CEEDs: Unleashing the Power of the Subconscious

Lessiter, Jane; Miotto, Andrea; Freeman, Jonathan; Verschure, Paul F. M. J.; Bernardet, Ulysses

doi:10.1016/j.procs.2011.09.069

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…This technology permits to model representational elements analog to real phenomena as well as highly abstract non-representation forms describing complex high-dimensional data in a controlled environment. The exploration of data in XIM is conceptualized as an integrative narrative of varying forms where implicit and explicit responses as well as neurophysiological signals from the user can be utilized to modulate data representation (Bernardet et al, 2011 ; Lessiter et al, 2011 ; Verschure, 2011 ; Omedas et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Fast mental states decoding in mixed reality

Massari

Pacheco

Malekshahi

et al. 2014

Front. Behav. Neurosci.

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The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR.

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Section: Introductionmentioning

confidence: 99%

Fast mental states decoding in mixed reality

Massari

Pacheco

Malekshahi

et al. 2014

Front. Behav. Neurosci.

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“…The analysis of complex datasets is a task that is becoming very common in the digital era where extremely large datasets are collected that overpass our capacity to extract useful meaning [2], [4]. There is a need of new tools in science and industries to analyze the datasets and support the process of understanding.…”

Section: Introductionmentioning

confidence: 99%

XIM-engine

Omedas

Betella

Zucca

et al. 2014

Proceedings of the 2014 Virtual Reality International Conference

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The development of systems that allow multimodal interpretation of human-machine interaction is crucial to advance our understanding and validation of theoretical models of user behavior. In particular, a system capable of collecting, perceiving and interpreting unconscious behavior can provide rich contextual information for an interactive system. One possible application for such a system is in the exploration of complex data through immersion, where massive amounts of data are generated every day both by humans and computer processes that digitize information at different scales and resolutions thus exceeding our processing capacity. We need tools that accelerate our understanding and generation of hypotheses over the datasets, guide our searches and prevent data overload. We describe XIMengine, a bio-inspired software framework designed to capture and analyze multi-modal human behavior in an immersive environment. The framework allows performing studies that can advance our understanding on the use of conscious and unconscious reactions in interactive systems.

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“…heart rate, electrodermal activity and gaze behaviour). Since only a small subset of sensory input of the human brain actually reaches conscious awareness, these otherwise hidden cues can help guide users' discovery of patterns and meaning within the datasets [10].…”

Section: Introductionmentioning

confidence: 99%

A sensing architecture for empathetic data systems

Wagner

Lingenfelser

André

et al. 2013

Proceedings of the 4th Augmented Human International Conference

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Today's increasingly large and complex databases require novel and machine aided ways of exploring data. To optimize the selection and presentation of data, we suggest an unconventional approach. Instead of exclusively relying on explicit user input to specify relevant information or to navigate through a data space, we exploit the power and potential of the users' unconscious processes in addition. To this end, the user is immersed in a mixed reality environment while his bodily reactions are captured using unobtrusive wearable devices. The users' reactions are analyzed in real-time and mapped onto higher-level psychological states, such as surprise or boredom, in order to trigger appropriate system responses that direct the users' attention to areas of potential interest in the visualizations. The realization of such a close experience-based human-machine loop raises a number of technical challenges, such as the real-time interpretation of psychological user states. The paper at hand describes a sensing architecture for empathetic data systems that has been developed as part of such a loop and how it tackles the diverse challenges.

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CEEDs: Unleashing the Power of the Subconscious

Cited by 6 publications

References 1 publication

Fast mental states decoding in mixed reality

Fast mental states decoding in mixed reality

XIM-engine

A sensing architecture for empathetic data systems

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