The Aging Urban Brain: Analyzing Outdoor Physical Activity Using the Emotiv Affectiv Suite in Older People
J Urban Health
The Aging Urban Brain: Analyzing Outdoor Physical Activity Using the Emotiv Affectiv Suite in Older People
Chris Neale 0 1 2 3 4 5
Peter Aspinall 0 1 2 3 4 5
Jenny Roe 0 1 2 3 4 5
Sara Tilley 0 1 2 3 4 5
Panagiotis Mavros 0 1 2 3 4 5
Steve Cinderby 0 1 2 3 4 5
Richard Coyne 0 1 2 3 4 5
Neil Thin 0 1 2 3 4 5
Gary Bennett 0 1 2 3 4 5
Catharine Ward Thompson 0 1 2 3 4 5
0 G. Bennett The Stats People , Sevenoaks, Kent, England , UK
1 P. Mavros ETH-Centre, Singapore , Singapore
2 N. Thin School of Social and Political Science, University of Edinburgh , Edinburgh, Scotland
3 R. Coyne Edinburgh School of Architecture and Landscape Architecture, University of Edinburgh , Edinburgh, Scotland
4 J. Roe Center for Design and Health, School of Architecture, University of Virginia , Charlottesville, VA , USA
5 P. Aspinall School of Built Environment, Heriot-Watt University , Edinburgh, Scotland
This research directly assesses older people's neural activation in response to a changing urban environment while walking, as measured by electroencephalography (EEG). The study builds on previous research that shows changes in cortical activity while moving through different urban settings. The current study extends this methodology to explore previously unstudied outcomes in older people aged 65 years or more (n = 95). Participants were recruited to walk one of six scenarios pairing urban busy (a commercial street with traffic), urban quiet (a residential street) and urban green (a public park) spaces in a counterbalanced design, wearing a mobile Emotiv EEG headset to record real-time neural responses to place. Each walk lasted around 15 min and was undertaken at the pace of the participant. We report Please note that the legend to Fig. 1 has been modified since this article was originally published, and also that in Tables 2, 3 and 4, R was corrected to (the now correct) R squared.
on the outputs for these responses derived from the
Emotiv Affectiv Suite software, which creates emotional
parameters (?excitement?, ?frustration?, ?engagement? and
?meditation?) with a real-time value assigned to them.
The six walking scenarios were compared using a form
of high dimensional correlated component regression
(CCR) on difference data, capturing the change between
one setting and another. The results showed that levels of
?engagement? were higher in the urban green space
compared to those of the urban busy and urban quiet spaces,
whereas levels of ?excitement? were higher in the urban
busy environment compared with those of the urban
green space and quiet urban space. In both cases, this
effect is shown regardless of the order of exposure to
these different environments. These results suggest that
there are neural signatures associated with the experience
of different urban spaces which may reflect the older age
of the sample as well as the condition of the spaces
themselves. The urban green space appears to have a
restorative effect on this group of older adults.
There is a large body of evidence, as reviewed by
Velarde et al. [
]; suggesting people generally
have a preference for viewing natural over urban
environments. Additionally, walking in nature has
been shown to be beneficial for both well-being
] and cognition [
]. The literature suggests
that the difference between directed (top-down)
attention, where an environment demands increased
cognitive effort, such as a busy road crossing, and
involuntary (bottom-up) attention, where features
of an environment are interesting as opposed to
demanding, may contribute the mechanism for the
beneficial effect of nature [
]. This aligns with
Attention Restoration Theory [
] which posits
that natural spaces have a restorative effect against
cognitive fatigue. What is currently less well understood
is the relationship between the environment and brain
activity in older people. The neuroimaging literature
suggests that there are specific neural signals that are
observable when viewing images of a given
environment as well as during real-time immersion physically in
an environment. However, to date, the majority of this
research has concentrated on younger participants
whereas this study focusses on older people.
To date, in laboratory settings described in the
academic literature, the comparisons between urban and
green/natural spaces have typically been made using a
clear contrast between built urban spaces and scenic rural
], or at least have not differentiated between
quieter urban spaces (such as residential spaces) and
busy built urban spaces. There is therefore a need to
understand the effects of different built urban spaces in
real-world settings that may be qualitatively different in
more subtle ways than a simple urban/rural contrast.
Functional magnetic resonance imaging (fMRI)
has shown distinct networks of activation associated
with viewing urban and rural scenes [
demonstrated that these networks could be mediated
by emotional preference . In addition, reductions
in resting state activation were found in the
subgenual prefrontal cortex after a 90-min walk in
nature as compared with a 90-min walk in an urban
]. Electroencephalography (EEG) has
shown that observing static rural images can induce
increases in alpha (8?13 Hz) neural activity [
associated with relaxation .
Laboratory data using a mobile
electroencephalography (EEG) headset (Emotiv EPOC+; validated
for both laboratory and outdoor settings [
showed outputs that corresponded with self-reported
measures on attractiveness, willingness to visit,
valence and arousal of static images of landscapes or
urban scenes . This study used data from the
Affectiv Suite, Emotiv proprietary software, which
analyzes EEG from distinct brain activity patterns
and allocates a label defining an emotional
parameter (?frustration?, ?excitement?, ?engagement?,
?meditation?, ?long term excitement?). These parameters
have been shown to accurately track different stages
of learning in various scenarios [
]. Based on the
results from a previous study [
] and discussions
with the manufacturer, ?engagement? has been
defined as immersion or interest, ?excitement? as high
arousal, ?frustration? as negative valence and
?meditation? as a low arousal, rested state. Landscape
scenes were predicted by increased levels of
?meditation? and lower levels of self-reported subjective
arousal. The Affectiv Suite was further used in this
pilot study to interpret implied brain activity
associated with walking sequentially in a city through a
quiet urban shopping street, then a green space and
then a busy commercial district (urban busy) in a
young (mean age 30.08 years) participant group
(n = 12) [
]. The results showed lower levels of
?frustration?, ?engagement? and ?excitement?, and
higher ?meditation? when moving into an urban
green space and higher levels of ?engagement? when
moving out of it into a busy-built urban street.
However, the walk settings were experienced
sequentially so it is unclear if the outcomes are
products of the differential settings, walking over time,
or the novelty of undertaking a mobile EEG study in
a public place. The purpose of this study then is not
to replicate the findings of the previous pilot, rather
improve on the experimental design and test this
improved protocol in an older participant group.
Our current study aimed to understand the impact
of the urban environment on neural activity using
mobile EEG with older participants. Based on
earlier findings using the Emotiv parameters [
was hypothesized that:
1. Walking in a busy urban street would be associated
with comparatively higher levels of ?frustration?,
?excitement? and ?engagement?.
2. Walking in an urban green space would be
associated with comparatively higher levels of
?meditation? and lower levels of ?engagement?.
3. Walking in a quiet urban residential environment
would be associated with comparatively higher
levels of ?engagement?, ?excitement? and
?frustration?, but this effect would not be as pronounced as
that found in the busy urban street.
The participants were healthy adults aged 65 years and
over (N = 95, mean age = 76.55 years, standard
deviation = 8.15, range = 65?92 years) and were recruited by
purposive sampling methods to ensure they met the
inclusion criteria. All participants scored over 27 on
the Mini-Mental State Exam (MMSE), suggesting that
the participants were cognitively alert, scoring above
Folsteins threshold of 24 for cognitive impairment
]. Furthermore, the participants all reported being
able to walk, unassisted, for at least 20 min, taken as a
measure of physical ability to walk the assigned route.
Exclusion criteria for study participation included visual
impairments, chronic mental illness and a history of
epileptic or psychiatric disorders. Ethical approval for
the study was provided by the University of Edinburgh,
Edinburgh College of Art Ethics Committee. To account
for brain hemispheric differences [
], all participants in
this study were right handed.
Experimental Design and Procedures
All participants were screened via a phone conversation
with the research team to ensure they fulfilled the
inclusion criteria before being invited to undertake a
practice session. This session served as an opportunity
to demonstrate the EEG headset (described below) while
familiarising participants with the route they would take
(also described in the next section) during the
experimental session. This was achieved by showing
participants a 15-min video of the route. This was important as
it meant participants subsequently did not need to refer to
maps or consult the researchers for wayfinding and also
generated familiarity with wearing the headset.
The experimental session was undertaken on a
separate day when participants were equipped with
a backpack to store the acquisition computer as
well as having the EEG headset calibrated prior to
commencing the walk. The participants were then
instructed to walk on their assigned route at their
own pace?meaning, walking times vary between
participants (10?15 min on
average)?understanding that a member of the research team was
following behind for safety purposes. All experimental
sessions were conducted in the morning to ensure
time of day effects was kept to a minimum.
The participants walked through one of six walk
scenarios, as indicated on the map in Fig. 1. The study site
was based in Leith, an historic but deprived urban
neighbourhood in Edinburgh, Scotland, and selected
due to the proximity of green, quiet and busy urban
spaces as well as a reasonably flat gradient to ensure
participants could undertake the route without excessive
exertion. Figure 1 indicates an interchange zone, in grey,
between the green space and the busy- and quiet-built
spaces, where participants had to cross a busy road
junction. The busy road crossing was not modelled in
the analysis due to the unpredictable nature of the
conditions around the pedestrian crossing.
Figure 2a, b and c shows images of each of the three
environments for walking. The participants were
required to walk sequentially between two of these
environments, with six possible routes generated from
1. Urban busy to urban green (n = 20)
2. Urban busy to urban quiet (n = 14)
3. Urban green to urban busy (n = 20)
4. Urban green to urban quiet (n = 13)
5. Urban quiet to urban busy (n = 14)
Urban quiet to urban green (n = 14)
We describe green space as an area with a
predominance of vegetated and non-built surfaces (including
grass and trees). We describe urban busy spaces as
having a predominance of buildings, paved areas and a
commercial street frontage which attracts a high footfall
and considerable vehicular traffic. Finally, we describe
urban quiet spaces as having a predominance of
buildings, some front gardens and paved areas, but these are
Fig. 2 Street views of the three
walking environments. a Urban
green. b Urban busy. c Urban
quiet (Photo credit: OPENspace
largely residential and, as a result, do not attract a high
footfall or prevalence of vehicular traffic.
EEG Data Acquisition
Brain electrical activity was recorded non-invasively
from the scalp using the Emotiv EPOC+ EEG headset
with 14 channels corresponding to the international 10?
20 position system (AF3, AF4, F3, F4, F7, F8, FC5,
FC6, T7, T8, P7, P8, O1 and O2). P3 and P4 acted as
reference electrodes. Electrode impedances were kept
below 5 k? and signals were internally sampled at
1024 Hz before being internally down sampled to
128 Hz per channel and sent via Bluetooth to the
acquisition computer. The Affectiv Suite creates a different
profile for each individual to account for potential
differences at the neural level, and then interprets the EEG
activity from the available channels into the four
emotional parameters: ?excitement? (short-term arousal),
?frustration?, ?engagement? and ?meditation?. These
parameters were normalized for each individual and scaled
as values between 0 and 1, which allow between-subject
comparisons, at each sampling point. This process
results in approximately seven samples per second (7 Hz).
Data inspection indicated that in the majority of cases,
there was flat-lining in the ?meditation? channel;
therefore, the presented analysis only assesses the effect of
the environment on levels of ?engagement?,
?excitement? and ?frustration?. We defined flat-lining as data
which did not deviate from a particular value (or limited
range of values) for the entire duration of a participant?s
walk. Table 1 presents working definitions of each of the
Affectiv Suite parameters as understood by the authors
from previous research or discussions with
representatives of Emotiv.
In order to analyze the data, one mean for each
individual per walking segment was generated for
each of the paired settings in a walk scenario for
each of the three emotional parameters available via
the Emotiv Affectiv Suite software. These means
were standardized by subtracting the group mean
from the raw mean for each individual and dividing
this by the standard deviation of the group mean.
This data set was subsequently analyzed using a
form of logistic high-dimensional correlated
component regression (CCR) able to deal with smaller
samples where p (number of predictors) is greater
than n (number of cases) as well as repeated
measures multicollinearity [
]. We assessed
difference scores between environmental conditions for
each of the six walking routes generated by a
calculation of x = (walk A ? walk B) at the participant
level for each of the Affectiv Suite emotional
parameters. Outliers were identified and amended
using a criterion of z = 2.5 (i.e. high difference
outliers which might be unduly influential were brought
back to the highest value within 2.5 standard deviations
). These final differences scores were used in the
Comparison of Walking between Urban Busy
and Urban Green Environments
The high dimensional CCR analysis shown in
Table 2 indicates that there were statistically
significant differences between urban busy (UB) and
urban green (UG) environments with regards to levels of
?engagement? and ?excitement?. There was no effect of
the changing environment on levels of ?frustration? in
The ?model fit? section of Table 2 shows the
value of R [
] from cross-validation along with its
standard error (SE) showing a small to medium
effect size. The standardised coefficients from the
usual regression output are shown, indicating the
rank order of predictors in the model. The positive
and negative signs of the standardised coefficients in
Table 2 associated with ?engagement? and
?excitement? levels are given context in Fig. 3. The figure
shows the differences in ?excitement? and
?engagement? in going from the first to the second part of
each walking scenario. A positive value indicates
levels for that parameter are greater in the first part
of the walk and a negative value indicates levels for
that parameter are greater in the second part of the
walk. This means that ?excitement? is higher in UB
than in UG and that ?engagement? is lower in UB
than in UG. However, for the walk in the reverse
direction (UG to UB), the results are reversed.
?Excitement? is negative, meaning that it is lower in UG
than UB; ?engagement? is positive, meaning it is
higher in the UG than the UB section. The Pratt
coefficient shows that ?excitement? contributed
86% to the model.
The high dimensional CCR analysis shown in Table 3
indicates that there were statistically significant
differences between UB and UQ environments with regard to
levels of ?excitement?. There was no effect of the
changing environment on levels of ?engagement? or
?frustration? in either route.
The ?model fit? section of Table 3 shows the value of
] from cross-validation along with its standard error
(SE), showing a small to medium effect size. The
standardised coefficients from the usual regression
output are shown, indicating the only predictor in the model
is ?excitement?. The negative sign of the standardised
coefficients in Table 3 associated with the ?excitement?
parameter is a given context in Fig. 4. The figure shows
the differences in ?excitement? going from the first to the
second part of each walking scenario. This means that,
going from UB to UQ, ?excitement? is higher in UB than
in UQ. However, for the walk in the reverse direction
(UQ to UB), the results are reversed. ?Excitement? is
negative, meaning that it is lower in UQ?the first walk
Comparison of Walking between Urban Green
and Urban Quiet Environments
The high dimensional CCR analysis shown in Table 4
indicates that there were statistically significant
differences between UG and UQ environments with regard to
levels of ?engagement? and ?frustration?. There was no
effect of the changing environment on levels of
?excitement? in either route.
The ?fit? section of Table 4 shows the value of R
] from cross-validation along with its standard
error (SE), showing a small to medium effect size.
The standardised coefficients from the usual
regression output are shown, indicating the rank order of
predictors in the model. The positive signs of the
standardised coefficients in Table 4 associated with
?engagement? and ?frustration? levels are given
context in Fig. 5. The figure shows the differences in
?engagement? and ?frustration? in going from the
first to the second part of each walking scenario.
For the first scenario (UQ to UG), the change is
negative for both ?engagement? and ?frustration?.
This means that both ?engagement? and ?frustration?
levels are lower in UQ than those in UG. However,
for the walk in the reverse direction (UG to UQ), the
situation is reversed, with both ?engagement? and
?frustration? being positive, meaning they are both
higher in UG?the first walk component?than UQ.
The Pratt coefficient shows that ?engagement?
contributed 66% to the model, while ?frustration?
contributed 34% to the model.
The results show higher levels of ?excitement? in urban
busy settings compared with both green and urban quiet
walks, higher levels of ?engagement? in the green setting
compared with urban busy and urban quiet and higher
levels of ?frustration? in the green setting when
compared with the urban quiet setting. These results add to
the growing literature regarding neural change associated
with experiencing changing environments.
Our first hypothesis regarding the urban busy
environment posited comparatively higher levels in
?excitement?, ?engagement? and ?frustration? from walking in
the urban busy walking scenarios. We found higher
levels of ?excitement? in the urban busy location, as
hypothesised, against both the green and urban quiet
settings. However, contrary to our hypothesis, higher
levels of ?engagement? or ?frustration? were not
associated with walking in an urban busy location. ?Excitement?
may be indicative of increased levels of top-down
attention previously associated with viewing urban images
]. This aligns with the spatial context in that the urban
busy route has an increased amount of both vehicular and
pedestrian traffic and participants also encountered
increased obstacles in the path (e.g. refuse bins, other
pedestrians) compared with the other settings. Given that
all our participants were 65 years or over (mean age 77),
there may be age-associated difficulties in spatial
], which require increased levels of top-down
attention to negotiate this section of the walk. The study
neighbourhood used here was less affluent than that used
in our previous research, reflected in poorer quality paths
and paving, and as such may require greater levels of
cognitive alertness in order to navigate. Poor path quality
may contribute to increased motion artefacts from the
participants. However, poor path quality is deemed to
be consistent between route types (i.e. urban busy, urban
quiet and green spaces). Further work should then focus
on the impact of path quality on mobile EEG, in
particular differences between different path qualities and if
there are differences in varied public spaces. The finding
that ?excitement? is not present in the model contrasting
the green and urban quiet scenarios suggests that the
urban busy setting induces neural behavior that is unique
to this particular setting. The relationship between the
?excitement? and ?engagement? parameters, shown
clearly between the urban busy and urban green
segments, may be reflective of the different neural
processing required for top-down and bottom-up attention.
Our second hypothesis regarding the urban green
environment stated we would see comparatively higher
levels of ?meditation? and lower levels of ?engagement?
from walking in the green walking scenarios. We were
unable to investigate the role of the ?meditation? channel
for the reasons outlined earlier, but this study shows
increases in ?engagement??as opposed to decreases
shown previously [
]?in the urban green condition
over both the urban busy and urban quiet conditions. In
our earlier paper [
], ?engagement? was interpreted as
directed attention due to increases in cognitive attention
associated with transitioning from green to urban busy.
However, it is important in this context to understand
how this attention is being directed. There are two
mechanisms for attentional processing: bottom-up and
top-down. Neuroimaging research has suggested that
there are distinct networks of neural activity associated
with top-down and bottom-up processing [
] and that,
in terms of mental well-being, there is a benefit of
bottom-up processing as it is associated with
involuntary attention [
]. Green environments are said to
influence our involuntary attention as compared to urban
environments that demand our attention directly. This
effect is described in environmental psychology as ?soft
], a process that is associated with
restorative health effects, including relief from fatigue,
stress and low mood [
]. Conversely, top-down
processing is associated with increased directed attention and
subsequent fatigue [
]. Recent fMRI research has
indicated that viewing urban images is associated with an
active network similar to that present in top-down
attentional processing, while viewing images of green
scenery is associated with an active network reflective of
bottom-up processing [
]. Perhaps, the results
presented, showing higher level of ?engagement? in the green
condition in this study, are indicative of increased
bottom-up processing. Previous EEG research has
shown increases in alpha activity, associated with
increased relaxation, when viewing of static green images
], so we might hypothesise that alpha changes
may be involved with ?engagement?, given the
correlation between increases in alpha associated with
green space in a laboratory setting and increases in
?engagement? walking in green spaces presented here.
Further analysis is required, perhaps primarily at the
laboratory level, to understand the relationship between the
Affectiv Suite outputs, the traditional raw EEG outputs
and the psychological and physiological implications of
We also see comparatively higher levels of
?engagement? in the green condition when transitioning to or
from an urban quiet condition. This again suggests that
the green environment used in this study has more
restorative properties than those in the built urban space,
despite this space being comparatively quiet in this
instance. There was no difference in the level of
?engagement? in the urban quiet vs. urban busy condition,
as the effect of ?engagement? is limited to the green
space. The urban quiet setting used in this study was
made up of largely residential properties with small front
gardens; this mixture of ?built? and ?green? spaces may
lead to increases in soft fascination, as discussed earlier
9, 10, 30
], but these effects are not significant in
comparison with exposure to urban busy spaces. Previous
research has suggested that quiet residential spaces can be
beneficial for the well-being in residents, based on the
attractive soundscapes and associated tranquillity present
in these spaces [
], suggesting that the urban quiet
setting encourages soft fascination, but this beneficial
effect has not been shown to be significant here.
An alternative explanation of our findings could be
that ?engagement? in this context relates to increased
levels of directed attention associated with negotiating
poorer quality paving in the green space when compared
with the paving in the urban busy and urban quiet
settings. Research has suggested that poor paving is a
barrier for older people in engaging with outdoor
]. However, in this case, while the paving
quality may be poor in the green space compared with
that of the urban busy and urban quiet spaces, the urban
spaces have an increased need for increased vigilance
due to road crossings, increased pedestrian and
vehicular traffic and increased levels of noise, combining to
create potential ?sensory overload?. Generally,
maintenance of outdoor spaces is seen to be vital in order for
older people to benefit from them [
]. Therefore, more
investigation may be required to understand the link
between the ?engagement? parameter and urban spaces
with regard to the barriers that each walking scenario
We also found higher levels of ?frustration? in the
green condition compared with the urban quiet condition.
This is the only model in which ?frustration? is shown as a
significant predictor, not appearing in either of the models
that include urban busy, contrary to the hypotheses. We
have previously defined ?frustration? as being associated
with negative valence [
], so it is unclear why this is
present in this particular model. That it is not present in
the urban green and urban busy model suggests that there
is something particular about the difference between the
urban green and urban quiet condition.
There is a large body of literature that suggests that
green environments have a restorative effect [
and that this effect can be seen when walking in green
space in both healthy [
] and clinical [
populations. Recent research has suggested that even a short
exposure to a static green scene is associated with
improved behavioral performance on a sustained attention
task when compared to short exposure to an image of a
built urban environment devoid of nature . The
results presented here suggest that this restorative effect
may extend to the quiet urban condition selected for this
study. Our study could have investigated the effect of
restoration further by analysis of the ?meditation? output,
but as previously discussed, this channel was removed
from the analysis due to flat-lining of this data.
Our third hypothesis posited that there would be
higher levels of ?excitement?, ?engagement? and
?frustration? in walking in the urban busy condition. There
does not appear to be any urban quiet-specific difference
that would suggest there is a neural pattern that is a
product of only the urban quiet setting, unlike that
shown with ?excitement? in the urban busy setting or
?engagement? in the urban green setting. This space is
perhaps reflective of a mixture of the urban busy setting,
in that the quality of the paths is similarly next to
vehicular traffic, and the green setting where there is
less vehicular and pedestrian traffic as well as an
increase in greenery in some front gardens.
The previous, pilot mobile EEG study, also conducted
in Edinburgh, albeit in a different and more affluent
neighbourhood and in a younger, smaller sample, using
Affectiv Suite data, suggested a different pattern of brain
activity to that found here [
]. The previous data
indicated that ?frustration? and ?engagement? decreased when
transitioning from a quiet urban environment into a green
environment, with ?engagement? increasing again
moving from the green space to a busy urban space. These
results are not replicated in the current study, which may
be indicative of the particular urban environments in new
routes undertaken, or in the responses of older aged
participants. Other research suggests there are
agerelated changes in neural activation [
] which could
explain the different findings shown here.
The key finding from this study is the relationship
between the two, Affectiv Suite-defined, emotional
parameters of ?engagement? and ?excitement? in the
comparison of the urban busy and urban green routes.
?Excitement? levels appear to be significantly higher in the
urban busy environment when compared with those of
the green environment, as hypothesised. The opposite is
true of ?engagement? in that ?engagement? levels are
higher in the green environment when compared with
those of the urban busy environment. This is the
opposite of what we hypothesised. It suggests there may be a
relationship between these two parameters in terms of
the neural processing that underpins the Affectiv Suite
definition of these terms. There was no statistically
significant change in levels of ?frustration? between the
two walking conditions.
Due to the intellectual property rights of Emotiv, we do
not have access to details on which particular EEG
signature underlies each of the Affectiv Suite outputs.
Therefore, while the results here suggest neural change
associated with varying urban environments, it is
important to recognise that only an assessment of the EEG
data using given frequency bands can fully illuminate
the neural basis of this change [
]. Future research may
also focus on providing a quantitative comparison of
effects on both young and old participants to
demonstrate if there are age-associated changes in neural
activity linked with experience of the urban environment.
Furthermore, there may be interest in looking at gender
differences in responses to urban environments, as these
differences could influence the way we perceive and
design fully inclusive public spaces. This was
unfortunately beyond the scope of this paper, as further dividing
the participant group into smaller sub-groups would
likely have had a detrimental impact on the statistical
While the method and protocol used in this study is
useful for understanding differences between settings,
future studies could modify this to look at differences
within settings. For example, assessing the specific
?ingredients? within an urban space (e.g. number of people,
street connectivity, architecural variation in facades) and
the interaction with health and wellbeing outcomes, as
measured by neural activity, is of interest. Recent research
has presented a tool for describing public spaces using
both space syntax and isovist analysis to understand the
connectivity of street segments and shape and size of open
spaces, both predicting levels of stress associated with
]. Indeed, in our study, the urban green setting
appears to be highly connected with multiple paths and
roads adjacent to the space, unlike the urban quiet setting,
implying a potentially higher average of pedestrian
]. Utilising additional analyzes, and
collecting more real-time data (e.g. pedestrian counts
during testing sessions), would give a richer understanding of
the dynamics of the different settings. Our recent work
suggested that a subset of participants from this study
found the urban green setting relaxing and peaceful [
but that could change at different times of the day.
Additionally, it would be beneficial to understand
differences that may occur between laboratory and
?mobile? EEG recordings. By its very nature, mobile EEG is
much more prone to known EEG motion artefacts from
both head and eye movements [
]. However, it is
unlikely that these artefacts are due to the makeup of
the walk settings detailed here, rather they would be
present throughout the recording sessions. Therefore, it
is unlikely that these walk settings generate consistent or
systematic blinking rates that could result in differences
between the Affectiv Suite parameters shown in the
This research shows varying levels of neural activity in
different urban environments while walking at a specific
set of locations in central Edinburgh. The study has
shown higher levels of ?engagement? associated with
walking in an urban green space compared to both a
busy urban commercial street and quiet residential area,
as well as higher levels of ?excitement? walking in a
busy urban space compared to both a green space and
quiet urban space. These findings are consistent with
restorative theory. To the authors? knowledge, this is the
first time such an effect has been shown using a sample
of older, healthy adults. The protocol developed here is
adaptable and could be further applied toward
improving understanding of how best to utilise urban and green
spaces for efficient age-friendly urban design. These
results indicate the potential beneficial effects of
walking in a green environment in an urban setting. Further
investigation is required to understand the exact
neurological processes that underpin these changes.
Acknowledgements The authors would like to thank Agn?s
Patuano and Esther Rind for their assistance in collecting the
experimental data. Further thanks are given to all the participants
who took part in this study. This work was undertaken under the
Mobility, Mood and Place (MMP) research programme
(20132017), supported by Research Councils UK as part of the Lifelong
Health and Wellbeing Cross-Council Programme (grant reference
number EP/K037404/1) under Principal Investigator Catharine
Ward Thompson and part of a work package led by
Co-Investigators Jenny Roe, Peter Aspinall and Richard Coyne.
Compliance with Ethical Standards Ethical approval for the
study was provided by the University of Edinburgh, Edinburgh
College of Art Ethics Committee.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided
you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license, and indicate if
changes were made.
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