, technical setup, and analysis frameworks and highlighting emerging trends.
We further propose new directions of development, such as the standardization of data structures and integrat-
ing predictive modeling with descriptive statistical approaches. Our review ends by underscoring the advances
and advantages of EEG-based canine cognitive neuroscience and the potential for accessible canine neuroimag-
ing to inform both fundamental sciences as well as practical applications for cognitive neuroscience, working dogs,
and human-canine interactions.
Keywords Canine science, Cognitive neuroscience, EEG
Introduction
e multidisciplinary field of cognitive neuroscience is
a synthesis of cognitive psychology and neuroscience,
aiming at mapping “elementary cognitive functions onto
specific neuronal systems” ([1], pg. 613). rough the
deployment of techniques such as the electroencepha-
logram (EEG) [2] and functional magnetic resonance
imaging (fMRI) [3, 4], neuroscientists are able to perform
empirical and quantitative analyses of cognitive pro-
cesses. For a comprehensive review of the history, meth-
ods, and current frameworks of cognitive neuroscience,
readers are directed towards Gazzaniga, Ivry and Mun-
gun’s 2019 treatment of the field [5]. Another multidis-
ciplinary field is canine science, combining disciplines
including evolution, genetics, cognition, ethology, physi-
ology, comparative medicine, and ecology [6–9]. Investi-
gating the recent surge of interest in the scientific study
of the domestic dog, Aria and colleagues [10] found a
sixfold increase in the number of studies in canine cogni-
tion and behaviour between the years 2006 and 2018, as
compared to the preceding period of 1993 to 2005. is
interest extends beyond purely veterinary, pharmaceuti-
cal, and basic neuroscience paradigms. Recent studies in
canine science span such varied topics as genetics [11],
evolutionary neuroscience [12, 13] and intelligence [14],
as well as investigations into models of epilepsy [15],
aging [16, 17] and dementia [18]. Recent developments,
such as the rise of open-science multi-team initiatives
such as the ManyDogs Project, which aims to investigate
*Correspondence:
Anna Zamansky
annazam@is.haifa.ac.il
1 Dognosis Technologies, Bangalore, India
2 Northumbria University, Newcastle upon Tyne, UK
3 Georgia Institute of Technology, Atlanta, USA
4 University of Haifa, Haifa, Israel
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Kulgodetal. BMC Veterinary Research (2025) 21:73
behavioral traits across multiple centers and populations
of dogs [19], and the Working Dog Project, which focuses
on improving genetic selection strategies in dog breeding
[20], herald the emerging trend of collaborative consorti-
ums to tackle fundamental questions in the field.
Berns etal. pioneered the use of fMRI in awake, non-
restrained dogs in 2012 [21], and in the following year,
Kujala and colleagues were the first to successfully deploy
non-invasive EEG with non-sedated dogs [22]. Further
developments included the investigation of a range of
cognitive processes and their neural underpinnings such
as executive functioning [23], visual [22, 24], auditory [25,
26], and olfactory [27] processing, social cognition [28],
learning [29] and sleep [30, 31]. While the field of canine
fMRI has received increasing scientific attention, non-
invasive canine EEG has eluded similar treatment. is
is despite its noticeable strengths, including high tempo-
ral resolution, accessibility, and real-world applications.
For these reasons, we offer a consolidation of the state of
EEG-based canine cognitive neuroscience, providing an
overview of the key conceptual framings, methodological
approaches, and findings. Importantly, we note that the
diagnostic use of EEG in dogs, such as for the diagno-
sis of epilepsy, is not in-scope of this review.
To that end, this systematic review contributes the
following:
An identication and mapping of 22 non-invasive
canine EEG studies based on a thorough literature
review and an tailored database query paired with
appropriate exclusion/inclusion criteria.
A systematic analysis of these studies dissecting
their research question and participant make-up,
technical setups deployed, dataset properties, and
analytical frameworks and findings.
A critical discussion on future avenues for non-
invasive canine EEG identifying promising questions
that can be pursued, ideal data practices, integration
with other research sub-fields and beneficial meth-
odological and analytical refinements.
Methods
Data sources andsearch query construction
No complete literature review was available as a start-
ing point for this review, although two articles on
sleep EEG in dogs [32] and sleep spindles [33] were
encountered that provided useful context. To begin,
we conducted an informal search using Google Scholar
in February 2023, searching for ‘EEG in dogs’ and read
through a number of obvious candidates ([22, 34–38] [26,
30, 39, 40]) to identify commonalities. Of these papers,
we noted that publishers included PLOS One, Springer,
Elsevier, and the Royal Society, while we would also
expect papers published in IEEE and ACM venues to be
relevant. is allowed us to minimize necessary redun-
dancy in data sources by employing each publisher’s
own search engine, as Scopus indexed all early identified
papers, and papers by expected publishers.
After reading the initial set of studies, we made the
following assumptions to aid in the construction of our
search query, to ensure as broad a coverage as possible:
• Dogs are referred to interchangeably as ‘dog*’ or
‘canine*’, even if articles do not necessarily include
the full scope of the canis family; this required an
additional exclusion criterion if papers include e.g.,
Canis lupus rather than just Canis lupus familiaris.
• EEG is referred to both as ‘EEG’ and ‘Electroenceph-
alography’ or more vaguely hinted at with terms such
as ‘brain signals’ or ‘neural processes’ in paper titles,
meaning we needed to search through abstracts as
well.
• ere is no consistent (from title) indication of
whether the used EEG technique was invasive or
non-invasive, this required liberal inclusion criteria
to include any relevant study and stringent exclusion
criteria to manually filter any invasive EEG study.
• e first non-invasive, non-sedated canine EEG study
could be clearly identified as taking place in 2013 [22]
so the year 2010 allowed for all relevant studies to be
included.
Based on the above assumptions and reading of the ini-
tial papers, we constructed this search query optimized
for the SCOPUS database in March 2023:
( TITLE ( ( canine* OR dog* ) ) AND TITLE-ABS-KEY ( ( eeg OR erp OR elec-
troencephalography ) ) ) AND PUBYEAR > 2010 AND PUBYEAR < 2024
AND ( LIMIT-TO ( LANGUAGE , “English” ) )
e search query resulted in 205 articles in the SCO-
PUS database.
Study selection
We first liberally applied the inclusion criterion, followed
by a set of three exclusion criteria – see Fig.1 for an over-
view of the entire process and interim study numbers.
e inclusion criterion was:
inc1 Studies applying electroencephalography to
dogs (canis lupus familiaris)
e following exclusion criteria were used to refine the
selection:
ex1 Studies of other canine species (e.g., wolves,
jackals, coyotes) – we built in this potentially
redundant exclusion criteria due to the polysemous
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Kulgodetal. BMC Veterinary Research (2025) 21:73
use of ‘canine’ in some literature, sometimes refer-
ring to its actual meaning of the Caninae subfamily,
but at other times used as a synonym for dogs only.
ex2 Studies employing invasive applications of EEG
– we define this as the physical ontology of the sub-
ject remaining unviolated, i.e. the epidermis of the
subject dog is not pierced or excised. is excluded
any study using intracranial EEG (iEEG) [41] as well
as sub-dermal EEG that uses needle electrodes, a
technique first used by Pellegrino and Sica (2004)
[42] in a veterinary context as well as cognition
studies such as by Howell and colleagues [43].
ex3 Studies employing anaesthesia or other form of
sedation – we do include studies of naturally sleep-
ing dogs, which, indeed, form the majority of the
studies conducted in this category
One author applied the inclusion and exclusion cri-
teria over the total set of 205 papers leading to a final
selection of 22 articles. To ensure consistent appli-
cation of the criteria, another author independently
coded a randomly selected 10% subset of the papers.
Inter-rater reliability analysis indicated substantial
agreement between authors on application of the inclu-
sion criterion (Cohen’s
κ
=0.69) criteria, as well as the
exclusion criteria (resp.
κ
=0.69, 1.00, 1.00 for ex1, ex2,
and ex3), leading to the same set of selected papers.
We found that the validating author labeled more criti-
cally, which led to some discussion as to whether ret-
rospective studies were to be included; but effectively
these had no effect on the actual selected publications
as most were ruled out by both authors on grounds of
the exclusion criteria.
Fig. 1 PRISMA flowchart of the study selection process
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Kulgodetal. BMC Veterinary Research (2025) 21:73
Data extraction andanalysis
We dissect the obtained studies according to the differ-
ent workflow stages of a scientific study: research ques-
tion and participants, data acquisition, and analysis/
findings, respectively. is process was done in entirety
manually by one author. After providing an overview of
the 22 studies, we break them down along the following
three dimensions -
• Setups deployed to collect data: electrode types,
montages, and amplifiers.
• Datasets collected in each of the papers: e.g., avail-
ability and quantity of data.
• Analysis frameworks and findings: e.g., pre-pro-
cessing pipelines (where relevant), and findings.
For each of these steps, we highlight and synthesize
common themes and approaches and use this to pro-
vide a consolidated outlook on the field and suggestions
for future work.
Results
Overview
e selected works are presented in Table 1. e
dimensions we use and the overall trends of the studies
were as follows :
• Topic investigated. We label each study with one or
more of the following categories -
– visual processing : perception, discrimination or
interpretation of images (n=3)
– auditory processing : perception, discrimination
or interpretation of sounds (n=1)
– language processing : perception and comprehen-
sion of speech (n=3)
– learning : associative learning, memory, and prob-
lem-solving (n=4)
– emotion : interpretation of emotionally coded
stimuli (n=2)
– social cognition : behavior with con-specifics or
humans (n=2)
– sleep : stages and occurrence of patterns during
sleep (n=14)
– methodology : approaches to data collection and
analysis such as automation or application of
machine learning (n=3)
• Specific research question. We briefly state the
research question/hypothesis investigated in each
study.
• Number of dogs. e number of participants in each
study ranged from 2 [39] - 155 [46].
• Type of dogs. Here we refer to home/laboratory dogs,
and their breed (specific/multiple).
• Dog Training. In this category, we describe the nature
of training provided to the dogs for data collection
in the study, distinguishing between studies that
employed special training and those that did not. We
use special training to refer to a dedicated training
process consisting of multiple preparatory sessions,
such as habituation to the equipment, conducted
prior to actual data collection.
Overall, four different research centers in North Amer-
ica and Europe have conducted non-invasive canine EEG
studies, with the majority of them coming from Eötvös
Loránd University in Budapest, Hungary. e majority of
studies (n=14) recorded EEG from sleeping dogs, taking
advantage of the ease of recording higher quality data for
longer periods and the presence of well-defined cross-
species EEG data-structures associated with sleep stages
and features.
e majority of dogs were companion (home) dogs
whose guardians were recruited using surveys, and con-
sisted of a diverse group of pure and mixed breeds across
ages in both male and female animals. Four studies [22,
34–36] record from purpose-breed laboratory Beagles
and only these dogs underwent extensive training for
EEG recording purposes, whereas companion dogs were
habituated to equipment on the same day as recorded
sessions.
EEG setups
To facilitate a systematic and rigorous comparison of
studies, we compiled Table 2 outlining the six differ-
ent canine EEG setups used by reviewed studies. Some
important dimensions of the setups are a) electrodes type
and attachment, which includes the type of electrodes,
method of attachment, whether fur was shaved, and the
impedance of signals (a measure of conductivity at the
electrode-skin interface); b) the number and montage
of electrodes, which refers to the number of recording
channels and the specific arrangement of electrodes; and
c) the amplifier model and sampling rate.
Electrodes type andattachment
Electrodes are made of different conductive materi-
als and are generally categorized along two binaries -
gel vs dry, and active vs passive. e benefit of different
electrode types are still debated [55, 56], however, with
regards to our review, all studies used passive gel elec-
trodes secured with surgical tape or electrode cream.
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Kulgodetal. BMC Veterinary Research (2025) 21:73
Table 1 Overview of the 22 reviewed studies
Study Ref Journal Lab location Topic Research
question No. of dogs Type of dogs Special training
Törnqvist et. al.,
2013 [34] Animal Cognition Jyväskylä Exploratory, Visual
Processing Do ERPs
distinguish
between human
vs dog faces?
8 Lab-breed;
Beagles Yes; 1.5yrs, twice
a week
Kujala et. al., 2013 [22] Plos One Jyväskylä Visual Processing Can non-invasive
EEG detect
changes in brain
oscillations?
8 Lab-breed;
Beagles Yes; 1.5yrs, twice
a week
Kis et. al., 2014 [30] Physiology &
Behavior Budapest Sleep Can non-invasive
EEG detect
changes in sleep?
22 home dogs;
multiple breeds No
Kis et. al., 2017 [40] Nature Scientific
Reports Budapest Sleep, Learning Are there
associations
between sleep
quality and learn-
ing ability?
15 home dogs;
multiple breeds No
Iotchev et. al.,
2017 [44] Nature Scientific
Reports Budapest Sleep, Learning Are there features
of sleep (spin-
dles) that predict
learning rates?
15 home dogs;
multiple breeds No
Levitt et. al., 2018 [35] Journal of Neuro-
science Methods Rhode Island Methodology Can ML (SVM)
models detect
EEG artifacts
across humans
and dogs?
9 Lab-breed;
Beagles Yes, unspecified
Bunford et. al.,
2018 [45] Nature Scientific
Reports Budapest Sleep What are
some variables
that affect sleep
macrostructure?
16 home dogs;
multiple breeds No
Iotchev et. al.,
2019 [46] Nature Scientific
Reports Budapest Sleep How does sex
and age affect
non-REM EEG
activity?
155 home dogs;
multiple breeds No
Iotchev et. al.,
2020a [47] Nature Scientific
Reports Budapest Sleep, Methodol-
ogy Are some
measures
of sleep spindles
better predictors
of learning rates?
46 home dogs;
multiple breeds No
Gergely et. al.,
2020 [48] Animals Budapest Sleep, Methodol-
ogy What factors
affect sleep
scoring and can
it be automated
with ML?
10 home dogs;
multiple breeds No
Iotchev et. al.,
2020b [49] Nature Scientific
Reports Budapest Sleep, Learning What
is the relation
between spin-
dle frequency
and learning?
58 home dogs;
multiple breeds No
Kiss et. al., 2020 [50] Frontiers of Psy-
chology Budapest Sleep, Social
Cognition Does sleep struc-
ture correlate
with perfor-
mance in a coop-
eration task?
27 home dogs;
multiple breeds No
Reicher et. al.,
2020 [51] Journal of Sleep
Research Budapest Sleep Does canine
sleep macro-
structure display
the first-night
effect?
24 home dogs;
multiple breeds No
Kujala et. al., 2020 [36] Nature Scientific
Reports Jyväskylä Visual Processing Can ERPs predict
visual (faces)
stimuli?
8 Lab-breed;
Beagles Yes; 1.5yrs, twice
a week
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Kulgodetal. BMC Veterinary Research (2025) 21:73
Four studies shaved the fur of participant dogs [22,
34–36], with the goal of ensuring higher quality data
by decreasing impedance, however studies that did not
shave fur have achieved comparable levels of impedance.
Higher impedance values result in a lower signal-to-noise
ratio (SNR), although optimal electrode impedances vary
relative to an amplifier’s input impedance [57] (but see
also Kappenman and Luck [58] for a review). All studies
reported keeping impedance values below 20 k

.
Electrodes number andposition
Canines have smaller brains than humans and subsequent
less space available for electrodes. e reviewed studies
used between 1 to 7 channels, along with a ground and
reference electrode. In comparison, human EEG stud-
ies typically vary between using 4–256 channels. While
the far lower number of electrodes in canine EEG
studies raises serious questions on the validity of the
experiments, modern advances in hardware as well as
advanced analysis techniques such as deep neural net-
works allow for meaningful inferences from few elec-
trodes [59]. For instance, Hartmann and colleagues
were able to predict seizure episodes in humans using
data from just two electrodes [60], and a plethora of
researchers have been able to validate classical human
EEG paradigms with 4–6 electrodes [61, 62]
Electrode montages in EEG research refers to the “logi-
cal, orderly arrangements of electroencephalographic
derivations or channels that are created to display activ-
ity over the entire head and to provide lateralizing and
localizing information” [63]. Human EEG research gen-
erally uses the 10–20 montage system, formalized in
1957-58 by Herbert Jasper [64], to standardize electrode
montages and ensure replicability across studies. Canine
EEG has traditionally borrowed from the human 10–20
system and the reviewed studies use similar derivations,
Table 1 (continued)
Study Ref Journal Lab location Topic Research
question No. of dogs Type of dogs Special training
Magyari et. al.,
2020 [37] Royal Society
Open Science Budapest Language Pro-
cessing Do ERPs
distinguish
between known/
unknown/non-
sense words?
17 home dogs;
multiple breeds No
Reicher et. al.,
2021a [52] Nature Scientific
Reports Budapest Sleep Does sleep mac-
rostructure vary
based on age?
91 home dogs;
multiple breeds No
Reicher et. al.,
2021b [53] Nature Scientific
Reports Budapest Sleep Do dogs display
hemispheric
asymmetry dur-
ing NREM sleep?
19 home dogs;
multiple breeds No
Boros et. al., 2021 [38] Current Biology Budapest Language Pro-
cessing, Learning Do dogs use
statistical learn-
ing to segment
speech and learn
words?
19 home dogs;
multiple breeds No
Bálint et. al., 2022 [26] Royal Society
Open Science Budapest Auditory Process-
ing Do ERPs
distinguish
between human
and dog vocaliza-
tions?
17 home dogs;
multiple breeds No
Karpiński et. al.,
2022 [39]Journal of Veteri-
nary Behavior Lublin Exploratory Do ERPs
distinguish
between voice
commands?
2 home dogs;
multiple breeds No
Carreiro et. al.,
2022 [54] MDPI Animals Budapest Sleep, Social
Cognition Do sleep fea-
tures correlate
with attachment
towards human
guardians?
42 home dogs;
multiple breeds no
Carreiro et. al.,
2023 [31] Nature Scientific
Reports Budapest Sleep Does sleep effi-
ciency correlate
with guardian-
rated hyperactiv-
ity?
86 home dogs;
multiple breeds No
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Kulgodetal. BMC Veterinary Research (2025) 21:73
Table 2 Overview of the six EEG setups used by the reviewed studies
Setup tag Papers Electrode type Attachment Fur shaved Impedance
(k
ω
)Active EL pos Active EL pos Ref EL Ground EL Amplier Sampling
rate (Hz)
Setup1
Jyväskylä [22, 34, 36] Unilect 40555
Neonatal EEG
electrodes
Bio-adhesive
solid gel Yes 9 ± 4 7 Fp1, Fp2, F3,
F4 P3, P4, Cz
(2013); F3, F4,
T3, T4, P3, P4, Cz
(2020)
Right ear Lower back Embla
Titanium-
recorder
512
Setup2 Rhode
Island [35] Custom built
EasyCap EEG
with Hydrospot
EEG Electrodes
Electrode gel Yes N/A 1 Fz Midline (Cz) Left musculus
temporalis Wireless
microEEG,
Biosignal
group
250
Setup3 Lublin [39] N/A Aqueous KCI Yes N/A 2 F3,F4 Ear N/A 16-channel
MindSet-1000
Nolan Com-
puter Systems
256
Setup4 Buda-
pest 1 [30, 40, 44–46,
52]Gold-coated
Ag|AgCl EC2 Grass Elec-
trode Cream No 15 2 Fz, Fp1 Cz Left musculus
temporalis 30-channel
Flat Style
SLEEP La
Mont Head-
box
249
Setup5 Buda-
pest 2 [31, 46, 47,
49–52, 54]Gold-coated
Ag|AgCl Electrode cream No 20 4 Fz, Cz, Fp1, Fp2 Pz Left musculus
temporalis 25-channel
SAM 25R
MicroMed
Headbox
1024
Setup6 Buda-
pest 3 [26, 31, 37, 38,
52, 54]Gold-coated
Ag|AgCl EC2 Grass Elec-
trode Cream No 15 4 Fz, Cz, Fp1, Fp2 Pz LMT 40-channels
NuAmps
amplifier,
Compumed-
ics Neuroscan
1000
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although setups differed from each other in the montages
used and authors differed in the labels used for specific
montage positions. For an outlook of the montages used
in the reviewed studies, see Figure2. A notable challenge
with standardizing electrode montages in dogs is the
remarkable variance in head shape and size across canine
breeds. One way to solve this challenge is to record only
from dogs of the same breed and avoid it all altogether,
which was the case with the studies recording only from
lab-bred Beagles [22, 34–36]. Another approach is to use
the relative distance between breed-invariant anatomical
markers followed by the Budapest setups [30, 37].
Traditional electrode montages on humans when
translated to dogs might suffer from a higher rate of mus-
cular artifacts due to the presence of a muscular scalp in
dogs, which motivated the authors of the Budapest set-
ups to place electrodes on the anteroposterior midline, or
sagittal crest, of the canine skull (Fz, Cz, and Pz respec-
tively), as it is a bony ridge that minimizes muscular arti-
facts. ese setups also used either 1 or 2 electrodes close
to the eyes (F7 and F8) to measure electrooculography
(EOG) signals. EOG signals are useful for understanding
eye activity, such as blinks, which can have an effect on
recordings from other electrodes. Jyväskylä setups dif-
fered in their approach by positioning electrodes laterally
- on the frontal cortex F3 and F4, on the posterior cortex
P3 and P4, and the temporal cortex T3 and T4 (the latter
only in the 2020 study [36]).
Finally, the position of the reference electrode plays
a key role in recordings as EEG data is derivational
and signals from any electrode are meaningful only in
respect to a reference electrode (or electrodes). In the
Budapest setups, either the center of the sagittal
crest (Cz) or the bony ridge of the occipital protu-
berance, or occiput, (Pz) was used as a reference, the
latter chosen given its relative distance from muscu-
lar activity. e Rhode Island setup also used a Cz ref-
erence. Meanwhile, Jyväskylä setups used an electrode
placed on the ear as the reference, as did the sole Lublin
study. Figure2 gives an overview of different potential
electrode montages used across the selected studies.
Amplier model andsampling rate
Various factors determine amplifier performance
including sampling rate, input range, amplifier imped-
ance, bandwidth, and the common mode removal ratio
(CMRR). A detailed discussion of these factors is outside
the scope of this review (readers are directed towards
the 2023 review of EEG systems by Niso and colleagues
[65] for a discussion). One feature worth highlighting is
the sampling rate which refers to the number of samples
that are digitally acquired per second (measured in Hz).
Higher sampling rates are useful primarily to measure
higher frequency brain activity, given by the Nyquist-
Shannon theorem [66] that states that for any periodic
signal of a given frequency, a sampling rate higher than
two times the frequency of the signal is needed to accu-
rately detect its presence. Sampling rates of amplifiers
used ranged from 250–1024 Hz. As all studies focused
analysis on bandwidths between 0-50Hz, all amplifiers
had a sufficient sampling rate.
Fig. 2 Different electrode montages used by the six technical setups. Electrode labels follow from the human 10–20 system, where letters indicate
the lobe - Fp=pre-frontal, F=frontal, P=parietal, T=temporal, and C=center. The left and right zygomatic arch are also depicted as LZA and RZA. Odd
numbers refer to electrode placement on the left side and even numbers indicate placement on the right side of the brain. Reference electrode
positions are highlighted in orange. It is worth pointing out that large variance amongst canine breeds means the figure is not representative of all
canine individuals
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Datasets
e six different technical setups described in the prior
section were used to acquire 18 datasets, summarized
in Table3. e discrepancy between number of papers
and datasets is because some papers developed novel
methodological approaches [44] or refinements [47, 48]
using older datasets, or deploying a different analytical
framework on the same dataset [22]. Many studies use
a combination/amalgamation of different data sources
so the datasets presented here should not be assumed to
be well-demarcated and unique. Moreover, the overall
number of dogs is difficult to establish, as we observed
a considerable overlap of participants between studies
and datasets, with the same dogs being recorded multi-
ple times and the same datasets being reused in different
papers. Datasets were categorized into either epoch or
continuous paradigms, based on which, the number of
events/minutes of data per dog and per dataset was cal-
culated. All but one [35] of the awake dogs datasets were
epoched and all sleep datasets were continuous. Finally,
we noted the availability of the datasets. No datasets were
freely available in their entirety and six datasets were
partially available, either requiring a) additional author
authorization on a data repository; b) an email request;
c) missing important metadata; d) missing raw data, i.e.
containing only processed data.
Analysis andndings
We review and consolidate awake and asleep dog studies
separately, given their different frameworks, workflows,
and results.
Preprocessing
Preprocessing pipelines in EEG data analysis involve
several steps to clean and prepare the data. One of
these steps is artifact removal that aims to identify and
remove segments of the signal containing unwanted
noise or corruption. These artifacts can originate
Table 3 Overview of the 18 datasets created by non-invasive canine EEG studies
Name Papers No. of dogs Unique
dogs? Type Conditions Events OR
sessions per
dog
Duration of
recording
per dog
(min)
Total events
OR duration
of data
Open source?
Faces1 [22, 34] 8 Yes Epoch, Con-
tinuous 2 240 80 976 No
Sleep_Explor-
atory1 [30] 22 Yes Continuous 1 1 180 3960 No
Sleep_Explor-
atory2 [30] 7 No Continuous 2 2 360 2520 No
Sleep_Learn-
ing1 [40] 15 Unsure Continuous 2 2 360 5400 No
Auto_Arte-
facts [35] 9 Yes Continuous 1 90 N/A N/A No
Sleep_Activity [45] 16 Unsure Continuous 2 2 540 8640 No
Sleep_Vari-
ation [46] 155 Unsure Continuous 1 1 180 27900 No
Sleep_Aging [47] 58 Unsure Continuous 2 2 N/A N/A No
Sleep_Indi-
vidual [50] 27 Unsure Continuous 1 1 180 4860 No
Sleep_Adapt [51] 24 Unsure Continuous 3 2 180 4320 No
Faces2 [36] 8 Unsure Epoch 8 1200 N/A 8000 Partially A
Phoentics [37] 17 Unsure Epoch 3 240 N/A 4080 Partially B
Sleep_Devel-
opment [52] 91 Partial Continuous 1 3 540 49140 No
Statistical_
Learning [38] 19 Unsure Epoch 4 320 12 6080 Partially B
Vocalizations [26] 17 Unsure Epoch 4 192–384 N/A N/A Partially C
Pilot_Explora-
tory [39] 2 Yes Epoch 2 2 N/A N/A No
Sleep_Attach-
ment [54] 43 Unsure Continuous 1 1 1-3h N/A Partially D
Sleep_Hyper-
activity [31] 86 Unsure Continuous 1 1 1.5-3h N/A Partially D
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Kulgodetal. BMC Veterinary Research (2025) 21:73
from various non-neural sources, such as eye blinks,
muscle activity, or external interference [67]. Artifact
removal can be performed using automated methods
that apply predefined thresholds, such as amplitude
criteria, to detect and exclude segments with excessive
noise. Alternatively, manual inspection techniques like
independent component analysis (ICA) or video moni-
toring can be employed. The choice between manual
and automated detection methods is a topic of ongoing
debate in the field, as each approach has its strengths
and limitations [68].
Reviewed studies differed in their application of
pre-processing techniques, which have potential
important ramifications for the analysis and valid-
ity of their respective findings. For example, Magyari
etal. (2020) [37] tested two different artifact removal
procedures - a multi-level method combining quan-
titative and qualitative steps, as well as a single-step
approach using filtering and amplitude-based artifact
removal. The multi-level approach consisted of auto-
mated amplitude-based rejection, manual video cod-
ing of movement and manual inspection of EEG data.
The single-step consisted only of automated artifact
removal and filtering. The results showed similar
condition differences between the two cleaning pro-
cedures, with a varying percentage of rejected trials.
Approximately 75% of the trials were rejected in the
multi-level data cleaning, while 53% were rejected in
the amplitude-based procedure. However, the analy-
sis findings did not differ between preprocessing
pipelines, and the authors conclude that both manual
multi-step and automated single-step pipelines were
equivalent. Subsequent studies from the Budapest
group only use the automated amplitude-based rejec-
tion step.
Kujala etal. [36] differed in their approach by apply-
ing a manual inspection of independent component
analysis (ICA) components to mitigate muscular and
other artifacts. Artifact-related components were vis-
ually identified and excluded, while a general linear
model was used to remove potential electric leakage
from the stimulus trigger signal to EEG channels.
In contrast, Levitt and colleagues [35] trained a sup-
port vector machine (SVM) classifier for the auto-
mated detection of EEG artifacts in human, canine, and
rodent subjects. ey labelled artifacts in the canine
EEG data by utilizing inputs from more than one
independent observers, although no specifications
are given on how coding conflicts were resolved. e
models showed relatively high accuracy across species
in identifying artifacts caused by skeletal and ocular
muscles, with an accuracy of 80.57% and an AUC-ROC
value of 0.87 for canines specifically.
Analysis andndings ofwakefulness EEG indogs
Awake dog EEG activity accounts for 8 of the 22 exam-
ined studies in this review. is section provides an
overview of the analysis frameworks and findings of
the 8 studies.
One of the studies, Levitt etal., focused on training a
SVM model to detect artifacts for preprocessing. Two
of the remaining seven studies were exploratory, with
Kujala and colleagues [22] showing for the first time the
ability to perform non-invasive EEG in eight awake dogs
in 2013. ey observed changes in the power spectrum
over the P3/P4 (parieto-occipital) electrodes during the
presentation of a visual stimulus vs rest. Karpinski et. al.
[39] recorded pilot data from two companion dogs dur-
ing rest and after two different commands, also observing
qualitative changes in the power spectrum over the F3/F4
(frontal) electrodes.
e remaining five studies [26, 34, 36–38] deployed
event-related potentials (ERP) frameworks to understand
visual, auditory and language processing. Törnqvist etal.
[34] investigated the ERPs of dogs in response to human
and dog faces. e study found that ERPs corresponding
to early visual processing were detectable at 75–100 ms
from stimulus onset, and significant differences for dog
and human faces could be identified at around 75 ms at
posterior sensors. Another study [36] deployed a similar
experimental paradigm, with the addition of emotionally
valenced faces and objects, and detected a group-level
response sensitive to emotional expressions at 130–170
ms, and the differentiation of faces from objects occur-
ring at 120–130 ms. e authors also trained a support
vector machine (SVM) classifier on the EEG data to
discriminate between responses to pairs of images. e
classification accuracy was highest for humans/dogs vs.
scrambled images, with the most informative time inter-
val being between 100–140 ms and 240–280 ms after the
presentation of stimuli.
Two studies investigated facets of language process-
ing using ERP paradigms [37, 38]. Magyari et. al [37]
explored the group-level ERPs of dogs listening to known,
unknown, and nonsense words, finding a significant dif-
ference in ERP values between known and nonsense
words at 650–800 ms. ey also found a positive asso-
ciation between the word usage frequency by the dog’s
guardian and the individual dog’s ERP effects, providing
evidence that the association may arise from familiarity
with the words. e second study to deploy an ERP para-
digm [38] investigated the neural processes underlying
speech segmentation. e authors examined ERPs from
the presentation of artificial words, after participants had
been exposed to a continuous speech stream that differed
in the distribution of words . In their study, two impor-
tant factors were considered: transitional probability and
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Kulgodetal. BMC Veterinary Research (2025) 21:73
word frequency. Transitional probability refers to the
likelihood of one sound or word following another in a
sequence, reflecting the statistical regularities of the lan-
guage. Word frequency, on the other hand, represents
how often a word occurs in a given language. e results
of the study showed that the ERPs exhibited an early
effect (220–470 ms) related to transitional probability
and a late component (590–790 ms) modulated by both
word frequency and transitional probability, indicating
the involvement of multiple cognitive processes in speech
segmentation.
Finally, Balint et al. [26] investigated the auditory
processing of 17 dogs in response to human and dog
vocalizations. ey found that, similar to humans, dogs
exhibited differential ERP responses based on the species
of the vocalizer. Specifically, within the time window of
250–650 ms after stimulus onset, ERPs were more posi-
tive for human vocalizations compared to dog vocaliza-
tions. Furthermore, a later time window of 800–900 ms
demonstrated an ERP response that also reflected the
species of the vocalizer. ese results highlight the exist-
ence of species-specific processing of vocalizations in
dogs and provide insights into the neural mechanisms
underlying their perception of human and non-human
vocalizations.
Analysis andndings ofsleep EEG indogs
EEG recordings of sleeping dogs were examined in 14 of
the 22 studies in this review.
In 2014, Kis and colleagues pioneered the use of non-
invasive EEG to record brain activity in sleeping dogs,
providing a tool to investigate fundamental questions
about sleep architecture in canines [30]. While most
studies relied on human coding and analysis, automated
techniques using algorithms to find specific patterns
called spindles [44, 47] were developed and refined. Fur-
thermore, machine learning models, including logistic
regression (LogReg), gradient boosting trees (GBTs),
as well as convolutional neural networks (CNNs), were
also deployed and validated to predict sleep stages in
dogs [48].
Another study investigated the effect of pre-sleep activ-
ity, timing, and location on sleep macro-structure, such
as the duration of sleep and the transitions between sleep
stages [45]. e authors discovered that the intensity of
pre-sleep activity and the location and timing of sleep
sessions had interactive effects on sleep macrostruc-
ture. Pre-sleep intensive activity and night-time sleeping
were associated with more time spent in both non-rapid
eye movement (NREM) and rapid eye movement (REM)
sleep. Furthermore, they found that dogs sleeping in a
location outside their home were less likely to experience
REM sleep. A later study by Reicher and colleagues [51]
investigated the well-known first-night adaptation effect
seen in humans and found that it also manifests in dogs,
albeit with marked differences. e first-night adapta-
tion effect refers to the recurring observation that the
first recorded sleep session in humans differs from all
subsequent recordings, as it is marked by the necessity
to adapt to the recording conditions. In dogs, a signifi-
cant difference was observed between the first and third
recordings, with dogs spending more time in sleep and
having a shorter latency to drowsiness in session 3 than
in session 1. Reicher and colleagues [53] also investigated
whether dogs exhibit functional hemispheric asymmetry,
a phenomenon in which the right and left hemispheres of
an individual displays differential activity during a cogni-
tive process, frequently observed during sleep in aquatic
mammals [69]. ey found a complex asymmetry contin-
gent on the recording session, sleep cycle, and type of fre-
quency, with some similarities but also many differences
between canines and humans.
In addition to exploring fundamental and comparative
questions, researchers have also investigated the effect of
biological variables such as age, sex, and weight on sleep
activity. e effect of age on sleep macrostructure is sig-
nificant, showing correlations with the power of some
frequency bands [52]. Specifically, past 8 months of age,
older dogs had higher powers of alpha, beta and gamma
frequencies, and lower delta frequencies, compared to
younger dogs. Another approach has involved the meas-
urement of spindles, which are phasic bursts of thalamo-
cortical activity that appear in the cortex as transient
oscillations in the sigma range (typically defined in
humans as 9–16 Hz) [70]. In 2017, Iotchev and colleagues
[44] developed an algorithm to quantify sleep spindles
in dogs and subsequent work has discovered associa-
tions between the frequency, density, and amplitude of
spindles with the age and sex of canine participants. It
is worth highlighting the observation that an increase
in age was associated with a decrease in the density and
amplitude of slow spindles [46].
Another theme explored by researchers in dog EEG is
the relationship between sleep activity and other cog-
nitive processes. For example, Kis and colleagues [40]
observed a connection between sleep activity and learn-
ing rates, specifically that increased beta and decreased
delta activity during REM sleep were related to higher
performance on a novel learning task. Which could be
related to the processes described in Iotchev’s work, on
the potential significance of spindle activity for learning
and memory processes [44, 47, 49]. ese studies suggest
that learning gain (increases in performance on cognitive
tasks between sessions inter-spaced by sleep) is corre-
lated with measures of spindle density. While the authors
acknowledged potential confounders with demographic
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Kulgodetal. BMC Veterinary Research (2025) 21:73
variables, the studies suggest a possible causal role of
spindles in the consolidation of memory.
Regarding a different association with sleep qual-
ity, Carreiro etal. [31] investigated the relation between
sleep activity and owner-rated hyperactivity and found
that dogs rated as more hyperactive and impulsive dem-
onstrated less total sleep time, a reduced percentage
of REM sleep, and lower spindle density compared to
dogs rated as less hyperactive and impulsive. Moreover,
owner-rated hyperactivity and impulsivity were associ-
ated with increased wakefulness after sleep onset and
greater sleep fragmentation.
Finally, two studies explored the relation between sleep
activity and features of human-canine interaction - spe-
cifically cooperation and attachment. Kiss et al. [50]
deployed an experimental paradigm testing the ‘audi-
ence effect’ between dogs and their human guardians,
which relates to the difference in task performance based
on the presence of visual attention. Spectral sleep analy-
sis revealed associations between REM and non-REM
power activity and susceptibility to the audience effect. In
other words, the willingness of participant dogs to follow
task instructions, irrespective of whether their guardian
was looking at them, was associated in a trait-like man-
ner with alpha, beta, theta, delta frequency bands power
during sleep. Carreiro et al. [54] used an adapted form
of the Strange Situation Task (SST) [71] to index attach-
ment levels of canine subjects and investigated whether
derived attachment scores correlated with sleep activity
features. ey found associations between the level of
attachment and the duration of NREM sleep, as the activ-
ity in certain frequency bands.
Discussion
Review ofndings
Temporal nature ofdog cognitive processes inwakefulness
Non-invasive EEG uses electrodes placed on the scalp
that pick up signals that are the end product of the inte-
gration of postsynaptic potentials of hundreds of thou-
sands of neurons traversing from the brain across tissue,
bone, muscle, skin and hair. is leads to a measure of
brain activity with a low spatial resolution but high tem-
poral resolution [72]. Effectively, this means that the
inferences from awake canine EEG data are related to
fine-grained features of temporal activity. is is what
we saw from the five studies that deployed hypothesis-
driven ERP analysis frameworks to investigating facial,
vocalization and speech comprehension, with the sig-
nificant time-windows displayed in Table4. Meaning-
ful inferences can be derived from a wide range of times
(between 30-950ms) post onset of a stimulus, as previ-
ously highlighted by a study on the potential of machine
learning (ML) models such as Support Vector Machines
(SVMs) in predicting stimulus categories based on activ-
ity in such time-windows [36].
Relationship betweensleep andphysiological traits indogs
In contrast to wakefulness dog studies, the reviewed
sleep studies took a different approach in their analysis
framework. While the high temporal resolution of EEG
was occasionally leveraged in the spindle studies [46, 47],
the main focus was on investigating associations between
more general sleep stages and patterns and psychologi-
cal traits. Some of the significant correlations found are
highlighted in Table5.
Comparisons betweendog andhuman EEG
Non-invasive EEG with humans has a rich literature, and
reviewed studies often used comparative framings to gen-
erate hypotheses or provide explanatory models. An over-
view of some of the overlapping components are provided
in Table 6. e N1 component, well-studied in human
subjects [73], appears to also be present in canines. Kujala
(2013) [22] noted a deflection at 75ms in response to visual
stimuli, earlier than typically observed in human studies.
is component, observed primarily in posterior channels
(P3/P4), appears to differentiate between human and dog
faces. Boros (2021) [38] observed a N100 effect at electrode
Fz as opposed to electrode Cz for word stimuli. A face-
sensitive component, representing a holistic representation
of a face, appears at 170ms post stimulus for humans [74].
Kujala (2020) [36] identified emotional expression-depend-
ent effects between 127-170ms from stimulus onset, sug-
gesting face processing in dogs may be connected with
the processing of the affective content of the stimulus. e
word-familiarity effect, visible in human infants between
200-400ms [75] post word-onset, may also be present in
dogs. ERP differences between WORDS and NONSENSE
conditions appeared between 650-800ms post word-onset,
towards the end of the words [37].
Table 4 Overview of selected time-windows that had significant
event-related potential activity
Time-window (ms) Description Studies
30–40 Aggressive dog faces [36]
75–110 Difference between faces/
objects and scrambled images [22, 36]
220–470 Word segmentation (transi-
tional probability) [38]
250–650 Species vocalization sensitivity [26]
360–400 Difference between dog
and human faces [36]
590–790 Word frequency [38]
650–850 Known versus nonsense words [37]
800–900 Valence in vocalizations [26]
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e P300 is a component of the event-related potential
(ERP) that is typically elicited in the process of decision
making [76]. It is most commonly evoked during “odd-
ball” tasks, where the participant is asked to respond to
infrequent or unexpected stimuli amongst a set of stand-
ard stimuli. e P300 wave occurs roughly in the range
300–600 milliseconds after the presentation of the stim-
ulus and is understood to be correlated with the degree
of attention allocated to a stimulus [77]. Dogs exhibited
a P300 response in the study by Balint (2022) [26], with
a more positive ERP response to human as compared to
dog vocalizations between 250 and 650 ms, which may
reflect a difference in the motivational significance and
allocated attention to human and dog vocalizations.
Lastly, the N400 component is a well-researched event-
related potential (ERP) [78, 79] that is often associated
with semantic processing in language comprehension.
It’s characterized by a negative peak occurring around
400ms post-stimulus onset. Dogs exhibited a more
positive deflection for words with high transitional prob-
ability than with low transitional probability between
220–470 ms after word onset, with late ERP effects also
observed between 590–790 ms, potentially representing
a higher order recognition at the word level.
Avenues forfuture development
Unexplored questions
While reviewed studies explored the neural correlates
of visual and auditory processing, olfactory processing
has so far been unexplored. Studies with humans indi-
cate that relevant olfactory processing features can be
extracted from EEG data [80, 81]. An understanding
of the neural correlates of olfaction would be vital to a
greater understanding of a dog’s perception and cogni-
tion. Early studies that measured EEG of sedated dogs
showed promise in decipherable differences between
evoked potentials between stimuli [82] and multiple
studies using fMRI have observed meaningful neural
correlates from olfactory tasks in dogs [27, 83, 84].
Similarly, studies exploring questions of cognitive
control or executive function, defined by Gazzaniga
and colleagues as the “set of psychological processes
that enable us to use our perceptions, knowledge, and
goals to bias the selection of action and thoughts from
a multitude of possibilities” [5], have remained rela-
tively underexplored. Cognitive control encompasses
a wide range of processes, including working memory,
attentional control, cognitive flexibility, and inhibitory
control, and some precedent for into these domains
is provided by prior canine cognitive research using
fMRI [23, 85]. As with olfaction, questions of cogni-
tive control not only inform us about the neural under-
pinnings of canine cognition but also have significant
practical value in the way humans communicate with
and train dogs.
Table 5 Overview of selected associations between sleep
macrostructure and patterns of sleep and physiological and
psychological states
Sleep feature Associated physiological
or psychology state/trait Studies
Duration in NREM
and REM Pre-sleep intensive activity,
time and location of sleep,
owner-rated hyperactivity
[31, 45]
Alpha frequency
power Age, cooperation, attach-
ment [50, 54]
Beta and delta fre-
quency power Age, learning rate, coop-
eration [40, 50]
Gamma frequency
power Age [51]
Frequency, density,
and amplitude
of spindles
Age, sex, learning gain,
owner-rated hyperactivity [44, 46, 47, 49]
Table 6 Overview of selected overlapping EEG components between humans and dogs
Component Human Canine
N1 (100ms) First negative deflection post-stimulus
onset Occurs at 75ms in response to visual stimuli, can differentiate
between human and dog faces.
Face-sensitive (170ms) Holistic representation of a face is gener-
ated at 170 ms post stimulus onset Emotional expression-dependent effects at 127–170 ms
post stimulus onset, suggesting face processing in dogs may be
connected to the processing of the affective content
Word-familiarity (200-500ms) Observable word-familiarity effect
in human infants ERP differences between WORDS and NONSENSE conditions
appeared between 650 and 800 ms following word-onset
P300 and LPP (300ms) Associated with attention and stimulus
evaluation Dogs show a differential ERP response depending on the species
of the caller between 250 and 650 ms, suggesting attentional dif-
ferences to human and dog vocalizations
N400 (400ms) Indicates speech segmentation of candi-
date words Significant effect of transitional probability (220–470 ms)
and possible higher order recognition (590–790 ms) after word
onset
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Kulgodetal. BMC Veterinary Research (2025) 21:73
Intertwined with these questions on cognitive control
are questions on the onset and processing of emotions.
Both Kujala et. al. [36] and Balint et. al. [26] investi-
gated the effect of positive and negative stimuli, and
found observable differences in the respective evoked
potentials. A broader and deeper investigation into the
realm of canine emotions would likely elucidate further
such evidence, as work with fMRI on emotions such as
jealousy shows [28, 86]. Relatedly, it is worth investi-
gating whether fundamental physiological states, such
as hunger, stress, and the need of elimination, are also
represented by neural correlates that can be be consist-
ently identified by non-invasive EEG. A fundamental
constraint on such questions is the poor spatial depth of
EEG, as emotional and physiological processing involve
structures located deeper in the brain, to which EEG is
not the adequate technique to map.
Another underlying challenge with some of these ques-
tions is the problem of ascertaining ground truth. is is
especially made apparent by the question of identifying
emotions and to create ethical experimental conditions
where specific emotions can be consistently engendered
in a non-human animal. However, there is potential in
cross-modality methods being used to triangulate such
ground truth, such as the use of machine vision [87] and
FACS (Facial Action Coding System) [88] as well as phys-
iological data on respiration and heart-rate from wear-
able sensors [89, 90].
Finally, the majority of reviewed studies deployed
group-level analysis frameworks. Such frameworks have
dominated canine cognition research, indeed Arden and
colleagues find that from 1911 to 2016, only three stud-
ies took an explicit individual-differences approach to
exploring canine cognition [14]. Given the large inter-
species variation amongst dog breeds, especially in head
and brain shape, a greater number of individual-differ-
ences analyses is well-warranted.
Standard setups fordog EEG
A challenge downstream of canine variance is the diffi-
culty in standardizing electrode montages. e conven-
tion used in human research is the 10–20 system that
allows for a consistent placement of electrodes across
individuals [64]. Reviewed studies borrowed from the
human 10–20 system in the placement of electrodes,
although they differed in their labels for similar elec-
trode montages. An important question is if the 10–20
system is capable of transferring over to canines in a
useful way given the marked and asymmetrical differ-
ence in head shapes between dolichocephalic, meso-
cephalic, and brachycephalic dogs [91]. For instance,
the distance of a 10% posterior increment between
a greyhound and a bulldog would likely lead to stark
differences in the brain regions that are recorded by the
same electrode position [12].
A related issue is the lack of a standard reference elec-
trode. ree different electrode position labels were used
by the six different setups, and it is not clear that these
labels refer to the same anatomical position. As men-
tioned prior, the position of the reference electrode has
a strong and irrevocable influence on the EEG record-
ing, and it is unlikely that meaningful comparisons can
be made across subjects and labs, if the same anatomical
reference is not used. Four studies used the ear as a ref-
erence position, which runs contrary to the 2020 recom-
mendations for reproducible human EEG research issued
by the Organization for Human Brain Mapping (OHBM)
[57], which recommended against physically linked ear-
lobe or mastoid electrodes as they are not a neutral refer-
ence and can introduce distortions in the data that make
modelling intractable. is is likely the case for canine
EEG as well, especially given the natural tendency for
dogs to move their ears to attune to stimuli, although it is
important to note the influence of breed type, as Beagles
arguably display less ear movement given their floppy
nature. e other four setups either use Cz or Pz as a ref-
erence. It is worth noting, as the authors themselves do
[26], that the choice of Pz could lead to an attenuated
recording from Cz relative to Fz, given the relative dis-
tance between the two. At the same time, the choice of
Cz as a reference could attenuate relevant sleep sig-
nals, as physiological transients to be dominant over
the central region of the cortex.
It is also important for the reference electrode to be
close to the same sources of noise as the channels, to
ensure relevant noise in the channels is eliminated.
Future research could incorporate other reference sys-
tems such as bipolar montages, where each channel rep-
resents the potential difference between two adjacent
electrodes, or the laplacian montage, where the reference
is averaged signal of neighboring electrodes [63].
e development of a standard montage and validation
of different reference montage systems, centered on the
specificity of canine anatomy, and equipped to deal with
the large variance amongst canine individuals would be
greatly beneficial to future progress in the field.
A standard data structure fordog EEG
As the volume and complexity of cognitive neuroscience
methods has grown, several challenges emerged in the
organization, dissemination, and analysis of data, lead-
ing to the creation of new standards and protocols for
neuroscience data structure and management. e brain
imaging data structure (BIDS), first proposed in 2016
for magnetic resonance imaging [92], is an exemplar of
such a standard that embodies the FAIR principles of
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Kulgodetal. BMC Veterinary Research (2025) 21:73
findability, accessibility, interoperability, and reusability
[93]. Recently, a BIDS standard for EEG data - EEG-BIDS
- was proposed to address the same concerns [94]. BIDS
allows for the friction-less sharing of data within and
between laboratories, as well as enabling the automation
of analysis scripts, that all serve to address the replicabil-
ity of findings.
As noted prior, none of the reviewed studies had data-
sets that were fully accessible to external researchers.
ree studies that did attempt to make data open-source
lacked the necessary information to replicate analy-
sis, either omitting raw data or lacking vital meta-data.
While researchers should be continued to be encouraged
to open-source their data, in the vein of suggested guide-
lines by the Organization for Human Brain Mapping
(OHBM), it is also vital that such shared data is interop-
erable and accessible for researchers across labs and time.
Adapting EEG-BIDS for canines would be a crucial step
to ensuring replicable and robust canine EEG research,
and can readily be done, with the primary challenge
being the adoption of a standard anatomical coordination
system to serve as the necessary metadata file. As such
a data structure would require adoption by researchers
in the field and it should be a priority to ensure consen-
sus on its creation and use. Additional canine specific
metadata, such as head size measurements, might also
be included in such a protocol to allow for flexibility
and refinement in analysis. Upon the adoption of canine
EEG-BIDS, data can be made accessible on a neuro-
science-specific repository such as OpenNeuro, which
would allow for large datasets. Furthermore, common
analysis pipelines, such as developed by the BCI2000
open source system for humans [95], could be readily
adapted to canine data, opening up the world of canine
brain-computer interfaces.
Improving signal‑to‑noise ratio
e presence of a furry and muscular scalp makes
improving the Signal-to-Noise Ratio (SNR) an impor-
tant challenge to overcome for non-invasive canine EEG
experiments. One approach to improving SNR would
be using electromyography (EMG), alongside EEG, to
quantify the contribution of scalp muscle activity. With
enough measurements from individuals and across dog
breeds, it could be possible to regress out muscle activ-
ity from neural activity, and allow robust recordings from
further electrode positions.
Alongside, impedance benchmarks for different breeds,
as well as for different electrode types (e.g. wet vs dry),
would inform optimal electrode design to increase SNR.
An example of such a study was performed by Luca and
colleagues for wired vs wireless sub-dermal electrodes
with canines [96]. e use of custom canine phantoms to
measure impedance for different systems could also be
productive, as seen for human EEG studies [97–99]. It is
worth noting that the majority of reviewed studies used
wired systems and only one study used a canine-specific
cap to hold electrodes in place. e use and development
of canine-specific caps coupled with wireless modern
systems would greatly reduce the noise from electrode
slippage and wire movement [65] and lead to an increase
in SNR. Moreover, such systems would allow experi-
ments in freely moving dogs in naturalistic settings.
Along with custom wireless canine systems, the use of
active electrodes, over the passive one used by all studies,
have the potential to greatly boost SNR. Active electrodes
consist of electrodes with a mini-amplifier system loaded
onto the electrode itself, boosting signal quality at the
source, and thus increasing SNR overall [100]. Another
avenue could be the creation of ground-truth standardi-
zation tests based on steady-state visual-evoked poten-
tials (SSVEPs) [101, 102]. A SSVEP is the brain’s evoked
potential in response to the presentation of periodically
flashing visual stimuli, and this has been observed to
form a stable EEG wave with a frequency that matches
the presented stimulus. us, a SSVEP framework can
provide a grounding upon which the SNR of a particular
system can be measured. Auditory steady-state responses
(ASSRs) are similar to SSVEPs but with specific frequen-
cies embedded in audio. ASSR paradigms are poten-
tially easier to do with dogs than SSVEPs as dogs don’t
have to be trained to stare at a flashing stimulus. Similar
to SSVEPs, ASSRs can provide a metric to ascertain the
SNR of a system [103].
Finally, the detection of artifacts is crucial to ensur-
ing high SNR. All awake experimental reviewed studies
deployed manual or algorithmic preprocessing pipelines
to clean their recorded canine EEG data. However, as
shown by Levitt and colleagues [35], machine learning
models are capable of eye-blink and muscular artifact
identification and removal for non-invasive canine EEG
data, although it is important to point out the limited
accuracy of 80%. Moreover, the same models used for
human EEG data performed well when trained on canine
EEG data, suggesting the potential for transferring mod-
els with weights from human EEG data to canine EEG
data. While the use of ML artifact removal pipelines con-
tinues to be debated in the field [68], and further larger
studies should be conducted before making any strong
conclusions, it seems possible for ML preprocessing
pipelines to be a valuable addition to the field, especially
as more than half the data from some awake dog studies
had to be manually thrown out because of artifacts [37].
As such pipelines could feasibly allow the identification
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Page 16 of 19
Kulgodetal. BMC Veterinary Research (2025) 21:73
and removal of artifacts without losing out on the data
from the entire trial, this could significantly increase the
quantity of data available for analysis, boosting SNR,
especially as the number and quantity of EEG recordings
increases.
A search forbetter models forcanine EEG
We observed in the reviewed studies a shift in focus
from explanatory models to prediction models, a distinc-
tion articulated by Yarkoni and Westfall in their 2017
paper [104]. e distinction is raised with the pertinent
criticism that psychology often prioritizes explana-
tory models, which are prone to overfitting and rarely
tested for out-of-sample accuracy. ey further argue
that this emphasis on explanatory models has contrib-
uted significantly to the replication crisis in psychology,
as these models often fail to generalize to different data
sets. In contrast, a predictive approach aims to construct
models that can effectively predict out-of-sample data
by leveraging established machine learning techniques
such as leave one-out cross-validation [104, 105]. is
shift toward prediction models offers a promising direc-
tion for improving the robustness and generalizability of
canine EEG research.
A particular pitfall to be addressed, is the observed
limitation in some reviewed studies, where multiple com-
parisons went uncorrected or under-corrected. As the
nature of EEG studies involves many degrees of freedom,
including multiple channel locations, time windows, and
subjects, spurious significance values are likely. Some
studies attempted to adjust for this using methods such
as the Bonferroni correction, which involves adjust-
ing the
α
value by the number of hypotheses (m) by the
following formulae - actual alpha = desired
α
/m. How-
ever, some studies omitted to consider the true range
of hypotheses being tested, e.g. not considering time-
window choice as a relevant hypothesis. is lack of
adequate correction for multiple comparisons, coupled
with the relatively small sample size of studies raises seri-
ous questions of replicability. It is worth pointing out,
however, that correcting for multiple comparisons is
a complex subject, and researchers in other domains
have argued that it might not always be necessary
[106] or valuable to do so [107].
One challenge with the use of predictive models is the
need for large amounts of data to train models [108].
One tractable approach could be to combine a predictive
model framework with an individual level analysis. at
is, large amounts of EEG data can be collected from a
few dogs for a specific task, and machine learning models
can be trained on data from these individual dogs. Com-
paring the performance of such models across dogs and
tasks, as well as correlation between model performance
with other behavioral and cognitive traits should provide
insight on questions of canine cognition, whilst provid-
ing an alternative model to solve the problem of multiple
comparisons.
Conclusion
e rise of non-invasive canine EEG can be traced to
lying at the intersection of three trends - the increasing
maturity of cognitive neuroscience, the rejuvenation of
canine science, and the increasing sophistication in port-
able and accessible neuroimaging methods. e latter is
primarily due to the increasing interest in brain-com-
puter interfaces (BCIs) [109]. We may be at the cusp of
the emerging field of canine brain-computer interfaces,
where wearable and non-invasive systems could allow
dogs to interact with objects, their environment, and
humans through cognitive processes alone. Such systems
could be beneficial to researchers in the field of canine
science, as it extends the field of possibilities in experi-
mental design, as well as potentially reducing the time
needed for operant training. e development of canine
BCIs would be benefited by the framings of the field of
Animal-Computer Interaction (ACI), which works to
design technologies for non-human users using a devel-
opment model which incorporates iterative prototyping,
animal welfare as a central value, and a direct involve-
ment of animal experts at all stages of the development
process [110].
It would be vital to remember that neuroscience needs
behavior [111], and that further work with canine EEG
continues to incorporate behavioral measures alongside
neural data. One useful approach could be the lens of
embodied and 4E cognition, where cognition is seen to
extend beyond the confines of the brain [112, 113]. An
embodied approach to canine EEG would emphasize
embodied data, such as from wearable heart and respi-
ration sensors, as well as acknowledge the influence of
the environment, as well as human and con-specifics, on
neural patterns.
In conclusion, the utility of non-invasive EEG encom-
passes the diverse and expansive roles that dogs have
come to occupy in our societies, providing a portable,
accessible, and ethical method to derive quantitative
data on canine cognition. Non-invasive EEG can lead to
insights on the shared neurological conditions as well as
cognitive processes in humans and canines, and provide
a data-driven amplifier to the training and deployment of
working dogs.
Authors’ contributions
A.K. and A.Z. conceived the study, A.K. and D.L. conducted the study, A.K, D.L.,
A.Z. and L.F. analysed the results. All authors (A.K., A.Z., D.L., L.F., M.J.) wrote the
main manuscript text and reviewed the manuscript.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 17 of 19
Kulgodetal. BMC Veterinary Research (2025) 21:73
Funding
Not applicable.
Data availability
Data from this study is available upon request to the corresponding author.
Declarations
Ethical approval and consent to participate
Not applicable.
Consent for Publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 23 October 2023 Accepted: 24 January 2025
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