Emotive cities: understanding human perception of urban spaces – a pilot study

Neurourbanism offers new ways to understand how urban environments shape human cognition and emotion, yet empirical studies in rapidly urbanizing regions like South India remain scarce. This pilot study investigates how diverse urban settings in Calicut, Kerala, influence neurophysiological states using mobile EEG technology. Participants navigated urban spaces varying in street enclosure, natural features, and activity levels, with EEG readings analyzed for emotional states including excitement, engagement, interest, relaxation, and stress. Results show significant shifts in these metrics as environments transitioned from busy to calm, narrow to wide, urban to green, and crowded to less occupied. This study is the first to apply mobile EEG in a South Indian context, highlighting the potential for evidence-based urban design to enhance mental wellbeing. Findings underscore the importance of integrating green spaces, optimizing street scale, and promoting walkability in urban planning, with implications for policy and design practice in developing cities.

1 Introduction

Rapid urbanization in developing regions like South India intensifies the interplay between built environments and human cognition, invoking interest across psychology, neuroscience, and urban planning (Maslow, 1943; Andrews, 1975; Kaplan, 1995; Chang et al., 2008). Previous studies in the field of cognitive sciences and neurourbanism have revealed compelling traces of the relationship between neuroscience and the built environment by analysing how neuroscience data can inform the design of a better built environment by understanding its cognitive foundations (Mavros et al., 2012; Beyer et al., 2014).

Urban environments elicit multiple, partly overlapping forms of human response, including physiological (e.g., hormonal changes, autonomic activation), psychological (e.g., perceived stress, mood, cognitive load), and behavioural reactions (e.g., avoidance, walking pace, withdrawal into private spaces). Laboratory and imaging studies have shown that city living is associated with altered activity in stress-related neural circuits, such as the amygdala and cingulate cortex, pointing to heightened physiological sensitivity to social and environmental demands (Krabbendam et al., 2020; Lederbogen et al., 2011). At the same time, epidemiological and survey-based work links urbanicity to increased psychological distress and mental health risks (Egbosimba, 2025), while observational and environmental psychology studies document behavioural adaptations such as reduced public space use or preference for quieter, greener routes (Jin et al., 2022; Ludwig, Lautenbach, and Zipf, 2021).

Although these response domains are interconnected, their exact causal relationships remain debated. Some models suggest that chronic physiological arousal may gradually shape psychological vulnerability, which in turn influences everyday behaviour, whereas others emphasize bidirectional feedback between subjective appraisals, bodily states, and actions. For this reason, recent neurourbanism research increasingly combines physiological indicators (such as EEG or biomarkers) with self-reported experience and observed behaviour to better understand how specific urban settings are processed and evaluated by city dwellers.

The main objective of this research was to conduct pilot experiments to verify the possibility of capturing the psychological effects on participants as they move about in diverse urban environments. Ultimately, this research seeks to elucidate the cognitive foundations underlying the psychological impact of urban space. Most empirical work to date is based in Western or Global North cities, whereas rapidly urbanising contexts in South Asia remain under represented. This experiment was a methodological pilot to investigate how well the mobile EEG measurement corresponded to the subjective experience of individual pedestrians on streets with diverse experiential atmospheres in a non-western large-scale urban environment.

2 Background

Human behaviour in urban public places is influenced by various attributes, such as physical, social, cultural, and sensory factors. Abraham Maslow’s Hierarchy of Needs suggests that physiological, biochemical, or aesthetic requirements; safety; love and belonging; and desire for self-actualisation, status, or esteem are essential variables in driving behaviour (Maslow, 1943). Based on primitive survival instincts, Appleton’s Habitat Theory hypothesizes that human aesthetic preference is rooted in our evolutionary history as both hunter and prey. Specifically, it suggests that we derive pleasure from landscapes that offer “prospect”—open views for surveying threats or escape routes—and “refuge”—concealed vantage points, such as trees or vegetation, for safety and strategic observation (Appleton, 1977). Kaplan’s Attention Restoration Theory (ART) suggests that exposure to natural surroundings supports more effortless brain activity, allowing it to heal and refill its focused attention capacity (Kaplan, 1995). Spatial scale and enclosure critically mediate these influences, with narrower streets accelerating social interaction and perceptual engagement compared to wide arterials (Karimi, 2022). In developing cities, even small-scale vegetation like home gardens provides accessible restorative benefits, supporting mental health in dense urban contexts (Sulistyantara, 2022).

Urban spaces, intentionally shaped with perceptual cues, deliver high-intensity multisensory inputs—traffic, pollution, noise, sights, chaos—that elevate basal alertness, stress, and cognitive load, often prompting retreat to calmer private areas (Hollander et al., 2020b). These environments trigger interconnected physiological, psychological, and behavioural responses, though their precise linkages remain debated. Physiologically, urban stressors activate the hypothalamic-pituitary-adrenal axis, releasing cortisol and adrenaline to mobilize energy for threat response; chronic elevation impairs immunity and cardiovascular health (Neale et al., 2020; Russell and Lightman, 2019).

For those living in cities, the intensity of inputs, such as traffic, pollution, noise, odours, sights, chaos, and other stimuli, is higher. This can cause cognitive overload by elevating the body’s basal levels of alertness, stress, and preparedness, in addition to causing people to seek comfort in calm private areas. This inclination may ultimately lead to social isolation, which is linked to anxiety and depression. Neuroimaging confirms heightened amygdala (emotion processing) and cingulate cortex (negative affect regulation) activity among city dwellers, with urban upbringing amplifying perigenual anterior cingulate responses regardless of current residence (Lederbogen et al., 2011; Hollander et al., 2020a). Psychologically, this manifests as elevated perceived stress, anxiety, and reduced attention restoration, while behaviourally, individuals exhibit avoidance of crowded spaces or preference for greener routes.

Psychophysiological measurements have been formulated based on the premise that physiological indicators reflect an individual’s psychological state. The advent of inexpensive and portable EEG equipment has opened new avenues for architects, planners, psychologists, and neuroscientists to assess subjective experiences and understand the foundation of human behaviour in architectural and urban settings (Mavros et al., 2012; Beyer et al., 2014). Mobile EEG provides non-invasive means of collecting people’s emotional states as they move about their surroundings, allowing scientists to determine which brain areas are active and their part in human behaviour (Hollander and Foster, 2016; Gordon Brown and Lee, 2016). In urban design, mobile EEG can help to better understand how people interact with their surroundings, assist the design process, and analyse how places work Mobile EEG devices have been used in environment preference studies, where 20 participants were asked to observe images of urban and natural environments on a computer screen and register their eye-tracking and EEG responses simultaneously while recording their subjective responses (Chang et al., 2008). Higher levels of local green space were linked to considerably lower levels of depression, anxiety, and stress symptoms.

Neurourbanism, a field of study that combines neuroscience and urban disciplines, has emerged as a new academic discipline that focuses on the interdependencies between urbanisation and mental health (Adli et al., 2017). A report published in collaboration with the Central Lab and University College London demonstrates the potential of neuroscience in the built environment, including how it interacts with emerging technology, employs and qualifies urban planning theory, and provides insight into improving city user experience, leading to increased productivity, well-being, and desirability (Camargo et al., 2020). Experimental studies have emerged in neurourbanism, with the first real-time observation supporting the literature that confirms the association of beta wave frequencies with active decision-making or other intense cognitive functions or stress (Karandinou and Turner, 2017; Neale et al., 2020; Erkan, 2018; Olszewska-Guizzo et al., 2018; Ducao et al., 2018).

3 Studying pedestrian movement

Walking is a popular scientific subject because of its health benefits, socioeconomic advantages, and environmental exposure. Walking focuses on the embodied experience with the senses and allows for the study of surroundings (Parsons et al., 1998). Walking enables multi-sensory engagement with constructed, natural, and social surroundings at pedestrian scale, facilitating nuanced psychological responses that stationary observation cannot capture (Sari et al., 2023). Pedestrians see and engage with constructed, natural, and social surroundings because of their modest pace and close proximity to buildings and people (Gehl et al., 2005). Environmental psychology studies have used naturalistic walking to examine the psychological effects of urban and natural settings. The “restorative” benefits of natural settings, such as parks or forests, have been a key area of research. Restorative theory suggests that green and natural environments have a positive psychological impact, making it easier to recover from stressful events or to restore cognitive function (Fett et al., 2019).

Recent urban design research similarly emphasizes how street-level spatial configuration shapes user experience and social dynamics (Amen and Nia, 2021). Researchers have increasingly used naturalistic experiments with walking participants to explore the potential positive psychological effects of short-term visits to natural environments (Thompson et al., 2014). Early experiments used self-reported measures, such as the Perceived Restorativeness Scale, and more recently, mobile psychophysiological measurements of people’s mental states. Research in the psychological effects of the environment has progressed from tightly controlled, visual exposure of various environmental scenes to situated and often mobile experiments of people walking “in the wild.”

4 Methodology

4.1 Materials

4.1.1 Data acuisition and instrumentation

The experiment employed the Emotiv Insight, a wireless, portable 5-channel wireless headset (AF3, AF4, T7, T8, Pz) with a sampling rate of 128 Hz and 16-bit resolution designed to capture real-time brainwave activity during participant movement in outdoor urban settings (Emotiv, 2025). This device records multiple frequency bands (delta, theta, alpha, beta, gamma) with onboard algorithms mapping these signals to performance metrics including engagement, excitement, interest, relaxation, and stress. This device was selected for its high portability and validated utility in ambulatory research settings where traditional tethered systems are impractical (Larocco et al., 2020). Data were transmitted via Bluetooth to a laptop carried by the investigator always maintaining proximity within wireless range. All equipment was cleaned and sanitized before and after each session to prevent contamination between participants.

4.1.2 Survey and interview instruments

The study utilized three self-report instruments, a pre-experiment survey, a post-experiment survey, and semi-structured interview administered during post-experiment debriefing. Participants completed a demographic questionnaire administered prior to device fitting, collecting information on age, gender, professional background, geographic origin, and prior familiarity with urban walking. This brief survey (∼5 min) used open-ended and closed-ended items and established context for interpretation.

Immediately following each walk through a specific urban area, participants completed a location-specific Likert-scale survey. Each performance metric (stress, engagement, interest, excitement, focus, relaxation) was rated on a 5-point discrete scale with anchors: “Not at all” (1), “Slightly” (2), “Moderately” (3), “Very” (4), and “Extremely” (5). Questions like “How engaged did you feel while walking through this street?” and “How stressed did you feel during the walk?” were asked. This structured approach enabled direct comparison with EEG-derived metrics while maintaining consistency across all participants and locations.

After completing each location-specific survey, participants engaged in brief semi structured interviews (∼5–10 min per location) to provide qualitative context for their emotional experiences. Investigators used standardized open-ended prompts including: “Please describe what you experienced emotionally while walking through this street,” “What specific environmental features (e.g., sounds, sights, vegetation, traffic) most influenced your feelings?” and “How would you characterize the overall atmosphere or mood of this place?” Responses were audio-recorded (with explicit participant consent) and subsequently transcribed verbatim for thematic analysis. These qualitative data complemented and enriched the quantitative EEG and Likert-scale findings, allowing participants to articulate nuances in their affective experiences not fully captured by numerical metrics alone.

4.2 Technology used

Existing studies that summarise neuroscientific assessment systems or investigations of the built environment are primarily concentrated in the Western world, using technologies such as eye tracking, fMRI, and EEG to explain or evaluate human experiences in public places (Mavros et al., 2016). However, fMRI equipment is bulky, noisy, and imposes posture constraints for participants, such as lying horizontal and still for the duration of the experiment. On the other hand, EEG is less restrictive, lightweight, wireless, and its new analytical tools have become increasingly tolerant of head and body movements, even allowing participants to walk freely. The advent of mobile EEG has resulted from the miniaturisation of the signal amplifier and the use of wireless protocols for data transfer from the head-mounted amplifier to an auxiliary recording device such as a desktop or tablet computer. This eliminated the need for long electrode leads and allowed participants to be involved in tasks with higher ecological validity, whether static (seated, screen-based) or active (walking, moving). Although consumer-grade mobile EEGs are rather limited in terms of electrodes, targeting only areas of the brain that are of interest for applications such as gaming or neurofeedback, they have spurred many applications within and beyond neuroimaging. Neurofeedback is a procedure in which a user receives information (feedback) in real time about his/her brain activity and deliberately attempts to control the brain’s electrical activity (Gruzelier, 2014).

Notably, the contemporary landscape of brain-computer interface technology has undergone significant transformation, democratising access to tools like the Emotiv Pro. This software, easily accessible to a wide array of disciplines, facilitates the interpretation of EEG signals captured from mobile EEG devices. There are different types of brainwaves that the EEG measures. Raw EEG signals can be identified as distinct waves with different frequencies. Raw EEG signals are often quite noisy and require preprocessing to remove artefacts caused by blinking of the eye, muscle activity, or electrical interference. Band-pass filters are applied to remove noise outside the frequency range of interest and certain algorithms are applied to remove the artifacts. Once the signals are cleaned, the power in different frequency bands are analysed to classify them into delta, theta, alpha, beta, and gamma bandwidths along with time domain and time frequency analysis. This helps to extract features that can be used to quantify different brain states. Techniques such as Principal Component Analysis (PCA) or other dimensionality reduction methods are applied to reduce the dimensionality of the data and focus on the most relevant features. These data are then fed into the classification algorithms that map them to specific performance metrics.

Although this is a tedious process, researchers are now empowered to decode and classify these signals into mental states, such as engagement, excitement, and stress, thereby enabling interdisciplinary studies within the realm of built-environment research without necessitating a comprehensive background in raw EEG analysis. Emotiv’s algorithms analyse the power spectrum of these frequency bands in real-time to classify cognitive and emotional states. For instance, increased power in the beta band might be interpreted as high engagement, whereas increased alpha power might be interpreted as relaxation. The exact thresholds and combinations of these frequencies are determined through machine learning models trained on extensive datasets of EEG recordings under various conditions. The Emotiv Pro software categorises these waves into six different measurable cognitive states called performance metrics. These performance metrics are described in the following table (see Table 1).

Table 1

Table 1. Performance metrics and bandwidth relationship.

The performance metrics explained in Table 1 were used to interpret the human brain waves into the current mental state of the participant as they traverse through various urban spaces. A preliminary behavioural experiment was conducted as a pilot study in which multiple participants underwent naturalistic walking through designated streets within familiar destinations with the objective of discerning meaningful patterns among the participants. It should be noted that the data collection took place in real-world environments, with participants actively engaged in walking in all experimental scenarios.

4.3 Procedure

4.3.1 Participant recruitment and informed consent

Participants were recruited through social media posts and poster circulation targeting adults aged 25–45 with diverse professional backgrounds and geographic origins in or near Calicut, India. A recruitment notice was disseminated via social media networks, explaining the study purpose, procedures, time commitment (∼90–120 min per session), and equipment used. Interested individuals contacted the principal investigator to arrange a session. Informed written consent was obtained before the start of any procedures. The consent form included a detailed project briefing, FAQ section addressing equipment safety and data privacy, and explicit authorization for audio recording and potential use of anonymized data and photographs in publications. Participants were informed of their right to withdraw at any time without penalty.

4.3.2 Pre-experiment preparation

Upon arrival, participants completed the pre-experiment demographic survey in a quiet environment (∼5 min). They were then escorted to a private parked vehicle where the EEG device fitting took place in a controlled, comfortable setting. During this fitting phase (∼15 min), the investigator explained how the Emotiv Insight operates, answered participant questions, and ensured proper electrode-scalp contact across all five channels (As seen in Figure 1). Participants were informed that the device is wireless, non-invasive, and safe for walking. The investigator then guided participants into a calm, meditative mental state by requesting they close their eyes and breathe slowly for 3–5 min while EEG signals were monitored on the Emotiv Pro software. Once the participant demonstrated stable alpha and theta activity consistent with baseline relaxation, baseline EEG data were formally recorded (∼5–8 min), serving as the individual’s reference state for subsequent comparisons during walking.

Figure 1

Figure 1. The emotiv insight and the location of its sensors.

4.3.3 Experiment

Upon baseline completion, participants were instructed to begin walking through the first designated urban area. The walking session proceeded according to a standardized protocol (See Figure 2):

Figure 2

Figure 2. Process of the experiment.

Walk Phase (∼10–15 min per location): Participants traversed a predetermined route through the assigned street while wearing the active EEG device. The principal investigator walked at a safe distance behind the participant (sufficient to remain visible and within Bluetooth range of the device, typically 5–10 m). Participants were explicitly instructed not to speak during the walk to minimize electrode artifact from jaw movement and vocal cord activation. The investigator carried a laptop connected via Bluetooth to the EEG device, monitoring signal quality and data continuity throughout the walk (Figure 3). The investigator also discretely observed and noted participant behaviour and any environmental anomalies (e.g., unexpected traffic, weather changes, or equipment malfunction).

Figure 3

Figure 3. A picture of the experimental setup in which the participant is walking on a selected street.

Pause and Survey Administration (∼8–10 min per location): Upon completing the walk through a specific urban area, participants paused in a nearby shaded or sheltered area. The investigator administered the post-experiment Likert-scale survey questionnaire, which participants completed independently. The EEG device remained fitted during this survey period, allowing for continued data recording during the transition between active movement and reflective assessment.

Semi-Structured Interview (∼5–10 min per location): Immediately following survey completion, the investigator conducted a brief face-to-face semi-structured interview using the standardized open-ended prompts listed above. Participants were encouraged to speak freely about their emotional and sensory experiences during the walk. The investigator audio-recorded all responses using a portable digital recorder (with explicit prior consent) while taking brief written notes to capture contextual observations. No responses were solicited or prompted beyond the initial open-ended questions, allowing participants to articulate their experiences without undue influence.

4.4 Site

The intention of this pilot study is to ascertain if there exists a discernible pattern among participants as they navigate different urban settings. Given that this study is in its initial stages, three distinct locations within Calicut city, Kerala, India each exhibiting unique urban characteristics, were chosen for the experiment.

1. Street A – Beach Road: An active urban thoroughfare with juxtaposing urban structures against an active and vibrant coastal backdrop with approximately 800 m of walking distance (Figure 4).

2. Street B – Savovaram Mini Bypass: An urban green street featuring a water body and lush greenery along one side and moderate traffic and buildings on the other side. It has a walking distance of approximately 900 m (Figure 5).

3. Street C – Mavoor Road: A bustling urban street lined by buildings on both sides with high-intensity traffic and footfall. It has a walking distance of 700 m (Figure 6).

Figure 4

Figure 4. Pedestrian eye level image of Street A next to satellite image of the surroundings.

Figure 5

Figure 5. Pedestrian eye level image of Street B next to satellite image of the surroundings.

Figure 6

Figure 6. Pedestrian eye level image of Street A next to satellite image of the surroundings.

All participants navigated the same three locations in an identical, fixed sequence to ensure environmental and temporal consistency. Sessions were conducted during non-rainy morning hours (08:00–11:30 a.m.) to minimize weather-related confounds and ensure consistent daylight conditions. The same time was chosen for each location to minimize time-based weather/temperature fluctuations. Between each location, participants were permitted a 3–5 min rest period to recover, though EEG recording and subsequent survey/interview administration proceeded immediately upon arrival at the next area.

This study employed a within-subjects experimental design wherein all participants experienced all three urban environments in an identical, predetermined sequence. This design was chosen to maximize internal validity by ensuring that individual differences in demographics, neurobiology, or familiarity with walking were controlled for through repeated measurement within each person. The fixed-order presentation of locations, while introducing potential order effects, was operationalized intentionally to standardize the experimental context and enable robust statistical and qualitative comparisons across the small pilot sample.

4.5 Qualitative analysis protocol

All semi-structured interviews were audio-recorded with participant consent. Recordings were subsequently transcribed verbatim by the principal investigator within 48 h of the session to ensure accuracy and minimize memory loss. Transcripts were reviewed for clarity and completeness. The transcribed interview responses were coded inductively to identify emergent themes related to emotional experience, environmental perception, and sensory responses. Themes were organized by participant, location, and perception to enable triangulation with quantitative EEG and survey data. This approach allowed qualitative responses to contextualize and validate the numerical findings. All audio recordings, transcripts, and EEG data were stored on a password-protected, encrypted drive accessible only to the principal investigator. Participant identities were replaced with anonymous participant codes (e.g., P01, P02) in all data files and analyses. Interview excerpts cited in results were anonymized prior to publication.

5 Results and discussion

The pilot study included a variegated sample of individuals from whom the final readings were obtained. The experiment involved a representative sample identified from the age group of 25–45 years with participants of various genders and professional backgrounds. Their professional backgrounds spanned a range of fields, including fitness training, education, architecture, research, and technical expertise. Of the eight participants who participated in the study, data from two people had to be discarded due to inconsistent contact of the electrode on the scalp in the EEG experimental setup, leading to discontinuity and missing data in the readings. The final data from six participants (four men and two women) from a diverse demographic mix were obtained. Additionally, the participants exhibited varying geographical backgrounds, with three being native to the study city and the remaining three originating from a different city.

The mean values for each performance metric were calculated for the six participants at each examined site. It is revealed that when analysing the experimental results, they have a clear pattern as far as street characteristics are concerned. The excitement levels of Street A, located along the beachside, had a (See Figure 7) higher mean values (Ex_A = 68.2) showing the increased physiological arousal and awareness. Also, it was noted that this street had the highest mean interest value (In_A = 71.5), indicating greater attraction and reduced aversion to its environment. There is a relative lack of focused attention on any specific activity which may be inferred from Fo_A = 28.3 which is the average focus value for this street. This could be due to increased liveliness and dynamism in the beach area, leading to increased arousal and attraction levels. Furthermore, it could be argued that this setting offers a feeling of ease which might reduce the cognitive load during walking, thereby lowering focus. In the post experiment survey, the participants described the walk as Relaxing and exciting walk. The view to the beach was highlighted in most responses. Few of the responses said “had a mix of built-up and green open spaces throughout the stretch. The urban design features such as landscapes, pedestrian paths and street designs captured interest at various points” “It was interesting to track my feelings while I focused on experiencing the space I was walking through. It was obvious that my level of interest and excitement would go up as I passed through shades and greenery.” Another respondent said, “The route areas with trees and landscape felt more relaxing and positive. It is nice walking with the view of beach.”

Figure 7

Figure 7. Box plots showing the distribution of each performance metric across three sites.

However, Street B which contained much green, had relatively higher mean values of excitement (Ex_B = 63.7), interest (In_B = 64.5), and relaxation (Re_B = 56). The presence of trees and water bodies seemed to promote increased relaxation, physical arousal, and interest levels. This aligns with findings that incidental greenery in developing urban settings yields measurable psychological benefits (Sulistyantara, 2022). Participants described the walk along Street B as very peaceful and calm experience. One participant said, “The walk was comfortable and felt rather relaxed and safe. The green landscape and the canal caught my attention more than the busy roads which made the walk enjoyable.” But few participants also said, “It is very calm and serene atmosphere, but the water is too smelly and unpleasant.” “I had a great time doing that experiment. In the beginning, the smell was not good. So, my attention was on the water feature and wondered if it was polluted. I also looked at the large and wild barks of the trees.”

Street C is characterised by busy roads and buildings lining both sides, thus exhibiting lower mean feelings of excitement (Ex_C = 42.2), interest (In_C = 54.8), and relaxation (Re_C = 40.5). However, the average focus value in this context was significantly higher (Fo_C = 35.3). This indicates that there was a reduced response to arousal, attraction, and relaxation, accompanied by increased attention to walking tasks. Several factors could explain such observations, including distractions from vehicular traffic and the absence of green spaces which can potentially increase the cognitive load. Moreover, the absence of vibrant activities in this environment may infer monotony with more concentration felt during walking. Participants responded that walking along street C felt was not very pleasant and it made them very alert and stressed. Many described it as “too loud and noisy.” One participant was worried about the crowd on the path, and said “I understood that I’m better off with very less people around me.” Another participant said, “Things that caught my attention were the new bus waiting shed on the opposite side of the road and a single tree near the junction at the end of the designated stretch” One participant wondered if walking above the slabs that covered the drainage was safe.

This study has shown that urban environments are intricately linked to people’s psychophysiological responses. The elevated relaxation on the green corridor (Street B) and the combination of interest and excitement on the coastal street (Street A) reflect Kaplan’s prediction that natural and semi-natural environments afford “soft fascination,” allowing cognitive recovery while maintaining engagement (Kaplan, 1995). The reduced stress and sustained positive affect on these streets support ART’s predictions about restorative environments. The elevated engagement on the narrower, more enclosed Beach Road aligns with research on spatial scale and social perceptual engagement. Already existing studies documents that moderate enclosure and legible spatial configuration heighten social and perceptual engagement compared to wide, monotonous arterials (Karimi, 2022; Amen and Nia, 2021). Our findings suggest that this effect extends to neurophysiological indices of engagement and interest, not merely self-reported perception. The heightened focus coupled with low relaxation on the busy commercial street (Street C) accords with literature documenting that high environmental complexity, noise, and crowding elevate cognitive load and trigger defensive, task-focused attention rather than exploratory engagement (Karandinou and Turner, 2017). The absence of restorative features (greenery, open space, quietude) appears to offer no compensatory benefits. The outcome shows how important different environmental elements like natural features; level of activity and the kind of ambience are when it comes to influencing emotional states and cognitive engagement during walking.

6 Limitations

The study serves as a proof-of-concept that mobile EEG walking studies are feasible in non-Western urban settings. It identifies practical challenges, environmental confounds and solutions like pre-walk briefing, outdoor device fitting, standardized routes, etc. Future, larger studies can build on these protocols. The work does not claim to definitively establish causal relationships between specific street features and cognitive-emotional states. Rather, it generates plausible patterns that motivate future investigation with larger samples, multimodal sensing (EDA, HR/HRV, environmental monitoring), and more sophisticated statistical models.

This pilot study had a few practical challenges including time-consuming data collection and analysis, the possibility of sensor disconnecting due to movements by participants and fluctuations in EEG readings from sudden distractions in the environment. The fluctuations in participants’ EEG readings because of minor environmental factors such as weather changes, traffic and day time must be considered thoroughly hence large sample sizes may be required for this. Moreover, logistical challenges like short laptop battery life reduces the number of experiments that can be conducted at a given time. Additionally, coordinating dates based on volunteer availability add further complexity to the research process.

Moreover, it is important to acknowledge that Calicut City, being a small urban area in South India, presents a distinct set of urban and environmental conditions. Due to its urban form that is mostly low-rise buildings and homogenous in nature, the city may offer limited variability in environmental stimuli compared to larger and more diverse urban areas. The lack of designed urban public spaces in the city limits the range of varied environments available for data collection and affects the generalisability of findings. With its tropical climate, the city experiences extremes of sunshine and heavy rainfall, which may influence participant behavior. Environmental factors, such as relative humidity, noise levels and air quality can possibly introduce confounding variables into the study.

Being a pilot study intended to validate a methodology, the sample size chosen was small. Such small sample size could affect internal validity of the result. Nonetheless, this constraint presents an opportunity for further investigation in the relevant study area. Studies with larger sample sizes would be able to tackle this problem more effectively, perform better randomisation, and control the effect of confounding variables. Strategies to reduce randomisation bias, such as using stratification techniques in participants, are recommended to improve the reliability and validity of future research outputs.

Potential influences of participant characteristics such as gender, age, familiarity with the city, and transient states (e.g., sleep quality or daily mood) were not examined statistically in this pilot and remain uncontrolled sources of variance. Future studies with larger samples should explicitly measure these variables and incorporate them as covariates or factors to better disentangle individual differences from environmental effects.

The advantages of using multiple EEG techniques simultaneously on participants in similar studies are that it can optimize time efficiency and increase the rate of data collection. it helps researchers to gather more information over a wider sample size in a shorter period thereby boosting the statistical power as well as reliability of the results. It also allows researchers to control the environmental variables more effectively. By exposing participants to the same environmental conditions simultaneously, the impact of external factors such as weather, traffic, and other variables can be reduced or accounted for in the analysis. Researchers can observe how different individuals respond to the same stimuli and change in visual characters.

Subsequent studies with larger and more diverse samples can build upon our findings, employing more sophisticated randomisation techniques and enhancing the reliability and validity of the results. Exploring alternative study designs or methodologies that mitigate the impact of small sample sizes could provide further insight into the complexities of the study.

7 Conclusion

This research, however, sheds light on the underlying dynamics that exist between urban environments and the psychophysiological responses of individuals, thus providing insight into what influences and affects individuals’ mood, cognitive functioning, and overall mental health. The findings reveal that pedestrians walking through active streets or natural features on at least one side experience increased positive psychological stimulation compared to those living in densely built areas. This study reveals that green spaces in the city and proximity to nature were found to have positive effects on mental health. These natural features have restorative qualities that help diffuse the stress of city life. This study calls for the incorporation of green spaces in urban planning and design because of their contribution to improving mental wellbeing. The results also indicate that incorporating a natural environment into the vibrant streets of a city or any other urban setting will result in lower stress levels among its inhabitants. Lively urban streets create interest and engagement in various activities, thus contributing positively to the urban experience in general.

Collectively, this study underscores the multifaceted benefits of urban planning strategies that encompass elements such as green spaces, natural environments, and diverse activities. By considering these factors, urban planners and policymakers can work towards creating more enriching and health-promoting urban landscapes that not only meet the physical needs of residents but also contribute to their psychological wellbeing. As cities continue to evolve, the insights gained from this study can serve as a foundation for the creation of urban spaces that foster healthier and more sustainable communities. The methodology of this pilot can be a stepping stone towards more meaningful research in this field.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Institute Ethics Committee, National Institute of Technology Calicut. The studies were conducted in accordance with the local legislation and institutional requirements. The study involved human participants and the collection of neurophysiological data using a wireless 5-channel EEG measuring device. The participants provided their written informed consent to participate in this study. Written informed consent was also obtained from the individuals for the publication of any potentially identifiable images or data included in this article. Additionally, data handling and analysis procedures were designed to protect the privacy and confidentiality of participants.

Author contributions

LM: Writing – review and editing, Conceptualization, Investigation, Methodology, Software, Visualization, Resources, Writing – original draft, Project administration, Validation, Formal Analysis, Data curation. CK: Supervision, Conceptualization, Writing – review and editing, Methodology, Resources.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. During the preparation of this work the authors used PaperPal in order to improve sentence framing and rephrasing. After using this tool/service, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.

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