This paper explores the use of mobile Electroencephalography (EEG) in the study of environmental perception and the ways the perception of physical measurements of a space may affect individual walking behaviour. The hypothesis of this study is that obtrusive and complex street environments stretch the perception of walking time and distance. So far, the factor of an individual’s affective state has not been taken into account in perceiving space. We propose the use of mobile EEG, a technology that permits such insights, to augment the traditional arsenal of questionnaires and self­reported measures of experience and mental representations of space.

1. 0.0 Presentation Outline
ARE WE THERE YET?
Exploring distance perception in urban environments with mobile Electroencephalography
Katerina Skroumpelou, CNLab, NTUA, Panagiotis Mavros, CASA, UCL, Dr Andrew Hudson Smith, CASA, UCL
GISRUK 2015 Conference, University of Leeds, April 16, 2015

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0.0 Presentation Outline
1. Introduction _ Hypothesis and Intentions
2. Background _ Distance Perception and Urban Mobility
3. Methods _ The use of Electroencephalography (EEG) in a field experiment
4. Results _ What the affective state of individual walkers suggests
5. Discussion _ Possible use and application of the results
6. References and Bibliography
7. Q&A

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1.1 Introduction & Hypothesis
Individual perceptions of the environment vary
These perceptions affect the way we travel in the city
Architects and planners should take into account different factors that affect
perceptions of the environment to produce better spaces
therefore

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1.2 Questions
people perceive physical measurements differently
(Tolman, 1948, Pequet, 2002)
a number of parameters affect distance perception
(Montello, 1997).
“[distance] is used to evaluate costs of traveling from one place to another, and it helps
us utilize resources efficiently (time, money, food).” (Montello, 1997)
does the built environment affect the emotional (affective) state of individuals?
does the affective state of individuals affect distance perception?

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1.3 Intentions
understand how much the build environment affects emotions
explore to what extend emotions affect the perception of distance
and route length
test how the differences of these perception affect
route/trip planning
discuss the potential of this kind of research on urban planning tactics

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2.1 INdividual Perceptions of Environment
environmental distance and vista space
(Montello, 1997)
different individuals have different experiences of the same space
perception-driven design by
ancient Greek architects
using Entasis on the columns
(a slight curve as the diameter
is decreased from the bottom
upwards), they allow the visual
effect to make the temple ap-
pear more symmetrical than it
actually is
architectural example

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2.2 Urban Mobility
WHY urban mobility and distance perception?
urban mobility behaviour is vital in the context of a rapidly urbanising population
studying travel behaviour, route-choice and transport mode choice
is an essential step towards better cities
perception of distance is a main factor influencing spatial decision making tech-
niques as it affects the decision whether to walk towards a destination or seek
an alternative means of transport.

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2.3 Distance Perception
Montello (1997)
_environmental features of a route
_the travel time (as perceived and reported)
_travel effort
Dewulf et al. (2012)
_sense of safety
_attractiveness
_complexity of the environment
_emotional responses
people do not necessarily choose the shortest route
Yoh et al. (2011)
connections between:
_frustration and traveler’s perceived walking distance and time
_complexity and pleasantness
_ walking time, distance, fatigue
It’s time to take the emotional state into account

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3.1 Methods_Electroencephalography
Learning modality, individual differences in spatial abilities or cognitive strategies used
influence the detail and accuracy of resulting mental representations of space
it is less clear how the emotional state of the individual affects their
perception of travelled distance
this study uses an electroencephalography (EEG) device
measuring brain activity, and a software that interprets this brain activity
into different emotional states (see also Aspinall et al, 2013)

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3.2 The EEG Device
The original Emotiv EPOC EEG headset The modified version of the Emotiv EEG headset
that was used in the experiments, here worn by a
participant.

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3.2 The EEG Device
A screenshot of the testbench
software that comes with Emotiv
(shows raw EEG data).
A screenshot from the Emotiv
Affectiv suite software that comes
with Emotiv.
Emotiv Affective Suite soft-
ware translates the raw EEG
signal to four different mood
readings:
_“Excitement”
_“Frustration”
_“Meditation”
_“Engagement/Boredom”

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3.3 Field Experiment
field experiment in the
area of Fitzrovia,
central London.
eight participants were
asked to walk a
designated route that
was divided into
four segments
of different environ-
mental characteristics
main, busy road,
backstreet, shopping
street, wide pavement,
presence of trees, resi-
dential, eating facilities,
paved pathway, tube
station
start/end

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3.3 The Route in Fitzrovia
Noise levels on and around Fitzrovia.
Air pollution levels on and
around Fitzrovia.

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3.3 The Route in Fitzrovia

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3.4 LoggerApp _Annotation of Events
_brain activity is
influenced by incidental events
or situations that occur during the experiment
eg: confrontation of an obstacle or verbal
communication
_an Android application was developed for
this purpose, LoggerApp
_records:
location, speed, GPS recording accuracy, al-
titude, current time in unix epoch time and in
human readable format
_sampling rate of 1Hz
_event options:
“Instructions/Talk”, “Pause”, “Obstacle”,
“Looks Around”, “Start Walking”,
“Hesitates”, “Controlled Crossing”,
“Uncontrolled Crossing”

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3.4 LoggerApp _Annotation of Events
51.517
51.519
51.521
51.523
51.525
−0.144 −0.140 −0.136
lon
lat
Event
0
ControlledCrossing
Hesitates
Instructions/Talk
LooksAround
Obstascle
Pause
StartWalking
UncontrolledCrossing
Events as recorded by the LoggerApp

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4.1 Results_Variables and Data
Independent Variables
_Distance
_Segment
_Street scale
_Street type
_Land Use
_Junction (yes/ no)
_Junction Type
_Location
Dependent Variables
_Mental States
_Excitement
_Frustration
_Engagement
_Meditation
_Self-reports
_Pleasant
_Noisy
_Long
Data
_continuus EEG data
_self-reported ranking for the route four segments
(the participants were asked to rank the four route segments according to
a number of characteristics)
intention-> group the experience of the route in four segments
of distinct environmental nature

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4.2 Spatial Diagrams
51.520
51.522
51.524
−0.142 −0.140 −0.138 −0.136 −0.134
Longitude
Latitude
fru
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
fru
Frustration
Frustration levels of a participant along the route

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4.3 Ranking Experiences
Question: Please rank the segments from the most to least stressful.
Answer: 1st-4th-3rd-2nd
According to the ranking, the most stressful takes 4 points of “stressfulness”, the next
in line takes 3 points of stressfulness, the next to last takes 2 points of stressfulness
and the least stressful takes 1 point of stressfulness.
The points that all participants give to each characteristic are then assigned to the
segments.

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4.3 Ranking Experiences

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4.4 Length Estimation

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4.5 The Role of Events
51.520
51.522
51.524
−0.1425 −0.1400 −0.1375 −0.1350
Longitude
Latitude
Event
0
ControlledCrossing
Hesitates
Instructions/Talk
LooksAround
Obstascle
Pause
StartWalking
UncontrolledCrossing
fru
0.2
0.4
0.6
0.8
1.0
Frustration
Frustration levels of a participant and all the events along the route

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0.00
0.25
0.50
0.75
1.00
0 20 40 60 80
x
C1
"CASA001"
CASA001
CASA002
CASA003
CASA004
CASA005
CASA006
CASA007
CASA008
MEAN
Frustration
4.5 The Role of Events
Frustration levels of all participants along the route and the mean frustration. Vertical
lines divide the graph in the four route segments.
This graph is pretty messy, we needed something more.

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4.5 The Role of Events
Frustration levels of participants 20 seconds before and 30 seconds after a recorded
encounter with an obstacle

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4.5 The Role of Events
This approach imitates a Neuroscience analytical method named “Event Related Poten-
tial”, which explains brain activity in response to stimuli.

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4.5 The Role of Events
0
2
4
6
0.00 0.25 0.50 0.75 1.00
value
density
ident
All Frustration
Obstacles
Uncontrolled Crossings
This histogram illustrates the distributions of the means of the recorded frustration val-
ues near obstacles and uncontrolled crossings as compared to the mean of all partici-
pants’ frustration readings.
>more high frustration readings than the mean of general frustration

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0
10
20
30
40
0.4 0.6 0.8 1.0
value
density
ident
Controlled Crossings
Obstacles
Uncontrolled Crossings
This histogram illustrates the distributions of the maximum frustration readings around
controlled crossings, uncontrolled crossings and obstacles.
>more high frustration readings for uncontrolled crossings and obstacles
4.5 The Role of Events

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5.1 Conclusions
route pleasantness, route tiredness and route length do not necessarily go together
there is stronger connection between tiring and stressful routes.
length seemed to be less depended on these attributes
the shortest the route, the bigger the excitement, but a solid conclusion cannot be
drawn due to other variables, such as stressfulness and annoyance
people seemed to value more the pleasantness of the route and comfort of walking
than the length
a more complex path and having to memorise complex directions increased the
reported stress and recorded frustration.
there could be a connection between negative feelings and an overestimation of length

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5.2 DISCUSSION AND Future Work
urban planning “improves the welfare of people and their communities by creating more conve-
nient, equitable, healthful, efficient, and attractive places for present and future generations”
Yet, we continue to live in cities that urban planning has not met its stated purpose
Once certain characteristics and street elements are pinpointed as being a main factor
of annoyance, they can be isolated. Once isolated, they can be removed from the envi-
ronments.
The ultimate goal of this kind of studies is towards a future of better planning strategies.
further work is needed to form accurate answers
the planning of the experiment can work as an example that will set a new paradigm
on how such kind of research (combining EEG with urban studies) can be conducted in
the future
a future study should recruit more participants
American Planning Association

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6. References and Bibliography
Aspinall, P., Mavros, P., Coyne, R., & Roe, J. (2013). The urban brain: analysing outdoor physical activity with mobile EEG. British Journal of Sports Medicine, 1,
1–7. doi:10.1136/bjsports-2012-091877
British Government, Department for Transport, “Understanding and Valuing the Impacts of Transport Investment”, October 2013
British Social Attitudes Survey 2011: Public Attitudes Towards Transport, Department for Transport, Copyright 2012
Dewulf, B., Neutens, T., Van Dyck, D., De Bourdeaudhuij, I., & Van de Weghe, N. (2012). Correspondence between objective and perceived walking times to urban
destinations: influence of physical activity, neighbourhood walkability, and socio-demographics. International journal of health geographics, 11, 43.
Garling, T. (1989). THE ROLE OF COGNITIVE MAPS IN SPATIAL DECISIONS. Journal of Environmental Psychology, 9, 269–278.
Ishikawa, T., & Montello, D. R. (2006). Spatial knowledge acquisition from direct experience in the environment: individual differences in the development of metric
knowledge and the integration of separately learned places. Cognitive Psychology, 52(2), 93–129. doi:10.1016/j.cogpsych.2005.08.003
Kitchin, R., & Blades, M. (2002). The cognition of geographic space (Vol. 4). IB Tauris.
Meilinger, T., Frankenstein, J., & Bülthoff, H. H. (2013). Learning to navigate: Experience versus maps. Cognition, 129(1), 24–30. doi:10.1016/j.cogni-
tion.2013.05.013
Montello, D.R. (1997). The perception and cognition of environmental distance: Direct sources of information. In Spatial Information Theory A Theoretical Basis for
GIS. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 297-311.
Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, S. A., Johnson, R., Miller, G., Ritter, W., Ruchkin, D., Rugg, M. and Taylor, M. J. (2000). Guidelines for using
human event-related potentials to study cognition: recording standards and publication criteria. Psychophysiology, 37(02), 127-152.
Richardson, A. E., Montello, D. R., & Hegarty, M. (1999). Spatial knowledge acquisition from maps and from navigation in real and virtual environments. Memory &
Cognition, 27(4), 741–50. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10479831
Saelens, B.E., Sallis, J.F. & Frank, L.D. (2003). Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning litera-
tures. Annals of behavioral medicine: a publication of the Society of Behavioral Medicine, 25(2), pp.80-91.
Waller, D., & Greenauer, N. (2007). The role of body-based sensory information in the acquisition of enduring spatial representations. Psychological Research, 71(3),
322–32. doi:10.1007/s00426-006-0087-x

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Thank you!
Katerina Skroumpelou, NTUA
kskroumpelou@cn.ntua.gr
@psybercity
Panos Mavros, CASA, UCL
Dr Andrew Hudson Smith, CASA UCL
questions?