Cette thèse porte sur la conception et l'évaluation de systèmes interactifs utilisant des interfaces cerveau-machine (BCI pour Brain-Computer Interfaces). Ce type d'interfaces s'est développé dans les années récentes tout d'abord dans le domaine du handicap, afin de fournir aux grands handicapés des moyens d'interaction et de communication, et plus récemment dans d'autres domaines comme celui des jeux vidéo. Néanmoins, la plupart des travaux ont porté sur l'identification des signaux du cerveau susceptibles de porter une information utile, et sur les traitements nécessaires à l'extraction de cette information. Peu de travaux ont porté sur les aspects d'utilisabilité et de prise en compte des facteurs humains dans l'ensemble du système interactif. Cette thèse se concentre sur les systèmes basées sur SSVEP (steady-state visually evoked potentials), et se propose d'étudier l'ensemble du système interactif cerveau-machine, selon les critères de l'interaction homme-machine (IHM). Plus précisément, les points étudiés portent sur la demande cognitive, la frustration de l'utilisateur, les conditions de calibration, et les BCI hybrides. / This PhD deals with the conception and evaluation of interactive systems based on Brain-Computer Interfaces (BCI). This type of interfaces has developed in recent years, first in the domain of handicaps, in order to provide disabled people means of interaction and communication, and more recently in other fields as video games. However, most of the research so far focused on the identification of cerebral pattern carrying useful information, a on signal processing for the detection of these patterns. Less attention has been given to usability aspects. This PhD focuses on interactive systems based on Steady-State Visually Evoked Potentials (SSVEP), and aims at considering the interactive system as a whole, using the concepts of Human-Computer Interaction. More precisely, a focus is made on cognitive demand, user frustration, calibration conditions, and hybrid BCIs.

With the progress of computer technology, computers are expected to be more intelligent in the interaction with humans, presenting information according to the user's psychological and physiological characteristics. However, computer users with visual problems may encounter difficulties on the perception of icons, menus, and other graphical information displayed on the screen, limiting the efficiency of their interaction with computers. In this dissertation, a personalized and dynamic image precompensation method was developed to improve the visual performance of the computer users with ocular aberrations. The precompensation was applied on the graphical targets before presenting them on the screen, aiming to counteract the visual blurring caused by the ocular aberration of the user's eye. A complete and systematic modeling approach to describe the retinal image formation of the computer user was presented, taking advantage of modeling tools, such as Zernike polynomials, wavefront aberration, Point Spread Function and Modulation Transfer Function. The ocular aberration of the computer user was originally measured by a wavefront aberrometer, as a reference for the precompensation model. The dynamic precompensation was generated based on the resized aberration, with the real-time pupil diameter monitored. The potential visual benefit of the dynamic precompensation method was explored through software simulation, with the aberration data from a real human subject. An "artificial eye'' experiment was conducted by simulating the human eye with a high-definition camera, providing objective evaluation to the image quality after precompensation. In addition, an empirical evaluation with 20 human participants was also designed and implemented, involving image recognition tests performed under a more realistic viewing environment of computer use. The statistical analysis results of the empirical experiment confirmed the effectiveness of the dynamic precompensation method, by showing significant improvement on the recognition accuracy. The merit and necessity of the dynamic precompensation were also substantiated by comparing it with the static precompensation. The visual benefit of the dynamic precompensation was further confirmed by the subjective assessments collected from the evaluation participants.

User studies play a vital role in data visualization research because they help measure the strengths and weaknesses of different visualization techniques quantitatively. In addition, they provide insight into what makes one technique more effective than another; and they are used to validate research contributions in the field of information visualization. For example, a new algorithm, visual encoding, or interaction technique is not considered a contribution unless it has been validated to be better than the state of the art and its competing alternatives or has been validated to be useful to intended users. However, conducting user studies is challenging, time consuming, and expensive. User studies generally requires careful experimental designs, iterative refinement, recruitment of study participants, careful management of participants during the run of the studies, accurately collecting user responses, and expertise in statistical analysis of study results. There are several variables that are taken into consideration which can impact user study outcome if not carefully managed. Hence the process of conducting user studies successfully can take several weeks to months. In this dissertation, we investigated how to design an online framework that can reduce the overhead involved in conducting controlled user studies involving web-based visualizations. Our main goal in this research was to lower the overhead of evaluating data visualizations quantitatively through user studies. To this end, we leveraged current research opportunities to provide a framework design that reduces the overhead involved in designing and running controlled user studies of data visualizations. Specifically, we explored the design and implementation of an open-source framework and an online service (VisUnit) that allows visualization designers to easily configure user studies for their web-based data visualizations, deploy user studies online, collect user responses, and analyze incoming results automatically. This allows evaluations to be done more easily, cheaply, and frequently to rapidly test hypotheses about visualization designs. We evaluated the effectiveness of our framework (VisUnit) by showing that it can be used to replicate 84% of 101 controlled user studies published in IEEE Information Visualization conferences between 1995 and 2015. We evaluated the efficiency of VisUnit by showing that graduate students can use it to design sample user studies in less than an hour. Our contributions are two-fold: first, we contribute a flexible design and implementation that facilitates the creation of a wide range of user studies with limited effort; second, we provide an evaluation of our design that shows that it can be used to replicate a wide range of user studies, can be used to reduce the time evaluators spend on user studies, and can be used to support new research.

Les interfaces cerveau machine (BCI pour Brain Computer Interfaces) non invasives permettent à leur utilisateur de contrôler une machine par la pensée. Ce dernier doit porter un dispositif d'acquisition de signaux électroencéphalographiques (EEG), qui sont dotés d'un rapport signal sur bruit assez faible ; à ceci s'ajoute l’importante variabilité tant à travers les sessions d'utilisation qu’à travers les utilisateurs. Par conséquent, la calibration du BCI est souvent nécessaire avant son utilisation. Cette thèse étudie les sources de cette variabilité, dans le but d'explorer, concevoir, et implémenter des méthodes d'autocalibration. Nous étudions la variabilité des potentiels évoqués, particulièrement une composante tardive appelée P300. Nous nous penchons sur trois méthodes d’apprentissage par transfert : la Géométrie Riemannienne, le Transport Optimal, et l’apprentissage ensembliste. Nous proposons un modèle de l'EEG qui tient compte de la variabilité. Les paramètres résultants de nos analyses nous servent à calibrer ce modèle et à simuler une base de données, qui nous sert à évaluer la performance des méthodes d’apprentissage par transfert. Puis ces méthodes sont combinées et appliquées à des données expérimentales. Nous proposons une méthode de classification basée sur le Transport Optimal dont nous évaluons la performance. Ensuite, nous introduisons un marqueur de séparabilité qui nous permet de combiner Géométrie Riemannienne, Transport Optimal et apprentissage ensembliste. La combinaison de plusieurs méthodes d’apprentissage par transfert nous permet d’obtenir un classifieur qui s’affranchit des différentes sources de variabilité des signaux EEG. / Non-invasive Brain Computer Interfaces (BCIs) allow a user to control a machine using only their brain activity. The BCI system acquires electroencephalographic (EEG) signals, characterized by a low signal-to-noise ratio and an important variability both across sessions and across users. Typically, the BCI system is calibrated before each use, in a process during which the user has to perform a predefined task. This thesis studies of the sources of this variability, with the aim of exploring, designing, and implementing zero-calibration methods. We review the variability of the event related potentials (ERP), focusing mostly on a late component known as the P300. This allows us to quantify the sources of EEG signal variability. Our solution to tackle this variability is to focus on adaptive machine learning methods. We focus on three transfer learning methods: Riemannian Geometry, Optimal Transport, and Ensemble Learning. We propose a model of the EEG takes variability into account. The parameters resulting from our analyses allow us to calibrate this model in a set of simulations, which we use to evaluate the performance of the aforementioned transfer learning methods. These methods are combined and applied to experimental data. We first propose a classification method based on Optimal Transport. Then, we introduce a separability marker which we use to combine Riemannian Geometry, Optimal Transport and Ensemble Learning. Our results demonstrate that the combination of several transfer learning methods produces a classifier that efficiently handles multiple sources of EEG signal variability.

To be successful citizen science projects need to do two key things: (1) they need to meaningfully engage the public and they must also provide people with the tools, expertise, and/or training needed to participate in rigorous research that can be used by the scientific community. In some ways, these requirements are potentially at odds. Emphasis on rigor and expertise risks excluding members of the public who do not feel qualified to participate in esoteric or technically difficult scientific research. Conversely, projects that eschew rigorous methods in favor of wider participation might lead to bad data that cannot be used to draw any meaningful conclusions to expand scientific understanding. How then do those who are aiming to design successful citizen science programs create tools and processes that facilitate both active engagement and meaningful scientific results for perceived non-expert researchers? This paper uses a case study of the Outsmart Invasive Species Project (Outsmart) to explore how visual media shape the experiences of citizen scientists participating in a data collection project. Outsmart uses visual media such as photographs and videos to train users in identifying invasive species, and asks them to submit their own location-tagged pictures to a central database for review by a trained research team. Using ethnographic field observation, we focused on how visual media serve to improve engagement in non-expert Outsmart users by building confidence and expertise. Our work can provide guidance to other citizen science projects in how to best use visual media to empower citizens and improve scientific outcomes.

Tens of thousands of years ago, humans drew sketches that we can see and identify even today. Sketches are the oldest recorded form of human communication and are still widely used. The universality of sketches supersedes that of culture and language. Despite the universal accessibility of sketches by humans, computers are unable to interpret or even correctly identify the contents of sketches drawn by humans with a practical level of accuracy. In my thesis, I demonstrate that the accuracy of existing sketch recognition techniques can be improved by optimizing the classification criteria. Current techniques classify a 20,000 sketch crowd-sourced dataset with 56% accuracy. I classify the same dataset with 52% accuracy, but identify factors that have the greatest effect on the accuracy. The ability for computers to identify human sketches would be useful particularly in pictionary-like games and other kinds of human-computer interaction; the concepts from sketch recognition could be extended to other kinds of object recognition.

A company, Unanimous AI, has created a software platform that allows individuals to come together as a group or a human swarm to make decisions. These human swarms amplify the decision-making capabilities of both the individuals and the group. One way Unanimous AI increases the swarm’s collective decision-making capabilities is by limiting the swarm to more informed individuals on the given topic. The previous way Unanimous AI selected users to enter the swarm was improved upon by a new methodology that is detailed in this study. This new methodology implements a new type of survey that collects data that is more indicative of a user’s knowledge on the subject than the previous survey. This study also identifies better metrics for predicting each user’s performance when predicting Major League Baseball game outcomes throughout a given week. This study demonstrates that the new machine learning models and data extraction schemes are approximately 12% more accurate than the currently implemented methods at predicting user performance. Finally, this study shows how predicting a user’s performance based purely on their inputs can increase the average performance of a group by limiting the group to the top predicted performers. This study shows that by limiting the group to the top predicted performers across five different weeks of MLB predictions, the average group performance was increased up to 5.5%, making this a superior method.

Diminished reality, as a sub-topic of augmented reality where digital information is overlaid on an environment, is the perceived removal of an object from an environment. Previous approaches to diminished reality used digital replacement techniques, inpainting, and multi-view homographies. However, few used a virtual representation of the real environment, limiting their domains to planar environments. This thesis provides a framework to achieve real-time diminished reality on an augmented reality headset. Using state-of-the-art hardware, we combine a virtual representation of the real environment with inpainting to remove existing objects from complex environments. Our work is found to be competitive with previous results, with a similar qualitative outcome under the limitations of available technology. Additionally, by implementing new texturing algorithms, a more detailed representation of the real environment is achieved.

There’s no denying the ever increasing presence of social networking in our daily lives. Every day, people share what they are thinking, doing, and experiencing. But even more so, they check their favorite networks to see what the people in their lives are sharing. Social networking has become so prevalent that most applications incorporate it since it keeps users engaged and beckons them back to the application again and again. PolyXpress is an interactive, location-based storytelling mobile application that functions as a platform for creating and experiencing stories. Written as a research project at California Polytechnic State University, it allows users to play through stories in real-world locations by using their smart phones. However, in an age of social networking, PolyXpress falls behind the curve, as it does not contain any social features. The work in this thesis aims to test if adding social networking features to PolyX- press will increase user engagement by performing a usability study. The new social features allow users to participate in public forums about stories, message friends while playing stories, and view their friends’ experiences within the app. The results of the study indicate that the overall user experience of PolyXpress was not increased by the social networking features; however, these features are desired and liked by the users. 70% of the experimental group enjoyed using the social features, while 30% remained indifferent. The problem is that the new features disrupted app satisfac- tion, as UI satisfaction decreased from 100% with the control group to 40% with the experimental group.

Bakgrund: Stroke drabbar miljontals människor världen över varje år och medför ofta ensidiga motoriska nedsättningar som allvarligt reducerar förmågan till självständighet i vardagen. Fysioterapin efter stroke sker därför vanligen genom uppgiftsorienterad träning riktad mot att rehabilitera den motoriska förmågan på den affekterade sidan så att patienten kan återgå till ett självständigt liv. Men processen ställer stora krav på patienten som inte alltid kan förväntas uppnå bästa resultat med sin rehabilitering. Därför forskas det alltmer på innovativa teknologiska hjälpmedel med potential att assistera strokepatient såväl som fysioterapeut i rehabiliteringen. Exoskelett och hjärndatorgränssnitt (BCI) är två sådana hjälpmedel som undersöktes i denna studie. Syfte: Studien hade syftet att sammanställa det vetenskapliga stödet för tillämpning av BCI-styrda exoskelett (BCI-Exo) vid rehabilitering av motorisk arm- och handfunktion efter stroke i dess subakuta samt kroniska fas. Metod: Litteratursökningar utfördes i databaserna PEDRO, PUBMED, AMED och CINAHL vilket gav 22 träffar som efter granskning och sållning resulterade i att fyra artiklar inkluderades i studien. Resultat: Samtliga studier redovisade statistiskt signifikanta förbättringar av motorisk handfunktion i interventionsgruppen jämfört med kontrollgruppen utifrån de utfallsmått som tillämpades. Konklusion: Resultatet indikerade att BCI-Exo kan främja återhämtning och neuroplasticitet för strokepatienter oavsett vilken fas de infinner sig i. Dock är teknologin fortfarande relativt ny varvid fler studier behöver utföras för att bättre specificera och förstå för- och nackdelar jämfört med konventionella behandlingsmetoder. / Background: Stroke affects millions of people around the world each year and often results in unilateral motor impairments that severely reduce the ability for independence in everyday life. Physiotherapy after stroke is therefore usually performed through task-oriented training aimed at rehabilitating the motor functional ability of the affected side so that the patient can return to an independent life. But the process places great demands on the patient who cannot always be expected to achieve the best results from their rehabilitation. Therefore, innovative technologies are increasingly being researched with the potential to assist stroke patients as well as physical therapists in the rehabilitation process. Exoskeletons and brain-computer interfaces (BCI) are two such rehabilitative tools that were investigated in this study. Objective: The study aimed to compile the scientific support for the use of BCI-controlled exoskeletons (BCI-Exo) in motor functional arm and hand rehabilitation after stroke in its subacute and chronic phase. Method: Literature searches were conducted in the databases PEDRO, PUBMED, AMED and CINAHL, which resulted in 22 hits which, after review and screening, resulted in four articles being included in the study. Results: All studies reported statistically significant improvements regarding motor function in the hemiplegic hand in the intervention group compared to the control group based on the outcome measures used. Conclusion: The results indicated that BCI-Exo can promote recovery and neuroplasticity after stroke regardless of its phase. However, the technology is still in its early stages and more studies need to be performed to better specify and understand the advantages and disadvantages compared to conventional treatment methods.