202 research outputs found

    Design of a Multimodal Mixed Reality Work Environment with Wearable Technology

    Get PDF
    Issues relating to health and well-being at work have risen in prominence, exerting negative effects upon both individuals and organizations. Two main contributing factors are a lack of awareness of one's bodily status and a lack of accessible and effective adjustment mechanisms. Through a comprehensive literature review in the fields of Physiology and Biosensors, Mixed Reality, and Environmental Psychology, this study examines the impacts of environmental attributes and investigates how technology can be leveraged to provide solutions for coping with changes in bodily status caused by internal or external stressors. To address the problem, this study proposes and develops a hybrid wearable and Mixed Reality system prototype that enhances awareness of bodily status and provides mediation. This prototype can adapt to the individual's real-time biometric data through a wearable glove, and provide personalized feedback in a Multimodal Mixed Reality working environment. A small-scale user testing was conducted and has yielded positive feedback. Ultimately, this study highlights that the implementation of a wearable and Mixed Reality system has the potential to contribute to a healthier and more productive workplace for individuals and organizations alike

    Consumer Neuroscience e Brand Relationship: misurare l’associazione implicita tra il SĂ© del consumatore e il brand.

    Get PDF
    Il presente elaborato si focalizza sulla connessione tra Consumer Neuroscience e Brand Relationship con un focus specifico sul SĂ© del consumatore, analizzato attraverso uno strumento di misurazione indiretta del comportamento. L’obiettivo Ăš stato quello di contribuire alla validazione e all’utilizzo nel contesto italiano di un SC-IAT per lo studio dell’associazione tra SĂ© e brand, interpretandone i risultati tramite un’analisi di matrice neuroscientifica su stimoli brand-related. Il vantaggio di questo strumento, rispetto allo IAT tradizionale, Ăš quello di poter ‘fotografare’ un’istantanea sulla relazione senza la necessitĂ  di utilizzare una dimensione comparativa. Misurando direttamente la forza dell’associazione tra il concetto del brand e quello del SĂ©. Per farlo, l’autore Ăš passato attraverso fasi distinte che hanno prima indagato gli aspetti puramente psicometrici dello strumento, per dedicarsi successivamente a un test neuroscientifico. I risultati hanno evidenziato delle buone performance del SC-IAT, cosĂŹ pensato, suggerendo approfondimenti futuri e applicazioni a brand dalla differente architettura. Inoltre, l’analisi neurofisiologica ha evidenziato come lo strumento possa risultare efficace nel fornire un’interpretazione aggiuntiva agli indicatori neurofisiologici testati durante la visualizzazione di uno stimolo relativo al brand

    Assessing Executive Function Impairments and Comorbidity between ADHD and Stuttering

    Get PDF
    Stuttering and ADHD are often considered ‘comorbid’ because different types of symptoms and processing issues occur in, for example, fluency, attention and working memory. This thesis addresses whether or not these shared factors signify fundamental similarities between stuttering and ADHD that distinguish them from typical controls. This is done in two main ways: First, a comparison is made of details of performance on attention capabilities using a range of behavioural and physiological measures in various test environments, including Web and VR approaches; Second, modelling analyses are conducted that compare networks representing participants’ performance across groups. Using the Load Theory of Attention methodology (Chapter 2), which addresses how to focus attention and ignore distractions up to a point where load exceeds perceptual capacity, it was observed that the performance of participants who stutter was significantly lower from the performance of controls in the auditory selective and divided attention tasks. The results showed that tasks in which attention demands enhanced were effective in detecting limitations in audio processing by PWS. Extending the task in the visual, audio and audio-visual domains in a virtual reality environment in people who stutter, (PWS) as well as people with ADHD (PWADHD) it was found that while audio was more affected in PWS, audio and audio-visual domains were affected in PWADHD. Lastly, Network Models (NMs) from the measures examined showed that comorbidity between PWS and PWADHD is limited. For better clinical assessments of attention, fluency and working memory problems, a Linear Mixed Model (LMM) was included in chapter 3 to understand if gender imbalance affected the results of PWS, PWADHD and controls in a selective attention task. LMM correctly determined that the gender imbalance did not affect the participants performance and PWS performed significantly worse from PWADHD showing that the groups were not comorbid and PWS is impaired in selective attention tasks. Further investigations were made in chapter 4 on PWS, PWADHD and controls in which data collected was extended to behavioural as well as physiological measures in a selective attention task implemented in a Virtual Reality (VR) environment. Although both PWS and PWADHD differed from controls with lower performance on the task, impulsive behaviours were only present in PWADHD (higher NOF) while inattentiveness was observed only in PWS (lower FD, higher theta activity). The architecture of NMs was different between PWS and PWADHD in task performance confirming again that comorbidity between groups is overstated while the frontal cortex is impaired in both groups as shown by NMs from EEG measures. While previous chapters showed that selective and divided attention tasks in Executive Function (EF) can correctly assess attention problems in PWS, chapters 5, 6 and 7 aimed at understanding which attention type in EF is impaired in PWADHD and can correctly assess attention problems in this group. An extensive investigation was made from 10 VR tasks that drew upon different attention types on behavioural measures and responses from questionnaires (chapter 5), eye measures (chapter 6) and brain activity (chapter 7). PWADHD were compared to controls on all the measures. NMs showed that sustained attention tasks in all domains and switched attention task only in the visual domain assessed ADHD traits in PWADHD on the measures examined. Furthermore, prefrontal cortex was impaired as shown from NMs in EEG measures. Finally, NMs were compared between controls, PWADHD and PWS in chapter 8 on cognitive factors including attention, fluency and working memory. NMs confirmed previous findings that the comorbidity of symptoms of both disorders is overstated. NM architecture between controls and PWADHD was similar, but both differed from PWS. Working memory was a strong factor that affected attention in all groups but the way it affected attention differed between PWS and PWADHD

    25th Annual Computational Neuroscience Meeting: CNS-2016

    Get PDF
    Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

    Interactive brains:How infant cognition interacts with the dynamic social world

    Get PDF
    Research taking a cognitive neuroscience approach has shed light on social cognition during infancy. These studies have provided invaluable knowledge about how infants process social information, but a number of concepts regarding infant social cognition are often discussed based on research utilising rigidly controlled experimental paradigms where the role of infants is typically passive as an observer of stimuli. Increasing evidence suggests differences between the social cognitive processes that occurs when we act as observers of others (a ‘third-person’ perspective) and the processes that emerge when we are actively engaging with other people in an interactional context (a ‘second-person’ perspective) (e.g., Redcay and Schilbach, 2019; Siposova & Carpenter, 2019). Accordingly, there has been a growing recognition that we need a ‘second-person’ perspective, as compared to conventional “third-person” approach. The aim of the current thesis is to explore the interplay between infant cognition and the social world surrounding them, by moving research settings to a more naturalistic and dynamic one where infants are positioned as part of interaction. Towards this goal, Study 1 (Chapter 2) reviewed the current progress of “second-person” neuroscience research to evaluate the validity and robustness of simultaneous dual brain scanning techniques, often referred to as hyperscanning. The review identified large heterogeneity in reported effect sizes between published studies, suggesting the need to improve comparability of research, such as establishing standardised methods or promoting open science practices including code and data sharing to achieve higher reproducibility. This thesis then turned to research using various techniques from a conventional screen-based paradigm to a more dynamic setting, with the aim of building a stable platform towards second-person cognitive neuroscience approaches that investigate infant cognition while the infant actively interacts with other people. Study 2 (Chapter 3) explored how infants encode information differently from two adults who give gaze cues to a target object with different levels of accuracy. Whilst the study utilised a conventional event-related potential paradigm using screen-based stimuli, this paradigm could be adapted to enable future studies to investigate how infants’ social cognitive ability to discriminate reliable and unreliable informants can inform their subsequent behaviour observed in a social interactional behavioural task. Study 3 (Chapter 4) moved towards the use of more dynamic video stimuli and explored the neural processing of unexpected events. The study identified challenges in using dynamic perceptual inputs as stimuli. Study 4 (Chapter 5) transitioned into more naturalistic social contexts and analysed infant cognition while 10-month-old infants were faced with an adult demonstrating novel object labels in a live interaction. The study not only showed the feasibility of second-person neuroscientific research with infant participants, but also advanced our knowledge about infant word learning a step further, and demonstrated the trajectory from the encoding of semantic word information to its consolidation as knowledge. Study 5 (Chapter 6) also utilised a naturalistic interactional setting where infants were able to actively engage in a social task with an experimenter in a live manner, and aimed to identify systematic differences in neural activity between 9-month-old infants who make perseverative errors originally reported by Piaget (1954) and those who do not. This study was, to our knowledge, the first of its kind to validate the feasibility of utilising neurophysiological measures in this traditional interactive behavioural paradigm, in such a way that it does not interfere with the standard procedure. This thesis produced a series of studies which jointly demonstrate the potential for conducting research in a more dynamic setting that investigates infant social cognition taking a ‘second-person’ cognitive neuroscience approach to advance our knowledge about the intricate interaction between infant cognition, behaviour and the environment. We conclude this thesis by addressing the challenges of such an approach, to which we also attempt to propose solutions, as well as discussing future directions for the field

    Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

    Get PDF
    This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

    Cognitive training optimization with a closed-loop system

    Full text link
    Les interfaces cerveau-machine (ICMs) nous offrent un moyen de fermer la boucle entre notre cerveau et le monde de la technologie numĂ©rique. Cela ouvre la porte Ă  une plĂ©thore de nouvelles applications oĂč nous utilisons directement le cerveau comme entrĂ©e. S’il est facile de voir le potentiel, il est moins facile de trouver la bonne application avec les bons corrĂ©lats neuronaux pour construire un tel systĂšme en boucle fermĂ©e. Ici, nous explorons une tĂąche de suivi d’objets multiples en 3D, dans un contexte d’entraĂźnement cognitif (3D-MOT). Notre capacitĂ© Ă  suivre plusieurs objets dans un environnement dynamique nous permet d’effectuer des tĂąches quotidiennes telles que conduire, pratiquer des sports d’équipe et marcher dans un centre commercial achalandĂ©. MalgrĂ© plus de trois dĂ©cennies de littĂ©rature sur les tĂąches MOT, les mĂ©canismes neuronaux sous- jacents restent mal compris. Ici, nous avons examinĂ© les corrĂ©lats neuronaux via l’électroencĂ©phalographie (EEG) et leurs changements au cours des trois phases d’une tĂąche de 3D-MOT, Ă  savoir l’identification, le suivi et le rappel. Nous avons observĂ© ce qui semble ĂȘtre un transfert entre l’attention et la de mĂ©moire de travail lors du passage entre le suivi et le rappel. Nos rĂ©sultats ont rĂ©vĂ©lĂ© une forte inhibition des frĂ©quences delta et thĂȘta de la rĂ©gion frontale lors du suivi, suivie d’une forte (rĂ©)activation de ces mĂȘmes frĂ©quences lors du rappel. Nos rĂ©sultats ont Ă©galement montrĂ© une activitĂ© de retard contralatĂ©rale (CDA en anglais), une activitĂ© nĂ©gative soutenue dans l’hĂ©misphĂšre contralatĂ©rale aux positions des Ă©lĂ©ments visuels Ă  suivre. Afin de dĂ©terminer si le CDA est un corrĂ©lat neuronal robuste pour les tĂąches de mĂ©moire de travail visuelle, nous avons reproduit huit Ă©tudes liĂ©es au CDA avec un ensemble de donnĂ©es EEG accessible au public. Nous avons utilisĂ© les donnĂ©es EEG brutes de ces huit Ă©tudes et les avons analysĂ©es avec le mĂȘme pipeline de base pour extraire le CDA. Nous avons pu reproduire les rĂ©sultats de chaque Ă©tude et montrer qu’avec un pipeline automatisĂ© de base, nous pouvons extraire le CDA. RĂ©cemment, l’apprentissage profond (deep learning / DL en anglais) s’est rĂ©vĂ©lĂ© trĂšs prometteur pour aider Ă  donner un sens aux signaux EEG en raison de sa capacitĂ© Ă  apprendre de bonnes reprĂ©sentations Ă  partir des donnĂ©es brutes. La question Ă  savoir si l’apprentissage profond prĂ©sente vraiment un avantage par rapport aux approches plus traditionnelles reste une question ouverte. Afin de rĂ©pondre Ă  cette question, nous avons examinĂ© 154 articles appliquant le DL Ă  l’EEG, publiĂ©s entre janvier 2010 et juillet 2018, et couvrant diffĂ©rents domaines d’application tels que l’épilepsie, le sommeil, les interfaces cerveau-machine et la surveillance cognitive et affective. Enfin, nous explorons la possibilitĂ© de fermer la boucle et de crĂ©er un ICM passif avec une tĂąche 3D-MOT. Nous classifions l’activitĂ© EEG pour prĂ©dire si une telle activitĂ© se produit pendant la phase de suivi ou de rappel de la tĂąche 3D-MOT. Nous avons Ă©galement formĂ© un classificateur pour les essais latĂ©ralisĂ©s afin de prĂ©dire si les cibles Ă©taient prĂ©sentĂ©es dans l’hĂ©michamp gauche ou droit en utilisant l’activitĂ© EEG. Pour la classification de phase entre le suivi et le rappel, nous avons obtenu un 80% lors de l’entraĂźnement d’un SVM sur plusieurs sujets en utilisant la puissance des bandes de frĂ©quences thĂȘta et delta des Ă©lectrodes frontales.Brain-computer interfaces (BCIs) offer us a way to close the loop between our brain and the digital world of technology. It opens the door for a plethora of new applications where we use the brain directly as an input. While it is easy to see the disruptive potential, it is less so easy to find the right application with the right neural correlates to build such closed-loop system. Here we explore closing the loop during a cognitive training 3D multiple object tracking task (3D-MOT). Our ability to track multiple objects in a dynamic environment enables us to perform everyday tasks such as driving, playing team sports, and walking in a crowded mall. Despite more than three decades of literature on MOT tasks, the underlying and intertwined neural mechanisms remain poorly understood. Here we looked at the electroencephalography (EEG) neural correlates and their changes across the three phases of a 3D-MOT task, namely identification, tracking and recall. We observed what seems to be a handoff between focused attention and working memory processes when going from tracking to recall. Our findings revealed a strong inhibition in delta and theta frequencies from the frontal region during tracking, followed by a strong (re)activation of these same frequencies during recall. Our results also showed contralateral delay activity (CDA), a sustained negativity over the hemisphere contralateral to the positions of visual items to be remembered. In order to investigate if the CDA is a robust neural correlate for visual working memory (VWM) tasks, we reproduced eight CDA-related studies with a publicly accessible EEG dataset. We used the raw EEG data from these eight studies and analysed all of them with the same basic pipeline to extract CDA. We were able to reproduce the results from all the studies and show that with a basic automated EEG pipeline we can extract a clear CDA signal. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. In order to address such question, we reviewed 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. Finally, we explore the potential for closing the loop and creating a passive BCI with a 3D-MOT task. We classify EEG activity to predict if such activity is happening during the tracking or the recall phase of the 3D-MOT task. We also trained a classifier for lateralized trials to predict if the targets were presented on the left or right hemifield using EEG brain activity. For the phase classification between tracking and recall, we obtained 80% accuracy when training a SVM across subjects using the theta and delta frequency band power from the frontal electrodes and 83% accuracy when training within subjects

    TRIZ Future Conference 2004

    Get PDF
    TRIZ the Theory of Inventive Problem Solving is a living science and a practical methodology: millions of patents have been examined to look for principles of innovation and patterns of excellence. Large and small companies are using TRIZ to solve problems and to develop strategies for future technologies. The TRIZ Future Conference is the annual meeting of the European TRIZ Association, with contributions from everywhere in the world. The aims of the 2004 edition are the integration of TRIZ with other methodologies and the dissemination of systematic innovation practices even through SMEs: a broad spectrum of subjects in several fields debated with experts, practitioners and TRIZ newcomers

    Social and Affective Neuroscience of Everyday Human Interaction

    Get PDF
    This Open Access book presents the current state of the art knowledge on social and affective neuroscience based on empirical findings. This volume is divided into several sections first guiding the reader through important theoretical topics within affective neuroscience, social neuroscience and moral emotions, and clinical neuroscience. Each chapter addresses everyday social interactions and various aspects of social interactions from a different angle taking the reader on a diverse journey. The last section of the book is of methodological nature. Basic information is presented for the reader to learn about common methodologies used in neuroscience alongside advanced input to deepen the understanding and usability of these methods in social and affective neuroscience for more experienced readers
    • 

    corecore