Use of Electroencephalography (EEG) for the Analysis of Emotional Perception and Fear to Nightscapes

Emotion analysis and recognition in 3D space using classifier-dependent feature selection in response to tactile enhanced audio-visual content using EEG

Traditional media such as text, images, audio, and video primarily target specific senses like vision and hearing. In contrast, multiple sensorial media aims to create immersive experiences by integrating additional sensory modalities such as touch, smell, and taste where applicable. Tactile enhanced audio-visual content leverages the sense of touch in addition to visual and auditory stimuli, aiming to create a more immersive and engaging interaction for users. Previously, tactile enhanced content has been explored in 2D emotional space (valence and arousal). In this paper, EEG data against tactile enhanced audio-visual content is labeled based on a self-assessment manikin scale in 3 dimensions i.e., valence, arousal, and dominance. Statistical significance (with a 95% confidence interval) is also established based on gathered scores, highlighting a significant difference in the arousal and dominance dimension of traditional media and tactile enhanced media. A new methodology is proposed using classifier-dependent feature selection approach to classify valence, arousal, and dominance states using three different classifiers. A highest accuracy of 75%, 73.8%, and 75% is achieved for classifying valence, arousal, and dominance states, respectively. The proposed scheme outperforms previous emotion recognition based studies in response to enhanced multimedia content in terms of accuracy, F-score, and other error parameters.

Small Urban Green Spaces: Insights into Perception, Preference, and Psychological Well-being in a Densely Populated Areas of Tehran, Iran

In metropolitan areas worldwide, abandoned properties are prevalent, prompting a need for small urban green spaces (SUGS) to meet the growing demand. Understanding residents' preferences and perceptions of transformed spaces is vital for effective urban design. This study delves into residents' preferences and perceptions regarding the transformation of such spaces into SUGS and their impact on psychological well-being. By examining how these preferences and perceived health benefits shape the value of transformed spaces, the research aims to inform effective urban design strategies. The participants underwent visual stimulation, with psychological reactions recorded through Electroencephalogram (EEG) readings and assessed via Questionnaire. Machine learning techniques analyzed EEG sub-band data, achieving an average accuracy of 92.8% when comparing leftover and designed spaces. Results revealed that different types of transformed spaces provoke distinct physiological and preference responses. Specifically, viewing SUGS was associated with significant changes in gamma wave power, suggesting a correlation between enhanced gamma activity and increased feelings of empathy. Moreover, participants also reported enhanced comfort, relaxation, and overall mood, and a strong preference for SUGS over untransformed spaces, emphasizing the value placed on these areas for their health benefits. This research highlights the positive impact of even SUGS on mental health, using EEG data to assess emotional states triggered by urban spaces. The study concludes with a call for further research to investigate the long-term benefits of SUGS on well-being, alongside an exploration of the gamma band as a neural marker for emotional restoration in urban green spaces. This research highlights the crucial role of urban design in fostering psychological well-being through the strategic development of green spaces, suggesting a paradigm shift toward more inclusive, health-promoting urban environments.

Cities and neuroscience research: A systematic literature review

BACKGROUND

Cities are becoming the socio-economic hubs for most of the world's population. Understanding how our surroundings can mentally affect everyday life has become crucial to integrate environmental sustainability into urban development. The present review aims to explore the empirical studies investigating neural mechanisms underlying cognitive and emotional processes elicited by the exposure to different urban built and natural spaces. It also tries to identify new research questions and to leverage neurourbanism as a framework to achieve healthier and sustainable cities.

METHODS

By following the PRISMA framework, we conducted a structured search on PubMed, ProQuest, Web of Science, and Scopus databases. Only articles related to how urban environment-built or natural-affects brain activity through objective measurement (with either imaging or electrophysiological techniques) were considered. Further inclusion criteria were studies on human adult populations, peer-reviewed, and in English language.

RESULTS

Sixty-two articles met the inclusion criteria. They were qualitatively assessed and analyzed to determine the main findings and emerging concepts. Overall, the results suggest that urban built exposure (when compared to natural spaces) elicit activations in brain regions or networks strongly related to perceptual, attentional, and (spatial) cognitive demands. The city's-built environment also triggers neural circuits linked to stress and negative affect. Convergence of these findings was observed across neuroscience techniques, and for both laboratory and real-life settings. Additionally, evidence also showed associations between neural social stress processing with urban upbringing or current city living-suggesting a mechanistic link to certain mood and anxiety disorders. Finally, environmental diversity was found to be critical for positive affect and individual well-being.

CONCLUSION

Contemporary human-environment interactions and planetary challenges imply greater understanding of the neurological underpinnings on how the urban space affects cognition and emotion. This review provides scientific evidence that could be applied for policy making on improved urban mental health. Several studies showed that high-quality green or blue spaces, and bio-diverse urban areas, are important allies for positive neural, cognitive, and emotional processes. Nonetheless, the spatial perception in social contexts (e.g., city overcrowding) deserves further attention by urban planners and scientists. The implications of these observations for some theories in environmental psychology and research are discussed. Future work should take advantage of technological advancements to better characterize behavior, brain physiology, and environmental factors and apply them to the remaining complexity of contemporary cities.

How Can Flowers and Their Colors Promote Individuals' Physiological and Psychological States during the COVID-19 Lockdown?

The global spread of COVID-19 has disrupted the normality of people’s daily lives, leading the population to social distancing and isolation. The closure of green areas also affected the well-being of the individual during the COVID-19 pandemic. Viewing flowers is expected to have similar positive effects to viewing natural scenery. Therefore, this study investigates how white, red, and yellow flower colors affect individuals’ psychological and physiological well-being. The experiment was conducted in an office-like setting with 50 participants. Participants looked at each flower color for 3 min. Electroencephalograms (EEGs), heart rate variability, and skin conductivity were measured to evaluate physiological responses along with both the semantic differential questionnaire (SD) and the Profile of Mood States (POMS) to assess psychological responses. EEGs showed that the mean values of alpha relative power in the prefrontal lobe were significantly higher when viewing yellow and red flowers vs. white flowers. Furthermore, heart rate variability revealed that viewing yellow and red flowers increased parasympathetic nerve activity significantly. After viewing the yellow and red flowers, the average results for each subscale of the POMS questionnaire improved. The vigor (V) subscale and overall mood status values were significantly improved. The results of the SD method revealed that viewing yellow and red flowers resulted in a significantly higher sense of relaxation, cheerfulness, and comfort than viewing white flowers.

Emotional Responses to the Visual Patterns of Urban Streets: Evidence from Physiological and Subjective Indicators

Despite recent progress in the research of people's emotional response to the environment, the built-rather than natural-environment's emotional effects have not yet been thoroughly examined. In response to this knowledge gap, we recruited 26 participants and scrutinized their emotional response to various urban street scenes through an immersive exposure experiment using virtual reality. We utilized new physiological monitoring technologies that enable synchronized observation of the participants' electroencephalography, electrodermal activity, and heart rate, as well as their subjective indicators. With the newly introduced measurement for the global visual patterns of the built environment, we built statistical models to examine people's emotional response to the physical element configuration and color composition of street scenes. We found that more diverse and less fragmented scenes inspired positive emotional feelings. We also found (in)consistency among the physiological and subjective indicators, indicating a potentially interesting neural-physiological interpretation for the classic form-function dichotomy in architecture. Besides the practical implications on promoting physical environment design, this study combined objective physiology-monitoring technology and questionnaire-based research techniques to demonstrate a better approach to quantify environment-emotion relationships.

Investigating the mental health implications of urban environments with neuroscientific methods and mobile technologies: A systematic literature review

Urbanization is an ongoing global process that is influencing and shaping individual mental health and well-being. This paper aims to provide an overview of the current literature containing state-of-the-art neuroscientific and mobile technologies that have been used to investigate the mental health implications of urban environments. Searches for peer-reviewed primary research articles were conducted in PubMed and SCOPUS, returning 33,443 papers; 90 empirical articles published from 1981 to 2021 were included in the final synthesis. Central findings suggest virtual reality and mobile electroencephalography to be the most commonly used methods, and demanding mood, affect, and health phenomena or states to be the most common concepts of study in both physical built settings and natural urban spaces. Recommendations for both future practice and study noting particular opportunities for future methodological contributions are discussed.

The Function of Color and Structure Based on EEG Features in Landscape Recognition

Both color and structure make important contributions to human visual perception, as well as the evaluation of landscape quality and landscape aesthetics. The EEG equipment liveamp32 was used to record the EEG signals of humans when viewing landscape images, structure images with filtered color, and color images with a filtered structure. The results show that the SVM classifier was the most suitable classifier for landscape classification based on EEG features. The classification accuracy of the landscape picture recognition was up to 98.3% when using beta waves, while the accuracy of the color recognition was 97.5%, and that of the structure recognition was 93.9% when using gamma waves. Secondly, color and structure played a major role in determining the alpha and gamma wave responses, respectively, for all the landscape types, including forest, desert, and water. Furthermore, structure only played a decisive role in forest, while color played a major role in desert and water when using beta waves. Lastly, statistically significant differences between landscape groups and scenario groups with regard to alpha, beta, and gamma rhythms in brain waves were confirmed. The reasonable usage and layout of structure and color will have a very important guiding value for landscape aesthetics in future landscape design and landscape planning.

How Do Urban Environments Affect Young People's Mental Health? A Novel Conceptual Framework to Bridge Public Health, Planning, and Neurourbanism

Childhood and adolescence are crucial periods for mental and social development. Currently, mental illness among young people is a global epidemic, and rates of disorders such as depression and anxiety are rising. Urban living, compared with rural living, is linked with a higher risk of serious mental illness, which is important because the world is urbanizing faster than ever before. Urban environments and their landscapes, designs, and features influence mental health and well-being. However, no conceptual frameworks to date have detailed the effect of urban environments on young people's mental health, and few studies have considered the growing role of digital and social media in this relationship, leading to calls for the development of holistic approaches to describe this relationship. This article synthesizes existing knowledge on urban places (both built and natural environments) and mental health in the public health and urban planning literature and examines the emerging field of neurourbanism (a multidisciplinary study of the effect of urban environments on mental health and brain activity) to enhance current practice and research. We developed 2 novel conceptual frameworks (1 research-oriented, 1 practice-oriented), adapted from Bronfenbrenner's socioecological model, that focus on the relationship between urban environments and young people's mental health. We added a digital and social media contextual level to the socioecological model, and we applied a multilayer concept to highlight potential cross-field interactions and collaborations. The proposed frameworks can help to guide future practice and research in this area.

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia

Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The K nearest neighbor classifier is applied to the extracted features to classify four (happy, relaxed, angry, and sad) emotions. Our experimental results show that among individual modalities, PPG-based features gives the highest accuracy of 78.57% as compared to EEG- and GSR-based features. The fusion of EEG, GSR, and PPG features further improved the classification accuracy to 79.76% (for four emotions) when interacting with tactile enhanced multimedia.

A study on the effects of traditional and olfaction enhanced multimedia on pleasantness classification based on brain activity analysis

Human emotions are recognized in response to content engaging one (audio music) or two human senses (videos). An enhanced sensation with a more realistic feel could be achievable by engaging more than two human senses. In this study, olfaction enhanced multimedia content is generated by synchronizing traditional multimedia content with an olfaction dispenser for engaging olfactory sense in addition to vision and auditory senses. Brain activity of 20 participants (10 males and 10 females) is recorded with a commercially available EEG headband, while engaging with traditional and olfaction enhanced multimedia content. The human brain activity is used to analyze and differentiate the content engaging two (traditional multimedia content) or more than two (olfaction enhanced multimedia content) human senses. For brain activity analysis, we apply a t-test on the power spectra of five frequency sub-bands (delta, theta, alpha, beta, and gamma) of the acquired EEG data in response to traditional and olfaction enhanced multimedia. We observe that alpha, theta, and delta bands are significant in discriminating the response to traditional and olfaction enhanced multimedia content. High brain activity is observed in alpha, theta, and delta bands of frontal channels, while experiencing the olfaction enhanced multimedia content. A user-independent pleasantness classification based on human brain activity is also presented, where classification performance is measured using 10-fold cross validation. We extract features in frequency domain i.e., rational asymmetry (RASM) and differential asymmetry (DASM) from five EEG bands to classify two pleasantness states based on their valence scores using support vector machine (SVM) classifier. Features are further selected based on EEG electrode pair positions and sub-bands. We observed that RASM and DASM features selected from delta band (olfaction enhanced content), and alpha or gamma bands (traditional multimedia content) gives best classification accuracy. We achieved an accuracy of 75%, sensitivity of 77.7%, and specificity of 72.7% in response to olfaction enhanced multimedia content and an accuracy of 68.7%, sensitivity of 71.4%, and specificity of 69.2% in response to traditional multimedia content in classifying pleasant and unpleasant states using SVM. We observed that classification of pleasant state was comparatively better with olfaction enhanced multimedia content than traditional multimedia content.

Comparison between Daytime and Nighttime Scenery Focusing on Restorative and Recovery Effect

The overall purpose of this study was to investigate psycho-physiological variations in human bodies by observing visual images of daytime and nighttime scenery to focus on restorative and recovery effects. Unlike previous studies that have focused on the natural versus built environments, this study aims to compare restorative and recovery potentials between daytime and nighttime. The experiment was conducted by showing a total of 12 images to 60 participants in order to measure the brain response with an electroencephalogram (EEG). As measures of the psychological impact of the images, perceived restorative and recovery scales were used. The self-reported data indicates that daytime sceneries are rated more positively than nighttime sceneries in terms of restorative and recovery effects. According to the EEG results, restorative and recovery feelings have negative relationships with the relative theta band, while positive relationships are shown with the relative alpha band. The correlation analysis between EEG bands and brain regions showed a significant correlation (p < 0.05) with 46 pairs for the daytime scenery stimuli and 52 pairs for the nighttime scenery stimuli. Through the results of the study, we conclude that daytime and nighttime scenery affect restorative feelings and the human brain response through both verbal and non-verbal methods.