Users’ Reactions Captured by Means of an EEG Headset on Viewing the Presentation of Sustainable Designs Using Verbal Narrative

The aim of this paper is to determine whether consu mers accept new arguments for choosing a product that adapts to future needs. It is also seeks to investigate whether the design of products and their ensuing advertising an d promotion through a sustainable approach by means of verbal narrative ads can gener ate a more positive emotional response in the future users of the product than wi th the application of visual narrative ads. To this end, an experiment was conducted consisting in consumers, with and without experience with the product, watching a promotional video based on verbal narrative, created using the new usage scenarios approach, in which the advantages of a sustainable product are shown. The neuronal respons e of the possible users was then measured by means of the EEG headset. In order to b e able to establish a comparison, the same response was also measured in the same con sumers when they viewed a commercial video based on visual narrative about a product with similar characteristics. The results show, among other conclusions, that vie wing the verbal narrative ad first triggers higher emotional values of excitement, bot h in the short and the long term, as well as frustration. It is also observed that havin g no experience with the product causes higher meditation values. This can be useful to enterprises both in order to design their products in such a way as to orientate them towards consumer concerns, and to design advertisements in such a way as to link consumers emotionally with the produ ct

Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety

Emotion And Cognition Analysis Of Intro And Senior CS Students In Software Engineering

he software engineering community has advanced the field in the past few decades towards making the software development life cycle more efficient, robust, and streamlined. Advances such as better integrated development environments and agile workflows have made the process more efficient as well as more flexible. Despite these many achievements software engineers still spend a great deal of time writing, reading and reviewing code. These tasks require a lot of attention from the engineer with many different variables affecting the performance of the tasks. In recent years many researchers have come to investigate how emotion and the way we think about code affect our ability to write and understand another’s code. In this work we look at how developers’ emotions affect their ability to solve software engineering tasks such as code writing and review. We also investigate how and to what extent emotions differ with the software engineering experience of the subject. The methodologies we employed utilize the Emotiv Epoc+ to take readings of subjects’ brain patterns while they perform code reviews as well as write basic code. We then examine how the electrical signals and patterns in the participants differ with experience in the field, as well as their efficiency and correctness in solving the software engineering tasks. We found in our study that senior students had much smaller distribution of emotions than novices with a few different emotion groups emerging. The novices, while able to be grouped, had a much wider dispersion of the emotion aspects recorded

Low-cost methodologies and devices applied to measure, model and self-regulate emotions for Human-Computer Interaction

En aquesta tesi s'exploren les diferents metodologies d'anàlisi de l'experiència UX des d'una visió centrada en usuari. Aquestes metodologies clàssiques i fonamentades només permeten extreure dades cognitives, és a dir les dades que l'usuari és capaç de comunicar de manera conscient. L'objectiu de la tesi és proposar un model basat en l'extracció de dades biomètriques per complementar amb dades emotives (i formals) la informació cognitiva abans esmentada. Aquesta tesi no és només teòrica, ja que juntament amb el model proposat (i la seva evolució) es mostren les diferents proves, validacions i investigacions en què s'han aplicat, sovint en conjunt amb grups de recerca d'altres àrees amb èxit.En esta tesis se exploran las diferentes metodologías de análisis de la experiencia UX desde una visión centrada en usuario. Estas metodologías clásicas y fundamentadas solamente permiten extraer datos cognitivos, es decir los datos que el usuario es capaz de comunicar de manera consciente. El objetivo de la tesis es proponer un modelo basado en la extracción de datos biométricos para complementar con datos emotivos (y formales) la información cognitiva antes mencionada. Esta tesis no es solamente teórica, ya que junto con el modelo propuesto (y su evolución) se muestran las diferentes pruebas, validaciones e investigaciones en la que se han aplicado, a menudo en conjunto con grupos de investigación de otras áreas con éxito.In this thesis, the different methodologies for analyzing the UX experience are explored from a user-centered perspective. These classical and well-founded methodologies only allow the extraction of cognitive data, that is, the data that the user is capable of consciously communicating. The objective of this thesis is to propose a methodology that uses the extraction of biometric data to complement the aforementioned cognitive information with emotional (and formal) data. This thesis is not only theoretical, since the proposed model (and its evolution) is complemented with the different tests, validations and investigations in which they have been applied, often in conjunction with research groups from other areas with success

Az Emotive Epoc +EEG készülék alkalmazásának lehetőségei kü-lönleges bánásmódot igénylő gyermekek fejlesztésében

Jelen tanulmány célja, hogy összefoglalja a különleges bánásmódot igénylő gyermekek fejlesztésében használatos agy-számítógép interfész kapcsolatot alkalmazó technikákat. Az eljárás az adatokat EEG készülék segítségével gyűjti be, amely kardinális információkat nyújt a vizsgálati személy, esetünkben a gyermeke érzelmi és figyelmi állapotát illetően. Ezeket az adatokat számos vizsgálatban mesterséges intelligencia segítségével dolgozták fel, amely lehetővé tette, az érzelmi állapotok azonosítását, csupán az EEG jelek által nyújtott információ alapján. A különböző mentális állapotok figyelemmel követésén kívül a technika lehetőséget nyújt a fejlesztésre is. A neurofeedback például egy hatékony módszer a különleges bánásmódot igénylő gyermekek fejlesztésében. A valós idejű visszajelzés az agyi aktivitásról, különösen a figyelmi vagy relaxációs állapotokról, segít a felhasználónak abban, hogy megtanuljon különböző mentális állapotokat elérni, illetve azokat fenntartani. Ez a képesség meghatározó lehet a figyelemhiányos, illetve önregulációs problémákkal küzdő gyermekek terápiájában. A tanulmányban az agy-számítógép interfész technikák összefoglalása mellett egy költséghatékony EEG készülék is bemutatásra kerül, amely számos kutatás bizonyult nem csak megbízhatónak, hanem könnyen használhatónak is

Emotion and Concentration Integrated System: Applied to the Detection and Analysis of Consumer Preference

With the expansion of consumer market, the appearance becomes an important issue when consumers make decisions under the situation of similar qualities and contents. Accordingly, to attract consumers, companies cost and take much attention on product appearance. Compared to using questionnaires individually, obtaining humans’ thoughts directly from their brains can accurately grasp the actual preference of consumers, which can provide effective and precious decisions for companies. \ In this study, consumers’ brainwaves which are related to concentration and emotion are extracted by wearing a portable and wireless Electroencephalography (EEG) device. The extracted EEG data are then trained by using perceptron learning algorithm (PLA) to make the judgments of concentration and emotion work well with each subject. They are then applied to the detection and analysis of consumer preference. Finally, the questionnaires are also performed and used as the reference on training process. They are integrated with brainwaves data to create one prediction model which can improve the accuracy significantly. The Partial Least Squares is used to compare the correlation between different factors in the model, to ensure the test can accurately meet consumers’ thoughts

Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals

Nowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person’s behavior and emotions based on brain signals is the brain–computer interface (BCI). Although previous works have already proposed the classification of emotions in people with sensory disabilities using machine learning techniques, a model of recognition of emotions in people with visual disabilities has not yet been evaluated. Consequently, in this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor.This work was supported by the National Council of Science and Technology of Mexico (CONACyT), through grant number 709656

REAL-TIME EEG BASED OBJECT RECOGNITION

A Brain Computer Interface (BCI) provides a communication path between human brain and the computer system. The major goal of BCI research is to develop a system that allows disabled people to communicate with other people, to control artificial limbs, or to control their environment. BCI is a challenging topic of computer vision research. It is extensively used by disabled people to communicate with other persons and helps to interact with the external environments. This paper provides an insight into object recognition by analyzing EEG signals in real-time. Three machine learning algorithms are implemented which are used for classification by supervised learning, namely Decision Trees, K-Nearest Neighbors and Support Vector Machine (SVM), multiple training sets and users are taken into account during the experiment and the efficiency of each algorithm is compared to suggest the best suited algorithm for this purpose

Fear Feedback Loop: Creative and Dynamic Fear Experiences Driven by User Emotion

This thesis examines whether it is possible to generate fear-eliciting media that custom fits to the user. The system described uses a genetic algorithm to produce images that get more scary through the generations in reaction to either physiological signals obtained from the user or a user-provided fear rating. The system was able to detect differing levels of fear using a regression trained on EEG and heart rate data gathered while users view clips from horror movies. It was also found to produce images with significantly higher fear ratings at the fifth generation as compared to the first generation. These higher scoring images were found to be unique between subjects