ArticlePDF Available


Cognitive dynamics are multimodal, and they need to integrate real-time feedback to be adaptive and appropriate. However, cognition research still relies on mostly unimodal paradigms using simple motor tasks in laboratory-based static situations. This paper addresses this limitation by presenting the Mobile Brain/Body Imaging approach based on the Embodied, Embedded, Extended, and Enactive perspective, which complements traditional laboratory work while also facilitating ecologically valid applications. First, we briefly review Mobile Brain/Body Imaging technologies used to obtain functional and structural images of the Brain/Body System during natural cognition. Specifically: mobile cognitive electrophysiology, mobile functional neurovascular dynamics, and mobile behavioral measurements. Second, we review the development of Mobile Brain/Body Imaging/4E in Chile. Finally, we discuss challenges and opportunities. We conclude that although this new epistemic/methodological approach is promising, there is a need for greater portability, robust equipment, and data-analysis tools that can integrate signals from the brain/body-in-the-world system. Future experimental designs need to re-consider their underlying logic and increase their ecological validity by perhaps-modifying the physical spaces in which experiments are conducted.
daptive Behavior
Special Issue: Chilean 4E cognition
Adaptive Behavior
2022, Vol. 0(0) 126
© The Author(s) 2022
Article reuse guidelines:
DOI: 10.1177/10597123211072613
Mobile Brain/Body Imaging: Challenges and
opportunities for the implementation of
research programs based on the 4E
perspective to cognition
Aitana Grasso-Cladera
, Stefanella Costa-Cordella
, Alejandra Rossi
Nikolas F Fuchs
and Francisco J Parada
Cognitive dynamics are multimodal, and they need to integrate real-time feedback to be adaptive and appropriate.
However, cognition research still relies on mostly unimodal paradigms using simple motor tasks in laboratory-based static
situations. This paper addresses this limitation by presenting the Mobile Brain/Body Imaging approach based on the
Embodied, Embedded, Extended, and Enactive perspective, which complements traditional laboratory work while also
facilitating ecologically valid applications. First, we briey review Mobile Brain/Body Imaging technologies used to obtain
functional and structural images of the Brain/Body System during natural cognition. Specically: mobile cognitive elec-
trophysiology, mobile functional neurovascular dynamics, and mobile behavioral measurements. Second, we review the
development of Mobile Brain/Body Imaging/4E in Chile. Finally, we discuss challenges and opportunities. We conclude that
although this new epistemic/methodological approach is promising, there is a need for greater portability, robust
equipment, and data-analysis tools that can integrate signals from the brain/body-in-the-world system. Future experimental
designs need to re-consider their underlying logic and increase their ecological validity by-perhaps-modifying the physical
spaces in which experiments are conducted.
MoBI, brain, body, real-world, 4E-cognition, Chile
Human cognition is characterized by its complexity and
multidimensionality. Adaptive and appropriate behavior in
the here and now requires a system which integrates sen-
sorimotor information from multiple modalities, the indi-
viduals personal experience, and possible future events.
This is achieved through real-time feedback processes that
guide, plan, predict, and compare sensorimotor states
(Wiese & Metzinger, 2017). These processes have been
conceptualized as natural cognition (Gramann et al., 2014;
Hutchins, 1995).
The multimodal nature of cognitive dynamics has been
known since the beginning of the study of the mind (Parada
& Rossi, 2020), from ancient Indian physicalism
(Bhattacharya, 2002) to the debate between Plato and
Anaxagoras (Plato, 1911). However, the traditional ap-
proach to studying cognition still relies on the acquisition of
datasets using simple motor tasks in relatively static situ-
ations, usually in a laboratory setting where participants are
isolated from each other (Figure 1 bottom). The main ad-
vantage of this approach is the control it gives over con-
founding variables. Its main disadvantage is the absence of
the multiple dimensions of the real world, which are not
considered in these experimental contexts. In the present
Centro de Estudios en Neurociencia Humana y Neuropsicolog´
ıa, Facultad
de Psicolog´
ıa, Universidad Diego Portales, Santiago, Chile
Escuela de Psicolog´
ıa, Facultad de Psicolog´
ıa, Universidad Diego Portales,
Santiago, Chile
Programa de Mag´
ıster en Neurociencia Social, Facultad de Psicolog´
Universidad Diego Portales, Santiago, Chile
Centro de Estudios en Psicolog´
ınica y Psicoterapia, Facultad de
ıa, Universidad Diego Portales, Santiago, Chile
Corresponding author:
Francisco J Parada, Centro de Estudios en Neurociencia Humana y
ıa, Facultad de Psicolog´
ıa, Universidad Diego Portales,
Vergara 275, Centro de Estudios en Neurociencia Humana y
ıa, Santiago 8370076, Chile.
article, we put forward an alternative and complementary
approach to the study of cognition in the 21st century. This ap-
proach falls within the emerging technico-methodological
Mobile Brain/Body Imaging (MoBI) framework, pioneered
most notably by the University of California, San Diego, and
the Technical University of Berlin (TU Berlin). The MoBI
framework is characterized by combining portable/mobile
neurobehavioral measuring devices with behavioral monitor-
ing in order to acquire high-dimensional data. Furthermore,
data-driven analytical approaches are furthermore used
in order to dissociate brain and non-brain activity (Gramann
et al., 2014;Makeig et al., 2009). The rst decade of
MoBI has been marked by technological advances in both
hardware and software, allowing simultaneous acquisition
of neural (e.g., electroencephalography, EEG), behavioral
(e.g., eye tracker), and/or bodily signals (e.g., electrocar-
diography, EKG) in real-time, while participants act nor-
mally in their environments (Figure 1 top).
From our perspective, MoBIs approach to the study of
cognition complements traditional laboratory work, while
also facilitating practical, ecologically valid interventions and
applications in elds such as psychology/psychotherapy
ıguez et al., 2018), art therapy (King & Parada,
2021), neuroprosthetics (Petrini et al., 2019), neuro-
aesthetics (Calvo-Merino et al., 2008;Chatterjee & Vartanian,
2014), sports science (di Fronso et al., 2019), and architecture
and design (Djebbara et al., 2019;Fich et al., 2019).
The rst section of this article will address the theoretical
assumptions underlying the MoBI approach, which
we argueare best understood from the ethico-onto-
epistemology known as the Embodied, Extended, Embed-
ded, and Enactive perspective to cognition (or simply, 4E
cognition). Later, we briey review the technological
advances that have enabled MoBI implementation. Finally,
a discussion of MoBIs strengths and limitations for the
study of cognition within the 4E perspective is presented.
Cognition is an embodied, extended and
embedded-for-action phenomenon but
can we measure it?
Although a key argument in the classical mind-body problem
(Bunge, 2014;Feyerabend, 1970) is the consideration of
cognition as a biological phenomenon (i.e.,aproductof
cognitive agentslived body), cognitive sciences have tra-
ditionally conceived cognition from a representational/
computational perspective. Thus, understanding the mind as
an information-processing machine, which syntactically
manipulates mental structures and/or abstract symbols
(Gardner, 1987;Rossi et al., 2019). This model also con-
ceptualizes cognition as an internal and predetermined pro-
cess that occurs in a central processing unit; it only happens
ontologicallyin the brain (Adams & Aizawa, 2001). This
perspective certainly centers human cognition at the throne of
mental abilities (i.e., anthropogenic; Lyon, 2006), even
comparing other species to human-like performance.
By contrast, the 1990s witnessed a revival of theories
from the rst half of the 20th century (Berthoz, 2008;
Gibson, 2014;Merleau-Ponty, 1976), including that of
the Enactive Mind, which emphasizes the ever-changing
relationship between mind, body, and environment
(Varela et al., 2016), the Extended Mind
, as the extension
of cognitive processes beyond the agents biological
structure (Colombo et al., 2019;Pritchard, 2010)ac-
centuating the functional coupling between cognitive
agents and their ecological niches (Clark & Chalmers,
1998;Flor & Hutchins, 1991), or the Dynamical System
Mind, understanding cognition as the product of ever-
increasing ontogenic complexity (Thelen & Smith, 1996).
In this way, cognition can be conceived as an evolutionary
phenomenon rooted in biology (i.e., biogenic; Lyon,
2006), but extended into the physical and socio-
cultural world, integrated with an ecological niche and
actively coupled with the outside world. This alterna-
tive perspective has recently been formalized within the
Figure 1. Experimental setups in cognitive/social
neuroscience. (Top) Dual-EEG and physiology
(hyperscanning) setting during psychotherapy. In this type of
setups, participants can behave naturally and interact depending
on the experimental constraints. (Bottom) Traditional
experimental setups where participants and their behavior is
more constrained within highly-structured experimental settings.
2Adaptive Behavior 0(0)
so-called 4E Cognition
perspective (Newen et al., 2018).
Briey, the 4E perspective to cognition is an ethico-onto-
epistemology that understands the mind as the emergent
result of complex multiscale interactions in time and
space, which can be understood by relatively independent
disciplinary scientic work on biophysical, psychologi-
cal, and social processes, along with resulting future
transdisciplinary efforts. It facilitates and fosters a
transversal and holistic way of thinking; connecting
students, researchers, and practitioners with human
exceptionalism while being accountable for the role we
play in the differential constitution and differential po-
sitioning of the human among other creatures(Barad,
2007, p. 136). Discussing the multiple dimensions and
implications of the 4E perspective is beyond the scope of
the present article, as it is the main topic for the present
special edition (see Gonzalez-Grandón & Froese, 2018;
Menary, 2010;andNewen et al., 2018). Nevertheless, we
would like to focus on one of the central tenets in the 4E
perspective: cognition is the product of cerebral dynamics
tightly coupled with physical, physiological, and socio-
cultural extracranial processes. Following Rossi et al.,
(2019)considering an internalist point of view
extracranial processes have little to no role in the gen-
eration of cognitive acts, while from an interactionist
perspective, they actually enable or constitute it (Figure
2(a)). See De Jaegher et al. (2010) and Rojas-L´
ıbano &
Parada for further discussion (2019).
According to Rossi et al., (2019), in the context of such
extracranial processes, two dynamics are conventionally
described: body dynamics (i.e., brain/body system) and
ecological dynamics (i.e., brain/body-in-the-world system).
One of the main problems arising from this kind of proposal
is the one regarding the constitutiveness of cognition, this is,
what processes actually constitute the mind? For example,
when a drummer hits a drum, she relies on neural processes
Figure 2. Are extracranial components and processes constitutive of the cognitive act? A. Internalist explanations of the mind posit
explanatory power in psychological, brain-bound processes. In contrast, interactional perspectives consider the diverse neural and
extracranial processes as functionally coupled systems from which the mind arises. Interaction-Dynamics-Process-driven perspectives
lead to re-considering the nature of cognitive mechanisms. B. What is the role of extracranial components and processes in a given
cognitive act such as drumming? C. The 4E perspective to cognition considers a cognitive act (e.g., drumming) as unequivocally
constituted by the dynamically coupled system composed by drummer, drumstick, and drum given their history of interactions.
Furthermore, those interactions uniquely inuence the sense making afforded by cognitive acts. For example, the unique textures and
psychological impact of art forms-such as the music composed by the expert drummer and composer Christian Vander-can only be
understood in the intersection of that particular ontogenic trajectory. Christian Vander photography courtesy of Stella Vander by Alain
Grasso-Cladera et al. 3
and her motor apparatus in order to enact such behavior
(Figure 2(b)). One could argue, though, that the hand-
drumstick-drum coupling is only enabling the drumming
process, and it is not constitutive of drumming behavior.
Nevertheless, the cognitive act of drumming (and any
musical/artistic endeavor) is the product of the dynamic
intersection between biophysical and sociocultural factors
unfolding throughout the agentsontogenic trajectory
(Figure 2(c)). This is, the cognitive act of drumming is only
understandable in the light of the history of interactions
between the drummer and her instruments (i.e., considering
the drummer-drumstick-drum system). The functional
coupling of such a complex system embedded within
specic contexts (e.g., the drummer drumming in a band at a
public concert) allows making sense in novel and unique
When complex constructs are considered for study
(Figure 2), our experimental tools are indeed limited. Further-
more, the constitutive,enabling,orcontextual nature of extra-
cranial processes in cognition is an ongoing debate in cognitive
science, we can only hope carefully designed experimental
manipulations will help advance these discussions (Adams &
Aizawa, 2001;De Jaegher et al., 2010;Kirchhoff, 2015;Parada,
ıbano & Parada, 2019). Thus, the experimental
side of cognitive/neural science also faces an epistemic/
methodological dilemma. The fact that cognition tends to be
studied in small and unnatural contexts is a problem that has
stressed experimental scientists throughout the 20th century
(Bronfenbrenner, 1977;Brunswik, 1943;Neisser, 1976). Sev-
eral have argued that laboratory reductionism is only able to
measure dynamics that are too dissimilar to the agentsbehavior
in the real world to have any social or ecological relevance
(Bronfenbrenner, 1977;Gibson, 2014). Thus, classical para-
digms usually have no consideration for person- and situation-
dependent factors that might be a limitation in studies, which
could be overcome in real-life paradigms (Shamay-Tsoory &
Mendelsohn, 2019). Person-dependent limitations refer to the
delimitation of the ability to act, reducing the sense of agency
while situation-dependent limitations are those that affect and
reduce the real-life experience in the world (Shamay-Tsoory &
Mendelsohn, 2019). It is furthermore relevant to consider
Brunswicksecological validity notion (for a current perspective
see Holleman et al., 2021) as many experimental paradigms that
at a rst sight seem unecologicalor articialmight actually
be functionally valid, triggering cognitive processes in the micro
level (Petitmengin & Lachaux, 2013) whereas 4E-based ex-
periments might be more interested in macrocognitive phe-
nomena (Fiore et al., 2010). Such epistemological concerns are
supported by empirical evidence from advanced neuroscientic
investigations of more complex questions (Krakauer et al.,
2017). For example, the effects of posture and movement on
cognition are so profound that even resting state brain dynamics,
as measured by hemodynamic measures, might be affected
simply by posture (Thibault et al., 2014). More recently,
Djebbara and collaborators (2019) showed that neuroelectrical
dynamics are modulated by potential body movements in space,
while Piñeyro Salvidegoitia et al. (2019) showed that spatio-
temporal context benets memory processes. Growing evidence
suggests that cognitive dynamics vary according to the action
being carried out by the agent. Furthermore evidence
demandsin parallel to the philosophical debatethe active
integration of physiological dynamics to the socio-behavioral
context of cognition for the empirical study of the mind
(Palacios-Garcia & Parada, 2019;Parada & Rossi, 2018;Rojas-
ıbano & Parada, 2019). Once again, the Scalable Experimental
Design heuristic (Matusz et al., 2019;Parada, 2018)willbea
useful strategy for carefully and systematically studying these
micro-to-macro cognitive processes.
From a theoretical standpoint, the 4E perspective requires
research programs incorporating ecologically valid and com-
plex, real world situations (Parada & Rossi, 2018;Rojas-L´
& Parada, 2019). Recent studies highlight the considerable
inuence of both behavioral context and the participants agency
on a range of cognitive processes (e.g.,Chrastil & Warren,
2012). Research using animal models reveals an interdepen-
dence between cognitive dynamics and active environmental
exploration. For example, in a study of the properties of visual
interneurons in fruit ies (Drosophila melanogaster),Maimon
and collaborators (2010) found that the activity of these cells
doubled during ight compared with resting state, due to an
increase in synaptic inputs to the visual vertical system. Re-
viewing comparative literature is beyond our scope (for reviews
see Gramann et al., 2011,2014;Ladouce et al., 2017). Nev-
ertheless, comparative results reinforce the idea that brain dy-
namics are inuenced by sensory, motor, cognitive, and neural
activity as well as the behavioral/contextual/social states
(Boehme & Olausson, 2022;Dagnino-Subiabre, 2022;Di Paolo
& De Jaegher, 2012;von Mohr et al., 2017). However, it is still
unclear whether the brain/body/world relationship is constitutive
of cognition or simply corresponds to enabling or contextual
interactions (De Jaegher et al., 2010;Rojas-L´
ıbano & Parada,
2019). The potential non-causal status of the relationship be-
tween multiple parts of a system (Craver, 2007) and the dynamic
interplay and reciprocal causality that exists between them have
yet to be elucidated (Leuridan, 2012). Until now, technological
constraints have hindered scientists in resolving this debate. In
the following section we introduce the technico-methodological
MoBI framework, which might open novel possibilities to
answering these questions.
Obtaining functional and structural images
of the Brain/Body-in-the-world System
during natural cognition
Understanding the complexity of cognition unveils the
limitation of traditional experimental approaches (Gramann et al.,
2014;Ladouce et al., 2017;Shamay-Tsoory & Mendelsohn,
2019). The importance of gathering (neuro)physiological data
4Adaptive Behavior 0(0)
in ecological contexts has been recognized since the be-
ginning of the 20th century both at the theoretical and
empirical levels; theoretical advances proposed by the 4E
perspective merely conrm this need. The fact that cog-
nitive science has largely been conned to the laboratory
is, for the most part, due to technical and methodological
limitations that go beyond theory. Laboratory experiments
try to eliminate any potential confounding factors asso-
ciated with natural behavior, being movement one of the
main sources of noise, as unwanted movements generate
large artifacts when measuring brain signals (Gwin et al.,
2010;Havsteen et al., 2017;Pl¨
ochl et al., 2012) and can
confound behavioral measures, even in carefully designed
experiments (Jungnickel & Gramann, 2016). Keeping
movement at a minimum is a very good practice indeed,
since one of the main advantages of traditional experi-
mental protocols is the fact that the researcher can exert
control over isolated variables and later precisely analyze
such factors. Thus, eliminating potential confounding
elements and maintaining artifactual signals to a minimum.
Hence, keeping brain signals as large as possible while any
backgroundnoise remains small. This notion is known
as signal-to-noise-ratio (SNR) and is one of the main
reasons laboratory experiments need many trials per
condition in order to adequately compute brain measures
of interest (Hu et al., 2010;Murphy et al., 2007;Sato et al.,
2004). Nevertheless, such controlled results might be a far
witness of what agents actually do in the real world,
decreasing their generalizability.
Fortunately, recent technological advances encompassed
under the MoBI framework, are just starting to match the
theoretical demands of the 4E perspective for the study of
cognition. In this section, we provide the reader with a brief
review of the main methodological advances that facilitate
an implementation of a research program based on the 4E
perspective; a program involving neurobehavioral data
acquisition in the real world. Providing a deep and detailed
account of every methodological approach greatly exceeds
the scope of the present work and this special edition.
Nevertheless, we hope to provide a perspective-opening
introduction to the reader while pointing towards key lit-
erature for further reading with an emphasis on the Chilean
Mobile cognitive electrophysiology
The main revolution in MoBI has been powered by non-
invasive electrophysiological measurement techniques.
Their high temporal resolution makes them suitable for
studying cognitive phenomena (Makeig et al., 2004). We
will discuss two of the most relevant: electroencephalog-
raphy (EEG, because of its prevalence in neuroscience
laboratories in Chile) and magnetoencephalography
(MEG). Even though MEG is not yet available in Chile, it
will be discussed as it presents a great opportunity to re-
search certain questions framed in the MoBI/4E research
Electroencephalogram (EEG) allows the measurement and
visualization of electrical elds resulting from electro-
chemical activity generated by spatially extended and
geometrically aligned populations of neurons (Hari & Puce,
2017;Nunez & Srinivasan, 2006). These eld dynamics are
captured by electrodes positioned on the scalp (Figure 1)
and its technology dates to the early 20th century (Berger,
1929). Electroencephalogram is a non-invasive technique
given its usability on most species without any risk of
damage or risk to the physical integrity of the partic-
ipants. EEG nowadays constitutes one of the most
powerful techniques to measure electrophysiologi-
cal cognitive brain dynamics. Given it is non-invasive,
EEG overcomes ethical limitations concerning the use of
neuroimaging/electrophysiology in human/non-human par-
ticipants. However, EEG provides only a general map of the
functioning brain, with limited spatial resolution, making it
difcult to discern precise structural locations from the
electric signals (Nunez & Srinivasan, 2006). Nevertheless,
recent advancements using high-density EEG, show that
source estimation might be highly possible and accurate,
even for subcortical structures (Seeber et al., 2019)and
depending on the research goal, spatial smearing can be an
advantage (Debener et al., 2015). Notwithstanding, despite
any potential disadvantages regarding spatial resolution,
given its great temporal resolution. EEG is one of the most
commonly used neuroimaging techniques in Chile and
worldwide. Its exibility allows building on Bergersrst
resting-state recordings (1929)bymeasuringongoingac-
tivity as well as triggering event-related dynamics in different
. Thus, scientists can acquire continuous ongoing
neurodynamics data from neurotypical participants and/or
special populations with research (Khanna et al., 2015;Smit
et al., 2008) and/or diagnostic purposes (Caricato et al., 2018;
Rubiños & Godoy, 2020). Likewise, scientists can analyze
event-related brain activity (Luck, 2012;Makeig et al., 2004)
in the time (e.g., Event-Related Potentials, ERP), frequency
(e.g., Power Spectra), time-frequency domains (e.g.,Event-
Related Spectral Power) and associated connectivity (Cohen,
2017; Varela et al., 2001).
Electroencephalogram has a special advantage in the
context of implementing MoBI/4E programs due to devel-
opments in the last decade. EEG systems now use more
compact, lightweight, and wireless devices which are closer
to the standard of experimental designs with ecological
validity (Gramann et al., 2014;Ladouce et al., 2017;Makeig
et al., 2009;Parada, 2018;Shamay-Tsoory & Mendelsohn,
2019). Based on these technological advances, research
Grasso-Cladera et al. 5
methodology has co-evolved to capture behavioral re-
sponses, ranging from simple actions like pressing a button
while remembering words (e.g.,Piñeyro Salvidegoitia et al.,
2019) to more complex perceptual and motor tasks made
possible by the portability of the latest EEG systems (e.g.,
Jungnickel & Gramann, 2016;Ladouce et al., 2019). These
techniquescomplement the use of other technologies, such as
motion capture and/or virtual reality to simulate environ-
ments (Djebbara et al., 2019;Fich et al., 2019;Gramann
et al., 2021). Furthermore allowing the modeling of cognitive
processes in real-time along with natural behaviors occurring
in the real world (Makeig et al., 2009).
Moreover, the development of new electrode technolo-
gies will enable EEG data to be collected from portable,
comfortable-to-wear (Mathewson et al., 2017;Oliveira
et al., 2016) and perhaps unobtrusive devices (Bleichner
et al., 2016;Debener et al., 2015;H¨
olle et al., 2020;
Mikkelsen et al., 2015;Mirkovic et al., 2016). These future
developments will allow researchers to acquire neuro-
physiological signals during daily activities, such as ex-
ploring the built environment (Gramann et al., 2017;
Palacios-Garcia et al., 2020;Parada, 2018;Wunderlich &
Gramann, 2020) and/or during clinical encounters, such as
psychotherapy (Lecchi et al., 2019;(Lecchi et al., 2019;
Parada, Mart´
ın, et al., 2018;Rodr´
ıguez, Mart´
ınez, D´
Flores, Alvarez-Ruf, Crempien, Vald´
es, Campos, Artigas,
Armijo, Krause, et al., 2018;Ryu et al., 2020).
Thus, EEG becomes a highly relevant method for the 4E
research program as it allows measurement during natural
conditions and even during social interaction. The mea-
surement of two or more participants simultaneously is
known as hyperscanning (Babiloni et al., 2006). Electro-
encephalogram has been one of the most used methods in
hyperscanning due to its high temporal resolution and
mobility in contrast with other neuroimaging techniques
(Ahn et al., 2018;Czeszumski et al., 2020;Dumas et al.,
2011;Liu et al., 2018). Hyperscanning analyses usually
assess the level of coupling/synchronization between two or
more participantsbrains (Babiloni et al., 2006;Dumas
et al., 2011;Liu et al., 2018). Nevertheless, other tech-
niques are also used (see Czeszumski et al., 2020 for a recent
review). The simultaneous recording of brain activity allows
the study of social interactions due to the possibility to
explore interpersonal underlying brain mechanisms during
social interaction scenarios (Balconi & Fronda, 2020;
Czeszumski et al., 2020;Liu et al., 2018). Hyperscanning
research affords studying social cognition in more natu-
ralistic settings (Hari & Kujala, 2009;Konvalinka &
Roepstorff, 2012). Thus, more natural and social experi-
mental paradigms may allow causality and/or correlation
analyses in brain activity of two or more subjects interacting
(Moreau & Dumas, 2021;Novembre & Iannetti, 2021).
Several topics related to social cognition have been studied
using EEG-hyperscanning: joint and shared attention,
interactive decision-making; affective communication, and
others (for recent reviews see Czeszumski et al., 2020; and
Liu et al., 2018). Furthermore, hyperscanning also allows
the implementation of Scalable Experimental Designs
(Experimentos Escalables en su Diseño, EED) as an attempt
to study cognition in natural/real-world settings (Matusz
et al., 2019;Parada, 2018).
One of the most interesting advances in recent years has
been the development of portable devices to measure
electromagnetic elds in the brain. Magnetoencephalogram
(MEG), like EEG, is also a direct and non-invasive brain
measure technique with excellent temporal resolution.
Furthermore, MEG signals are less compromised by con-
ductivity smearing at the scalp, rendering them appropriate
for source reconstruction imaging techniques with a mil-
limeter spatial resolution (Dale et al., 2000). Until now,
MEG has required high-cost equipment which is bulky and
impossible to move. This is because the underlying su-
perconducting technology (known as SQUID, Super-
conducting Quantum Interference Device) is supercooled
by liquid helium (Ahonen et al., 1991). A newer technology,
known as Optically Pumped Magnetometer (OPM) is less
expensive than SQUID, requires no supercooling, and can
be worn by a human participant (Boto et al., 2018). Results
obtained using OPM-based MEG indicate that data quality
is comparable to that of traditional MEG, even in the
presence of neck and head movements (Boto et al., 2018).
Unfortunately, there are still important limitations to OPM.
The most important is that it will only work in spaces that
are properly shielded from other electromagnetic sources,
including the Earths magnetic eld. This fact makes it
impossible to use in the actual real world,leaving mobile
MEG inside the shielded laboratory at best. Nevertheless,
this technological advance is still relevant for MoBI/4E,
given MEGs excellent temporal and spatial resolution
(Dale et al., 2000;Hill et al., 2020;Stam, 2010). Mobile
MEG is thus a promising and viable option for the direct
study of cognitive neurodynamicsboth in its structural
and functional aspectsusing more ecologically valid
study designs in healthy adults and other populations (Hill
et al., 2019,2020;Seymour et al., 2021). Pioneers such as
Cerca Magnetics Ltd. and others will lead the way in
combining mobile MEG with virtual/augmented reality
settings (Roberts et al., 2019) and more ecological labo-
ratory tasks in order to further advance the MoBI/4E pro-
gram (Seymour et al., 2021).
Body physiology
Considering that the self is rooted in the bodyis a basic
premise of both MoBI and the 4E perspective, measuring
6Adaptive Behavior 0(0)
body physiology is highly relevant. Several of these mea-
sures can be included within MoBI. Here, we consider what
we think as some of the most relevant for the 4E research
First, electrodermal activity (EDA), as a complex cor-
relate of sympathetic nervous system activity (Braithwaite
et al., 2013), refers to autonomic changes or variation of the
electrical properties of the skin modulated by sweat gland
activity (Benedek & Kaernbach, 2010;Braithwaite et al.,
2013). It has commonly been used to assess the level of
cognitive arousal (Posada-Quintero & Chon, 2020). Its
measurement and physiological relevance dates to the
second half of the 19th century (Hermann & Luchsinger,
1878;Vigouroux, 1879) and was consolidated as a robust
research technique in the rst half of the 20th century
(Landis, 1932;Prideaux, 1920).
Electrodermal activity can be measured non-invasively
by applying a low electric potential in two points of the skin
e.g., hands, feet, wrist (Benedek & Kaernbach, 2010;
Fowles et al., 1981), and it can be achieved by an endo-
somatic recording (i.e., data collection without an external
source of electricity) or exosomatic recording (i.e., constant
application of a voltage via electrodes; for a recent sys-
tematic review see Posada-Quintero & Chon, 2020). Tra-
ditional so-called polygraph systemscan record EDA.
Nevertheless they are highly susceptible to muscle move-
ment, cable sway, and some might be uncomfortable to
wear. The wearable/mobile versions of EDA (Poh et al.,
2010) allow measurements on a variety of natural situations
as sensors can be located in real-world objects such as
clothing (Howell et al., 2016;Kappeler-Setz et al., 2013)
and wristbands (OHaire et al., 2015). Thus, EDAs portable
and wearable nature makes it an attractive candidate for 4E-
based and social interaction research with all sorts of
populations (Hernandez et al., 2014).
Equally relevant for 4E-based research are visceral
signals (Azzalini et al., 2019;Critchley & Harrison, 2013;
Thompson & Varela, 2001) such as the electrocardiogram
(ECG, Waller, 1887) and electrogastrogram (EGG, Alvarez,
1922). The hearts electrical activity recorded over time is
known since the late 19th century (Luciani, 1873;Waller,
1887;Wenckebach, 1899) while gastroenteric dynamics
were rst described early in the 20th century (Alvarez,
1922;Tumpeer, 1926).
EKG is a non-invasive and commonly used technique to
record and visualize electrical activity from the heart by
presenting the series of waves related to the electrical im-
pulses of each heartbeat (Al Rasyid et al., 2016). EKG
allows describing heart rate and heart rate variability, tools
for the evaluation of autonomic nervous system and it has
been associated with different cognitive functions (Forte
et al., 2019). EKG presents similar limitations to EDA.
Holters and Event Monitors are the most commonly used
methods to monitor and measure heart activity, but they
have disadvantages or limitations such as size, cable dis-
position, time of recording, possible skin irritation (due to
hydrogel-based electrodes), among others (L´
azaro et al.,
2020). Researchers have been working to develop more
portable devices allowing comfortable longitudinal re-
cordings (Ehnesh et al., 2020;Iskandar et al., 2019;L´
et al., 2020). Examples of these efforts include a chest belt
with wireless embroidered dry electrodes (i.e., textile
electrodes) with a water reservoir guaranteeing sensor hu-
midity for longer periods of time (Weder et al., 2015) or the
development of a wearable armband that allows monitoring
and recording ECG for long periods of time by using hy-
drophobic dry electrodes to ensure an accurate wireless
measurements (L´
azaro et al., 2020). These developments
are relevant for 4E research programs as cardiac-related
dependencies have been found in several domains including
visual (e.g., visual search, Galvez-Pol et al., 2020; micro-
saccades, Ohl et al., 2016) and somatosensory perception
(Al et al., 2020), among others (for a recent review see
Azzalini et al., 2019). Furthermore, another signal that is
gaining psychophysiological interest are respiratory dy-
namics, since it is bidirectionally coupled to cardiac
rhythms (Dick et al., 2014;Kralemann et al., 2013) and
dependencies on perception and behavior have been sug-
gested (Kay et al., 2009;Perl et al., 2019) yet remain to be
Similar to EKG, EGG non-invasively measures gastric
myoelectrical activity using cutaneous electrodes placed on
the abdomen. It is a fundamental biorhythm and its mea-
surement allows understanding the control, timing, and
propagation of gastric peristaltic contractions; crucial di-
gestive dynamics (Koch & Stern, 2003). Electrogastrogram
has been recently re-discovered for psychophysiological
research (Davis et al., 1957;Wolpert et al., 2020). In EGG
recordings, other physiological rhythms such as respiration
and EKG can be observed nested in the signal (Koch &
Stern, 2003) and should be timed into experimental para-
digms instead of modeling out or using artifact reduction
techniques for their removal. Since EGG has not yet become
a mainstream signal for cognition research, not many de-
velopments of mobile/portable technology have been made.
Nevertheless, as evidence accumulates, visceral dynamics
will become ever more relevant for the 4E research agenda,
as are characterized by their pacemaker nature (Babo-
Rebelo & Tallon-Baudry, 2018). Furthermore, evidence
indicates that body physiology is nested and intertwined
with brain dynamics (Azzalini et al., 2019;Babo-Rebelo &
Tallon-Baudry, 2018;Pezzulo et al., 2019;Wolpert et al.,
Mobile functional neurovascular dynamics
Based on the premise of neurovascular coupling
(Carmignoto & Gómez-Gonzalo, 2010), the measurement
Grasso-Cladera et al. 7
of specic changes in blood concentration/levels of dif-
ferent substances has become a reliable proxy for changes in
the metabolic activity of the nervous system. At a global
level, fMRI is commonly used to obtain structural and
functional images of the brain. In Chile, only a few research
institutions have access to the technology, mostly linked to
hospital settings. All neurovascular acquisition methods
require participants to lie on their backs in a scanning
device, which violates most of MoBI and 4E requirements.
In contrast to electrophysiology, mobile hemodynamic
technologies have achieved only a modest level of devel-
opment. In this section, we review the most promising
efforts towards the MoBI/4E goal of obtaining structural
and functional images of the brain in ecologically valid
Functional magnetic resonance imaging
Since its conception in the mid-1990s, fMRI rapidly became
the most popular neuroimaging method since it seemingly
posibilited the determination of the operations carried out by
the various brain areas (Posner et al., 1988). Similar to other
imaging methods, its function is based on the neurovascular
coupling premise; the fact that brain metabolism changes
with cognitive function (Bandettini et al., 1992). This idea
was rst explored by the Italian physiologist Angelo Mosso
in the late 19th century (Sandrone et al., 2012). Accordingly,
there are hemodynamic changes related to cellular activa-
tion after stimulation, which generates changes in the blood
oxygen level, known as Blood Oxygen Level-Dependent
signal (de la Iglesia-Vay´
a et al., 2011;Logothetis, 2002).
The past 5 years have seen considerable interest in de-
veloping a mobile and low-cost system of magnetic reso-
nance imaging for medical reasons. Prototype devices based
on the aforementioned SQUID technology have achieved a
degree of portability, but at a high cost (Espy et al., 2015). A
more affordable approach has been based on the use of
specically designed electromagnets (Sarracanie et al.,
2015). However, these devices, known as ultra-low eld
MR, still require participants to lie down while data is
obtained (Thibault et al., 2014), which for most situations is
contrary to the 4E research program. For such reasons,
fMRI technology is still far from fullling the requirements
of the MoBI/4E framework and we are not aware of any 4E-
inspired functional hemodynamics work (nevertheless, for a
review on some attempts to use fMRI in naturalistics set-
tings see Tikka & Kaipainen, 2014).
Positron emission imaging
Positron Emission Imaging (PET) is a research method
based on the radioactivity detection emitted by a radioactive
tracer injected into the peripheral vascular system (e.g.,
intravenous injection of Fluorine-18 which emits a positron
and collides with a tissues electron generating a process of
conversion into photons that will be absorbed by crystals
and produced a light turn into an electrical signal, A. Berger,
. This method was widely used in the 1980s to obtain
functional images at high spatial resolution (Cabeza &
Nyberg, 1997). However, given the fact that participants
had to be subjected to a small dose of radiation (A. Berger,
2003), this method is considered invasive and was widely
replaced by fMRI in the 1990s. Nevertheless, its ability to
detect ligands makes PET a powerful tool in both a clinical
context and scientic context (A. Berger, 2003;Phelps,
2000). Therefore, the invention of a mobile PET system,
requiring only a low dose of radiation (known as low-dose
PET) has considerable value. A close implementation of
such a device is the Rat Conscious Animal PET (RaTCAP),
which allows PET in rats during movement (Schulz &
Vaska, 2011). As it is composed of a miniature high-
performance PET scanner and complex mechanical
methods to attach the scanner to the rat while allowing
movement, RaTCAP is not suitable for human cognition
research. Nevertheless, RaTCAP adaptations have been
attempted for human research. Examples include the PET-
Hat prototype (Yamamoto et al., 2011) and the portable PET
(Yamaya et al., 2015). However, due to the size of the
sensors used, these systems cannot yet be considered
mobile.A more promising prototype known as ambu-
latory microdose PET (Melroy et al., 2017) uses microdoses
to reduce the quantity of radiation to 75 - 90%. This system
is effectively mobile as it is lightweight and allows restricted
movement. But just like PET-Hat and mobile PET, it is still
in development and still far from becoming appropriate
methods for advancing MoBI/4E research programs. In
Chile, traditional PET research is virtually non-existent.
Functional near-infrared spectroscopy
Another technique with major promise for the MoBI/4E
framework is called functional near-infrared spectroscopy
(fNIRS). This non-invasive imaging method uses the pro-
jection and reection of near-infrared light to quantify
changes in the concentration of blood hemoglobin (Strait &
Scheutz, 2014;Villringer et al., 1993). Functional near-
infrared spectroscopy sensors are small and lightweight so
they can be used in mobile equipment to measure cortical
activity during movement (Miyai et al., 2001;Quaresima &
Ferrari, 2019;Suzuki et al., 2008). Furthermore, fNIRS can
be used in combination with other neuroimaging systems,
such as fMRI and EEG (Pouliot et al., 2012). In Chile,
several institutions have access to fNIRS technology with
few research outcomes.
The main disadvantage of fNIRS, as with other tech-
niques based on neurovascular coupling, is that temporal
resolution is in the order of seconds and therefore not as
optimal for the study of temporal aspects of cognitive
8Adaptive Behavior 0(0)
processes (Cohen, 2011;Irani et al., 2007;Makeig et al.,
2009). Nevertheless, given its non-invasiveness, tolerance
to bodily movements, high portability, and cross-population
usage (i.e., can be used from newborns to the elderly), the
future combination of mobile EEG/fNIRS holds great
promise for advancing MoBI/4E research programs (Pinti
et al., 2020). Thus, coupling fNIRS with EEG will be highly
benecial for any 4E-based research program as it provides
researchers both temporal and spatial resolutions with
portable/mobile setups (Pinti et al., 2015;2020;Piper et al.,
2014;Quaresima & Ferrari, 2019;Shin et al., 2018). Even
though most experimental data comes from hyperscanning
EEG (for reviews see Czeszumski et al., 2020; and
Konvalinka & Roepstorff, 2012), some studies have used
fNIRS (for reviews see Konvalinka & Roepstorff, 2012; and
Pinti et al., 2020), and fewer have combined both (Balconi
& Vanutelli, 2016;Biessmann et al., 2011;Rosenbaum
et al., 2020). A fairly recent example is Balconi and
Vanutelli (2016). They manipulated social context using
a sustained attention task (basically, a very simple inter-
personal competitive game) while measuring EEG/fNIRS.
Among other things, their results suggest that even though
combining methods may sound like a solution for the 4E
research programme, there still is a wide methodological/
analytical gap for interpreting and understanding high-
dimensionality neural data. We will return to this point in
the Discussion section.
Mobile behavioral measurement
Mobile behavioral measurement has achieved a break-
through in the past 20 years thanks to the development of
high-resolution digital sensors, which are also inexpensive
and portable. In Chile, several institutions have access to
movement laboratories equipped with motion capture
(MOCAP) systems. In MOCAP systems, motion is captured
using several cameras which track the position of reective
markers (e.g., active light emitting diode (LED)) placed near
the wearers joints to compute the appropriate kinematics
(Gwin et al., 2010). Although still expensive, the MoBI/4E
framework could also benet from MOCAP technologies.
However, because of the positioning of the sensors and the
multiplicity of cameras required, MOCAP tends to be viable
only in specic laboratory settings (Gramann et al., 2021;
Gwin et al., 2010). Recently, the usage of user-grade
technology has allowed acquisition of movement data us-
ing more affordable solutions (Galbusera et al., 2019;
Tschacher et al., 2014). Movement laboratories equipped
with motion capture technology is regularly used in physical
rehabilitation research to analyze the effectiveness of ap-
propriate therapy plans (Alarcón-Aldana et al., 2020),
sometimes even combined with virtual reality (Holden,
2005). Movement laboratories equipped with motion cap-
ture can be combined with any neuroimaging technology
that allows movement. Recently, EEG has been the most
used technology within the MOCAP context (Gramann
et al., 2021;Makeig et al., 2009). As opposed to the
classical behavioral paradigms (e.g., Go/NoGo) where
participants sit in front of a computer and button presses are
recorded as behavior, MOCAP-aided functional neuro-
imaging studies can capture multi-joint motions of the body
in 3-D space while simultaneously measuring brain activity
underlying those movements. For example, the integration
of MOCAP and EEG has been used in gait analysis ex-
periments to derive pathological neuromarkers of multiple
sclerosis which could be used to improve the assessment of
the disorder (De Sanctis et al., 2020). Another novel area of
research using this integration within a MoBI/4E framework
is the neuroscience of performing arts. Recently,
Barnstaple et al., (2020) were able to analyze real-time
changes in brain dynamics while participants learned and
performed a choreographed dance, opening the door to
further highly ecologically valid studies investigating
complex activities in real-world-environments.
In the next section, we discuss further technological
advancements in the measurement of behavioral dynamics,
which can also be used in research contexts where move-
ment is involved.
Audiovisual digital recordings
High-resolution and low-cost digital sensors, including
mass-consumption devices such as the Nintendo Wii remote
(Attygalle et al., 2008), Microsoft Kinect (Galbusera et al.,
2019), and digital video cameras (Sigal et al., 2009;
Sundaresan & Chellappa, 2005) can be easily incorporated
into laboratories for measuring multiple angles of move-
ment. In addition, the capture of video footage allows an
analysis of the interaction and dynamics between partici-
pants in relevant contexts (Tschacher et al., 2014). Novel
software solutions mean that motion analysis can also be
performed using consumer camera systems, making ana-
lyses of movement more accessible and easy to implement
in highly ecological contexts. For example, the motion
energy analysis (MEA) algorithm quanties the differences
between consecutive frames and then sums the number and
amount of pixel change in a predetermined region of interest
(or the whole frame) obtaining as a result a time-series of
this quantication (Ramseyer, 2020). There are several
parameters to consider before result interpretation such as
video resolution, the size of the region of interest and the
recording noise-to-signal ratio (Ramseyer, 2020;Ramseyer
& Tschacher, 2011).
Motion energy analysis allows the automatic identi-
cation of nonverbal synchrony (movement coordination)
rather than using the manual rating technique by an observer
(Ramseyer & Tschacher, 2011). Given its simplicity MEA
provides an inexpensive solution for data acquisition in
Grasso-Cladera et al. 9
ecological settings and can be used in combination with any
neuroimaging technique, in any type of data acquisition
setup, and species (Stringer et al., 2019). Due to these
advantages, MEA is already used in psychotherapeutic
contexts (Ramseyer, 2020;Ramseyer & Tschacher, 2011;
Tschacher et al., 2014). In psychotherapy, a therapist fre-
quently meets with a patient/client, developing a profes-
sional relationship based on standard therapeutic
interactions (Krause et al., 2007;Mart´
ınez, 2011). Such
natural social interactions can be longitudinally recorded,
in-situ, effectively extending the laboratory (Parada, 2018).
In Chile, this methodological approach has been used in
conjunction with EEG to study the psychotherapeutic
process and associated brain dynamics (Parada et al., 2018;
ıguez et al., 2018).
Furthermore, through the use of high-resolution audio-
visual systems, body movements and non-verbal commu-
nication data can be obtained and analyzed alongside
vocalization and verbal interaction between participants.
Voice perception is a generally forgotten dimension in
cognitive science, but it has an important role in under-
standing cognition in its natural state (Campanella & Belin,
2007). In Chile, there has been only one study on the use of
voice in the context of psychotherapy (Tomicic et al., 2015).
Therefore, behavioral and verbal dynamics are a future
niche for development in the MoBI/4E framework
(Goregliad Fjaellingsdal et al., 2020).
Eye-tracking techniques offer a simple and non-invasive means
of investigating cognitive and socio-emotional phenomena. Due
to their ease of use, and relatively simple set-up, eye-tracking
technology applications have been widely developed and used
in recent decades (Alemdag & Cagiltay, 2018;Duchowski,
2007a;Kredel et al., 2017;Parada et al., 2015;Pieters & Wedel,
2017). Eye Tracking is based on the measurement of corneal
reection due to an infrared LED that illuminates the eye surface
(Cooke, 2005). This reectionismeasuredinrelationtothe
pupil center (Duchowski, 2007b). Thus generating reections
that are captured by a camera and then used to establish the
reection of the light in the cornea and the pupil (Farnsworth,
2019). Then, it is possible to calculate a vector of the cornea-
pupil reection angle and to relate this information with some
dimension of the world in order to estimate what the person was
looking at. There are several types of Eye Tracking devices
ranging from traditional setups to mobile and wireless tech-
nologies (Cooke, 2005;Richardson & Spivey, 2004). Research
using eye-tracking seeks to provide ecologically valid experi-
mental designs (e.g.,Aryadoust & Ang, 2019;John et al., 2018;
Kredel et al., 2017) as well as high-precision measurements with
good spatial and temporal sensitivity. The development of ro-
bust mobile technology, capable of capturing eye movements as
participants actively interact in natural and virtual environments,
has allowed researchers to address several questions pertinent to
the 4E perspective (Dowiasch et al., 2015;2020;Fong et al.,
2016;Macdonald & Tatler, 2018;Palacios-Garcia et al., 2020;
Stuart et al., 2018;Wohltjen & Wheatley, 2021). Recently,
Wohltjen & Wheatley (2021) explored hyperscanning eye-
tracker during naturalistic conversations, opening the door for
social interaction and other meaningful 4E-based research
paradigms. Future functional neuroimaging studies combined
with eye-tracking technology will further improve the MoBI/4E
research program. Given that eye-tracking technology is widely
available and affordable, there are already several institutions in
Chile using such systems in lines of research ranging from
marketing to cognitive/neuroscience.
Mobile brain/body imaging/4E in Chile
Chilean human neuroscience of social phenomena research
began in the second half of the 2000s, with the opening of
human electrophysiology laboratories at the Universidad
Diego Portales in 2006, Universidad De La Frontera in
2007, and, later, others. The original conguration of these
laboratories was inuenced by the traditional cognitive
science paradigm (Figure 1). Recently, there has been a
gradual shift in approach as Universidad Diego Portales
and the Ponticia Universidad Católica de Valpara´
ıso in
2016, Universidad de Chile in 2017, and Universidad de
Concepción in 2018 acquired mobile and portable equip-
ment for novel research contexts. However, this has yet to
result in the establishment of high-impact research pro-
grams at domestic and international levels. One reason for
such delay is the complexity of carrying out a research
program based on the 4E perspective, implemented through
MoBI technologies. Having updated the reader on the main
theoretical and technological advances that have made
MoBI/4E research possible, in what follows, we discuss the
main challenges and opportunities of its implementation and
their impact on the development of 4E research. These
challenges are not only pertinent for Chilean research but
are also relevant at a global level.
Main challenges and opportunities in the
implementation of research programs
based on MoBI/4E
The current theoretical/technological status for the study of
human cognition, enables researchers to explore questions
that might have been considered science ction in the recent
past. Today, behavioral and physiological activity data
acquired from one or more agents in motion can be collected
simultaneously (Czeszumski et al., 2020;Ladouce et al.,
2017;Shamay-Tsoory & Mendelsohn, 2019). However,
these technologies are far from easy to use as they are
not plug & play. Below, we discuss the most relevant
factors that, in our opinion, represent both challenges and
10 Adaptive Behavior 0(0)
opportunities when obtaining functional images of the
brain/body-in-the-world system.
Specic technical requirements for the
acquisition of mobile signals
About consumer-grade versus
research-grade systems
As reviewed in the previous sections, various types of
mobile brain/body measurement technology are currently
available. Electroencephalogram with its low-cost and high
portability has acquired a status of consumer technology
and has begun to be used in recreational applications
sius & Damaˇ
cius, 2016). Such low-cost systems
have also been adapted for research purposes (Badcock
et al., 2013). The majority of consumer-grade systems work
well in situations of high SNR, such as ERP paradigms
(Badcock et al., 2013;Debener et al., 2012). However, in
low-SNR/high-uncertainty situations, such as real-world
conditions, many do not perform as well as expected.
During 2018, our research group monitored eye movements
(using Tobii Glasses 2 EyeTracker), EEG signals (using the
EnoBio 8, Rufni et al., 2007), spatial positioning (using
global positioning system, GPS), and ambient noise while
participants walked a predened 600-m route in different
neighborhoods of Santiago de Chile (Palacios-Garcia et al.,
2020;Parada et al., 2019). However, under these real-world
low-SNR conditions, the captured EEG signals were not
sufcient to answer our initial research questions
. When
another system was used (ANTneuro EEGO), the EEG
signals seemed more robust (Figure 3). Thus, following the
procedures applied by Radüntz (2018), we evaluated signal
quality in order to quantify our sense of signal usabilityin
these dry-sensor EEG systems during a semi-structured
real-world situation. This experience conrms that (i) in
low-SNR situations, it is better to use as many sensors as
possible (as suggested by Gwin et al., 2010) and avoid
consumer-grade equipment by using systems exclusively
designed for research (conclusion reached by Mathewson
et al., 2017)
, and (ii) dry EEG electrodes might need further
development in order to achieve signal quality required for
research purposes (conclusion reached by both
Oliveira et al., 2016; and Radüntz, 2018).
About system synchronization
Online equipment synchronization is an often overlooked
crucial factor. In traditional settings, this is a minor problem
since (i) multiple simultaneous measurement systems are
not generally used and (ii) when multiple systems are used,
equipment tends to be static and connected by cables, so
when properly congured enable online or ofine syn-
chronization. However, MoBI requires minimal cabling to
ensure mobility. Currently, the best way to synchronize data
sources is through the Lab Streaming Layer (LSL, Kothe,
2019). Lab Streaming Layer was our method of choice
when implementing the Human Interaction Laboratory at
Universidad Diego Portales in order to study social inter-
action with a multiple-participant MoBI system. Although
other options for signal synchronization might be available,
in our experience, these are unsatisfactory as they might
lead to measurement errors. If multiple data sources are to
be sampled simultaneously, the synchronization software
implementation must be rigorous. The systems used must be
properly synchronized by software and hardware that is
specically designed for such a task (Barraza et al., 2019).
Specic requirements for experimental
designs and the physical space that
they require
As mentioned previously, cognitive/neural science labora-
tories in Chile were set up following the logic of traditional
cognitive science (Figure 1 bottom). Implementing MoBI/
4E research programs requires not only new research hy-
potheses but also new physical spaces. In 2018, our research
group suggested the idea of scalable experimental design
(EED) as a heuristic methodological tool (Parada, 2018).
Figure 3. Signal-to-noise ratio (SNR) on different EEG systems.
Upper panel displays median SNR values over all sensors in 2
different EEG dry-electrode systems. Lower panel shows median
SNR values at each sensor in topographical representation.
Grasso-Cladera et al. 11
Similar ideas have been proposed by other research groups
(King & Parada, 2021;Matusz et al., 2019;Shamay-Tsoory
& Mendelsohn, 2019). The central concept behind the EED
heuristic is the active integration of traditional (i.e., struc-
tured) experiments with MoBI experiments, where partic-
ipants can freely move within a dened context (i.e.,
structured/semi-structured, e.g.,Jungnickel & Gramann,
2016) and real-world situation (i.e., semi-structured/
unstructured, e.g.,Cruz-Garza et al., 2017). Experimentos
Escalables en su Diseño applies the idea of scalable com-
plexity to the study of cognition (Gramann et al., 2021;
Ladouce et al., 2019). This scalability depends on formu-
lating hypotheses in such a way that allows testing phys-
iological markers both in traditional laboratory conditions
and in MoBI experiments and real-world experimental
situations (Gramann et al., 2021;Ladouce et al., 2019;
Piñeyro Salvidegoitia et al., 2019;Soto et al., 2018). Thus,
the EED heuristics has four main objectives: (i) to inves-
tigate the extent to which cognitive phenomena induced in
the laboratory can be considered an artifact product of the
experimental design (Bronfenbrenner, 1977), contributing
directly to the empirical development of the 4E perspective,
(ii) to replicate known effects in conditions of greater
ecological validity (as in Debener et al., 2012;De Vos et al.,
2014;Gramann et al., 2021;Soto et al., 2018), (iii) to
systematize the extension of the laboratory setting into the
real world (Parada, 2018;Parada & Rossi, 2018), and (iv)
increasing the interpretability of results obtained under real-
world low-SNR conditions through inherent replicability.
A further innovation that supports the MoBI/4E ap-
proach is the possibility of acquiring longitudinal signals
from participants in everyday situations, enabling re-
searchers to study brain dynamics in their natural state
(Bleichner & Debener, 2018;H¨
olle et al., 2020;Parada,
ın, et al., 2018;Parada & Rossi, 2020;Rodr´
ıguez et al.,
2018). The development of novel hypotheses relevant to
this type of dataset will be characterized by low SNR,
virtually unlimited hours of recording, multiple data sources
(e.g., EEG, eye-tracker, and electrocardiography) and high
heterogeneity among participants. Data integration relating
to phenomenological aspects, such as lifestyle (e.g., ana-
lyses of activities of daily living) and/or experiences (e.g.,
discourse analysis) will be used to interpret, parse, and/or
segment the acquired signals (Parada & Rossi, 2018,2020).
The experimental implementation of an MoBI/4E re-
search program is impeded not only by theoretical aspects of
experimental designs but also by the physical layout of the
space provided for traditional experimental setups. Thus,
the implementation of new spaces for laboratories and/or the
ability to exibly recongure current research spaces will be
necessary (see Gramann, 2018 for a feature on the Berlin
Mobile Brain/Body Imaging Lab, BeMOBIL - https://www.
Klaus_Gramann.pdf). This represents a signicant
economic and strategic challenge for researchers and re-
search institutions in Chile and elsewhere.
Specic hardware and software
requirements for mobile signal analysis
Acquiring MoBI data in semi-structured and unstructured
research contexts represents a challenge both at the hard-
ware and software level, while the visualization and analysis
of such data can be even more complicated. In such cases,
the computer science expression garbage in, garbage out
is truer than ever, especially if we consider that SNR is
reduced in contexts of major movement (Gwin et al., 2010)
and depends directly on the quality of the systems used
(Radüntz, 2018). Thus, researchers working with the 4E
perspective will need to use high-quality hardware (Nordin
et al., 2018;Radüntz, 2018), implement state-of-the-art
computational algorithms (Blum et al., 2019;Gramann
et al., 2021;Gwin et al., 2010;Klug & Gramann, 2020),
and consider the EED heuristic when designing research
projects (Matusz et al., 2019;Parada, 2018;Shamay-Tsoory
& Mendelsohn, 2019). Some of the greatest advances re-
garding software have been achieved by open-source ini-
tiatives such as MOBILAB to visualize and analyze MoBI
data (Ojeda et al., 2014) or the HyPyp pipeline for analyzing
hyperscanning data (Ayrolles et al., 2021). Given the het-
erogeneity of designs and data sources, it is important to
emphasize that the MoBI community lacks standards or
agreements on how to analyze and/or visualize data.
However, it is important to consider that more than 90 years
after the invention of the EEG, there is still no unied
approach to the analysis and interpretation of signals ob-
tained under traditional conditions (Cohen & Gulbinaite,
2014). The edgling MoBI/4E approach should scaffold its
growth in nascent unifying efforts to create globally ac-
cepted data-sharing and managing standards (Pernet et al.,
Concluding remarks
In recent years, cognitive science has made signicant
progress in understanding how the mind works, especially
through the identication of various cognitive and neural
subsystems and their structural, functional, and connectivity
dynamics (Shine et al., 2019;Swanson et al., 2020;Zamani
Esfahlani et al., 2020). By establishing relationships be-
tween different brain dynamics involved in cognition,
classical laboratory experiments have been of great value.
But despite the validity (i.e., controlling relevant variables)
and reliability of the data they produce, these experimental
designs tend to lack ecological validity. Likewise, without
investigating the dynamics of the brain/body system in
interaction with the world it is not possible to study cog-
nition in its natural state. The use of recently developed
12 Adaptive Behavior 0(0)
technologies under the paradigm proposed by MoBI can be
seen as a possible solution to this problem, allowing the
realization of controlled experimental research outside the
laboratory. However, this new epistemic/methodological
approach comes with its own challenges, from the need
for greater portability and robustness of equipment to a
requirement for data-analysis tools that can integrate myriad
signals from the brain/body-in-the-world system. Younger
researchers will have to embrace transdisciplinary ap-
proaches, managing both theory and methodology at a
sufcient level of expertise so they can collaborate with
each elds disciplinary experts. Additionally, future ex-
perimental designs need to allow and consider parametri-
cally increased ecological validity, through the modication
of physical spaces in which experiments are conducted and
the logic underlying experimental designs (Parada, 2018).
Furthermore, correct hardware and software im-
plementation avoids gathering data that will have limited or
no usability, will waste participantstime, and allocated
research funding. Hence, appropriate experimental design
and data acquisition are certainly a matter of research
Novel hardware and software developments also present
an opportunity to improve diagnosis and treatment strate-
gies for neuropsychiatric conditions, allowing patients to
acquire and manage information about their own health
status in lower cost, non-invasive manners (Goverdovsky
et al., 2017;Piwek et al., 2016) and potentially establishing
a direct and continuous link with their health professionals.
It should be noted that questions relating to both the clinical
validity and ethical implications of asking patients to in-
corporate wearable and/or hearable technologies in their
daily lives still persist, some have identied the need for the
determination of neuro-rights(Sommaggio et al., 2004;
Viosca, 2018).
Thus, challenges are accompanied by opportunities such
as (i) the possibility of identifying and diagnosing neuro-
psychiatric diseases in a non-invasive way at a reduced cost
and (ii) acquiring multiple signals from participants in
everyday situations, thereby expanding our range of re-
search hypotheses and theoretical conceptualizations about
the phenomena we want to understand
. In sum, we are at a
key moment to advance as a research community by solving
these challenges and moving towards a cognitive science
with greater ecological validity; one that does not conne
the study of cognitive processes to understanding intrace-
rebral phenomena, but instead integrates laboratory and the
outside world, taking full account of the organisms body
and environment in which cognition arises.
Authors contributions
FJP conceptualized and wrote the rst draft. AGC and FJP made
the gures. AGC and SCC edited and wrote the manuscript. AGC,
SCC, AR, NFF, and FJP wrote and edited the nal version of the
Declaration of conicting interests
The author(s) declared no potential conicts of interest with re-
spect to the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following nancial support
for the research, authorship, and/or publication of this article: The
present work was funded by Agencia Nacional de Investigación y
Desarrollo (ANID) del Ministerio de Educación del Estado de
Chile, through Fondo Nacional de Desarrollo Cient´
ıco y Tec-
nológico (FONDECYT) Iniciación en Investigación program,
project N
11180620 awarded to FJP. AR, SCC, AGC, and FJP
receive funding from Fondo Nacional de Desarrollo Cient´
ıco y
Tecnológico (FONDECYT) regular, project N
1190610 awarded
to AR and FJP. AGC receives additional funding from Programa de
ıster en Neurociencia Social, Facultad de Psicolog´
ıa, Uni-
versidad Diego Portales and from FONDECYT Iniciación en
Investigación program, project N
11180620 awarded to FJP.
Francisco J Parada
1. We understand the extended principle of cognition in a broad
sense. This is, in time, the agents being-in-the-world affords
meaningful interactions and relational possibilities for action
within natural, built, and/or digital environments. Certain as-
pects of these environments (physical and/or sociocultural) can
be incorporated into larger arcs of contextualized perception-
action spatio-temporal couplings, which will be mechanically
and constitutively linked to experience. This broad under-
standing is closer to the enactive approach denition (Di Paolo,
2009) as we frame both the distributed cognition (Hutchins,
1995) and the extended mind/active externalism (Clark &
Chalmers, 1998) notions within an interaction/emergentist
view. It is important to emphasize that Enaction is not equal
to Interactivism (De Jaegher & Di Paolo, 2012).
2. i.e. Cognition is an Embodied,Extended, and Embedded
phenomenon, better studied using the Enactive approach.
3. Even though the simplest and most common interpretation of
these measures is within an heteronomous information-
processing framework, in reality, cognitive neurodynamics
are far more complicated. The range of oscillatory variability
within and between participants (Croce et al., 2020;Haegens
et al., 2014;Rousselet et al., 2007,2011) and the failure to nd
any real functional specialization of specic brain areas (Grill-
Spector et al., 2006;Poldrack, 2006), reects the complexity of
a signal that summarizes the cumulative history of an agent;
from genetic to psychological (Anderson, 2010;Aurlien et al.,
Grasso-Cladera et al. 13
2004). Thus, carefully understanding neurophysiological sig-
nals suggests that they are far from being reections of intrinsic
information processing capabilities of the brain, but situated
products of their underlying synaptic organization which is a
direct consequence of prior learning, past experiences, psy-
chophysiological states, and present goals (Freeman, 1997,
1999). Providing interpretations of cognitive neurodynamics
within the 4E perspective largely exceeds our current scope.
Nevertheless, we are certain that the 4E perspective and the
subeld of cognitive electrophysiology, can mutually benet
and that any future integration efforts will mean great ad-
vancement to both elds. Cognitive electrophysiology can help
4E-based research to quantify -for example-integrative neu-
rodynamics in the context of developmental plasticity or
tracking the many-to-many brain network-to-cognitive function
mapping through so-called biomarkers across interaction do-
mains (e.g., from simulated social interactions to real and
engaged social interactions). Furthermore, the 4E perspective
will provide a truly biogenic (and much needed) approach to
the understanding of the electrophysiological dynamics of
4. In contrast, Single-Photon Emission Computed Tomography
(SPECT) uses gamma rays for the generation of the signal
(Knoll, 1983). The procedure involves the administration of
radioactive-labeled pharmaceuticals by injecting the patient
(usually a radionuclide that emits gamma ray photons) which
will be distributed according to tissue types and provide in-
formation of the spatial concentration of the compound. SPECT
offers a broader range of applications due to the long life of the
isotopes, their reduced costs (Knoll, 1983;OConnor & Kemp,
2006;Seo et al., 2008). Similarly to other imaging methods,
SPECT is far from being developed as a truly mobile tech-
nology (Studenski et al., 2007).
5. Further data collection using our newer, most robust setup was
interrupted by the 2020 COVID pandemic and we hope it will
be possible to continue in 2022.
6. The difference between consumer-grade and research-grade
systems comes from several sources, from cable quality to
amplier electronics and properties. Nevertheless, we think the
most important thing here is sensor quality. This is, during
mobile data acquisition, sensors should keep good and constant
electrical conductance with the scalp, minimizing sensor
movement. Given the fact that dry sensors do not use a con-
ductive substance, signal quality will be heavily reduced if
the sensors conductive materialfor technical or design
reasonsdoes not ensure proper electrical conduction and/or is
prone to wiggle. Nowadays we can distinguish two types of dry
sensor: the pin-type and the at-type. Pin-type sensors will
allow contact between scalp and electrode by passing through
hair, and therefore will need high-pressure on the scalp and are
therefore a little bit uncomfortable for longitudinal recordings.
In contrast, at-type sensors need a hair-free surface for proper
conduction and therefore can only be used in the forehead, face
or a prepared surface. Novel materials (such as conductive
polymer) allow for exible, softer pin-type sensors (bristle-like)
that can be less susceptible to movement. For the reported
systems in the present article, apart from amplier and cable
electronics sensor technology, material, and morphology is very
different and may account for our disparities in data quality (we
will not be explicit about these aspects as we do not want to
explicitly endorse or criticize any of these EEG systems).
7. For static, controlled, laboratory conditions, EEG systems with
gel-based, liquid-electrolyte, and dry-sensor conductance all
performed comparably well (Hinrichs et al., 2020;Schwarz
et al., 2020). A considerable susceptibility to movement and
sweating artifacts is expected when using dry electrodes in
natural conditions (Leach et al., 2020). Similar systematic
testing is required for gel-based, liquid-electrolyte, dry, and
semidry sensors in mobile setups (for systematic evaluation of
other relevant dimensions see Klug & Gramann, 2020; and
Scanlon et al., 2021).
8. This rst opportunity is derived from the possibility that future
MoBI devices (such as C-grids, Debener et al., 2015) will be
unobtrusive and will probably be incorporated into the daily
activities of participants/users/patients as wearable/hearable
devices. These devices already provide continuous monitor-
ing of peoples activity and physiological dynamics (e.g., heart-
rate variability in consumer wearable technology, Hernando
et al., 2018). For example, in the future we will have people
who have proled their heart physiology for most of their adult
life. Health providers (apps or practitioners) could use this to
provide early diagnostics (Healey & Wong, 2019), test novel
interventions (King & Parada, 2021), evaluate environmental
factors of health and disease (Helbig et al., 2021), and/or
monitor treatment outcomes (Gresham et al., 2018). As was
mentioned earlier, raising obvious ethical concerns.
9. This second opportunity allows asking the question: What does
a cognitive neuroscience experiment look like in the 21st
century? We think the MoBI/4E research program has to re-
conceptualize what a human neuroscience experiment looks
like, since it has to at least consider the role of the environment
and social interaction in the constitution of the mind. The rst
decade of MoBI and the future MoBI/4E integrative efforts
have begun to describe aspects (e.g., event-related potentials,
heartbrain and gutbrain dynamics) that will allow the future
creation of interactomes (Parada & Rossi, 2020), effectively
integrating brain-body-world connectomes (Gollo et al., 2018;
Puxeddu et al., 2020;Rojas-L´
ıbano & Parada, 2019) with
exposomes (Niedzwiecki et al., 2019). The monumental task of
integrating these aspects into data collection and analyses lie
Adams, F. & Aizawa, K. (2001). The bounds of cognition.
Philosophical Psychology,14(1), 4364.
Ahn, S., Cho, H., Kwon, M., Kim, K., Kwon, H., Kim, B. S.,
Chang, W. S., Chang, J. W., & Jun, S. C. (2018). Interbrain
14 Adaptive Behavior 0(0)
phase synchronization during turn-taking verbal interaction-a
hyperscanning study using simultaneous EEG/MEG. Human
Brain Mapping,39(1), 171188.
Ahonen, A. I., Hamalainen, M. S., Kajola, M. J., Knuutila, J. E. T.,
Lounasmaa, O. V., Simola, J. T., Tesche, C. D., & Vilkman,
V. A. (1991). Multichannel SQUID systems for brain re-
search. IEEE Transactions on Magnetics,27(2), 27862792.
Al Rasyid, M. U. H., Pranata, A. A., Lee, B.-H., Saputra, F. A., &
Sudarsono, A. (2016). Portable electrocardiogram sensor
monitoring system based on Body Area Network. 2016 IEEE
International Conference on Consumer Electronics-Taiwan
(ICCE-TW), Nantou, Taiwan, 2729 May 2016, pp. 12.
(2020). Upper limb physical rehabilitation using seri-
ous videogames and motion capture systems: A system-
atic review. Sensors,20(21), 5989.
Alemdag, E. & Cagiltay, K. (2018). A systematic review of eye
tracking research on multimedia learning. Computers &
Education,125, 413428.
Al, E., Iliopoulos, F., & Forschack, N. (2020). Heartbrain in-
teractions shape somatosensory perception and evoked po-
tentials. Proceedings of the National Academy of Sciences.
Alvarez, W. C. (1922). New methods of studying gastric peri-
stalsis. JAMA: The Journal of the American Medical Asso-
ciation,79(16), 12811285.
Anderson, M. L. (2010). Neural reuse: A fundamental organiza-
tional principle of the brain. The Behavioral and Brain
Sciences,33(4), 245313. discussion 266313. https://doi.
Aryadoust, V. & Ang, B. H. (2019). Exploring the frontiers of eye
tracking research in language studies: A novel co-citation
scientometric review. Computer Assisted Language Learn-
ing,34(7), 898933.
Attygalle, S., Duff, M., Rikakis, T., & He, J. (2008). Low-cost, at-
home assessment system with Wii Remote based motion
capture. 2008 Virtual Rehabilitation, Vancouver, BC, Canada,
2527 August 2008, pp. 168174.
Aurlien, H., Gjerde, I. O., Aarseth, J. H., Eldøen, G., Karlsen, B.,
Skeidsvoll, H., & Gilhus, N. E. (2004). EEG background
activity described by a large computerized database. Clinical
Neurophysiology: Ofcial Journal of the International
Federation of Clinical Neurophysiology,115 (3), 665673.
Ayrolles,A., Brun, F., Chen, P., Djalovski, A.,Beauxis, Y., Delorme,
R., Bourgeron, T., Dikker, S., & Dumas, G. (2021). HyPyP: A
Hyperscanning Python Pipeline for inter-brain connectivity
analysis. Social Cognitive and Affective Neuroscience,16(1
2), 7283.
Azzalini, D, Rebollo, I, & Tallon-Baudry, C (2019). Visceral
signals shape brain dynamics and cognition. Trends in
Cognitive Sciences,23(6), 488509.
Babiloni, F., Cincotti, F., Mattia, D., Mattiocco, M., De Vico Fallani,
F., Tocci, A., Bianchi, L., Marciani, M. G., & Astol, L. (2006).
Hypermethods for EEG hyperscanning. Conference Pro-
ceedings: ... Annual International Conference of the IEEE
Engineering in Medicine and Biology Society,2006,
36663669. IEEE Engineering in Medicine and Biology So-
Babo-Rebelo, M. & Tallon-Baudry, C. (2018). Interoceptive signals,
brain dynamics, and subjectivity. In The interoceptive mind:
From homeostasis to awareness (Vol. 46). https://books.
Badcock, N. A., Mousikou, P., Mahajan, Y., de Lissa, P., Thie, J., &
McArthur, G. (2013). Validation of the Emotiv EPOC
gaming system for measuring research quality auditory ERPs.
PeerJ,1(1), e38.
Balconi, M. & Fronda, G. (2020). The use of hyperscanning to
investigate the role of social, affective, and informative
gestures in non-verbal communication. Electrophysiological
(EEG) and inter-brain connectivity evidence. Brain Sciences,
10(1), 29.
Balconi, M. & Vanutelli, M. E. (2016). Competition in the Brain.
The contribution of EEG and fNIRS modulation and per-
sonality effects in social ranking. Frontiers in Psychology,7,
Bandettini, P. A., Wong, E. C., Hinks, R. S., Tikofsky, R. S., &
Hyde, J. S. (1992). Time course EPI of human brain function
during task activation. Magnetic Resonance in Medicine:
Ofcial Journal of the Society of Magnetic Resonance in
Medicine/Society of Magnetic Resonance in Medicine,25(2),
Barad, K. (2007). Meeting the Universe Halfway. Duke University
Barnstaple, R., Protzak, J., DeSouza, J. F. X., & Gramann, K.
(2020). Mobile brain/body Imaging in dance: A dynamic
transdisciplinary eld for applied research. The European
Journal of Neuroscience,54(12), 83558363.
Barraza, P., Dumas, G., Liu, H., Blanco-Gomez, G., van den
Heuvel, M. I., Baart, M., & P´
erez, A. (2019). Implementing
EEG hyperscanning setups. MethodsX,6, 428436. https://
Benedek, M. & Kaernbach, C. (2010). A continuous measure of phasic
electrodermal activity. Journal of Neuroscience Methods,190(1),
Grasso-Cladera et al. 15
Berger, H. (1929). Electroencephalogram in humans. Archiv Fur
Psychiatrie Und Nervenkrankheiten,87(1), 527570. https://
Berger, A. (2003). How does it work? Positron emission to-
mography. BMJ,326(7404), 1449.
Berthoz, A. (2008). Neurobiology of Umwelt: How living beings
perceive the world. Springer Science & Business Media.
Bhattacharya, R. (2002). C
aka fragments: A new collection.
Journal of Indian Philosophy.
Biessmann, F., Plis, S., Meinecke, F. C., Eichele, T., & Müller,
K.-R. (2011). Analysis of multimodal neuroimaging data.
IEEE Reviews in Biomedical Engineering,4,2658. https://
Bleichner, M. G. & Debener, S. (2018). Towards transparent EEG:
Ear-centered EEG acquisition using the cEEGrid. The
Journal of the Acoustical Society of America,143(3), 1744.
Bleichner, M. G., Mirkovic, B., & Debener, S. (2016). Identi-
fying auditory attention with ear-EEG: cEEGrid versus
high-density cap-EEG comparison. Journal of Neural En-
gineering,13(6), 066004.
Blum, S., Jacobsen, N. S. J., Bleichner, M. G., & Debener, S.
(2019). A riemannian modication of artifact subspace re-
construction for EEG artifact handling. Frontiers in Human
Neuroscience,13, 141.
Boehme, R. & Olausson, H. (2022). Differentiating self-touch
from social touch. Current Opinion in Behavioral Sciences,
Boto, E., Holmes, N., Leggett, J., Roberts, G., Shah, V., Meyer,
S. S., Muñoz, L. D., Mullinger, K. J., Tierney, T. M.,
Bestmann, S., Barnes, G. R., Bowtell, R., & Brookes, M. J.
(2018). Moving magnetoencephalography towards real-
world applications with a wearable system. Nature,
555(7698), 657661.
Braithwaite, J. J., Watson, D. G., Jones, R., & Rowe, M. (2013). A
guide for analysing electrodermal activity (EDA) & skin
conductance responses (SCRs) for psychological experi-
ments. Psychophysiology,49(1), 10171034. http://www.
Bronfenbrenner, U. (1977). Toward an experimental ecology of
human development. The American Psychologist,32(7),
Brunswik, E. (1943). Organismic achievement and environmental
probability. Psychological Review,50(3), 255272. https://
Bunge, M. (2014). The mindbody problem: A psychobiological
approach. Elsevier.
Cabeza, R. & Nyberg, L. (1997). Imaging Cognition: An Empirical
Review of PET Studies with Normal Subjects. Journal of
Cognitive Neuroscience,9(1), 126.
Calvo-Merino, B., Jola, C., Glaser, D. E., & Haggard, P. (2008).
Towards a sensorimotor aesthetics of performing art. Con-
sciousness and Cognition,17(3), 911922.
Campanella, S. & Belin, P. (2007). Integrating face and voice in
person perception. Trends in Cognitive Sciences,11(12),
Caricato, A., Melchionda, I., & Antonelli, M. (2018). Continuous
electroencephalography monitoring in adults in the intensive
care unit. Critical Care/the Society of Critical Care Medicine,
22(1), 75.
Carmignoto, G. & Gómez-Gonzalo, M. (2010). The contribution
of astrocyte signalling to neurovascular coupling. Brain
Research Reviews,63(12), 138148.
Chatterjee, A. & Vartanian, O. (2014). Neuroaesthetics. Trends in
Cognitive Sciences,18(7), 370375.
Chrastil, E. R. & Warren, W. H. (2012). Active and passive con-
tributions to spatial learning. Psychonomic Bulletin & Review,
19(1), 123.
Clark, A. & Chalmers, D. (1998). The extended mind. Analysis,
58(1), 719.
Cohen, M. X. (2011). Its about Time. Frontiers in Human Neu-
Cohen, M. X. & Gulbinaite, R. (2014). Five methodological chal-
lenges in cognitive electrophysiology. Neuroimage,85(Pt 2),
Colombo, M., Irvine, E., & Stapleton, M. (2019). Andy clark and
his critics. Oxford University Press.
Cooke, L. (2005). Eye tracking: How it works and how it relates to
usability. Technical Communication,52(4), 456463. https://
Craver, C. F. (2007). Explaining the brain: Mechanisms and the
mosaic unity of neuroscience. Clarendon Press. https://play.
Critchley, H. D. & Harrison, N. A. (2013). Visceral inuences on
brain and behavior. Neuron,77(4), 624638.
Croce, P., Quercia, A., Costa, S., & Zappasodi, F. (2020). EEG
microstates associated with intra- and inter-subject alpha
variability. Scientic Reports,10(1), 2469.
16 Adaptive Behavior 0(0)
Cruz-Garza, J. G., Brantley, J. A., Nakagome, S., Kontson, K.,
Megjhani, M., Robleto, D., & Contreras-Vidal, J. L. (2017).
Deployment of mobile EEG technology in an art museum
setting: Evaluation of signal quality and usability. Frontiers in
Human Neuroscience,11, 527.
Czeszumski, A., Eustergerling, S., Lang, A., Menrath, D., Ger-
stenberger, M., Schuberth, S., Schreiber, F., Rendon, Z. Z., &
onig, P. (2020). Hyperscanning: A valid method to study
neural inter-brain underpinnings of social interaction.
Frontiers in Human Neuroscience,14, 39.