Franck Tarpin-Bernard

Frameworks and Patterns for Synchronous Groupware: AMf-C Approach

Frameworks and design patterns are emerging technologies in software engineering. They increase software quality in terms of reusability, modularity and extensibility. Synchronous groupware can benefit of these new technologies. This article describes AMF-C, a multiagent model which structures each agent with a various number of facets, and two associated frameworks. Indeed, a cooperative application can use either a fragmented framework (facets are dispatched into the network) or a replicated one (each agent is totally replicated). Design patterns are identified for the definition and the interconnection of facets. In this last case, an expressive graphical formalism is used to wire control components. The design and implementation tasks are largely reduced and mainly rely on a good choice and combination of patterns. Finally, we introduce the associated tools and methodology that holds great promise in addressing the design issues.

Modeling Elementary Cognitive Abilities for Adaptive Hypermedia Presentation

The adaptation of hypermedia can be carried out at three levels, namely the content, navigation and presentation level. The presentation level is the least studied of the three, apparently because it refers to user properties that are not easy to model. In this paper, we present a new approach to modeling cognitive abilities that relies on basic mental functionalities. We describe the Cognitive User Modeling for Adaptive Presentation of Hyper-Documents (CUMAPH) environment, which mainly provides an authoring tool and an adaptation engine. The aim of this environment is to adapt a hyper-document presentation by selecting the elements that best fit the user cognitive profile. Its architecture is based on four main components: a cognitive user model, a hyper-document builder, an adaptation engine and a generic style sheet. To validate our approach, we designed an innovative protocol and conducted an experimental study involving 39 students. The first results show that an adaptive presentation can significantly increase the efficiency of hypermedia presentations.

Task models and interaction models in a multiple user interfaces generation process

In a Multiple User Interfaces (MUI) context, several models must be defined and adapted (tasks, user, domain...). Abstract models are progressively enriched in concrete models using pattern libraries and filtering processes. In this paper, we define the central role of the interaction model in MUI design and specification. This model manages the interaction between the user and the application, and ensures the link between task models, abstract interfaces and the functional core of the application. In our approach, we use AMF, a multi-agent and multi-facet architecture, to define the interaction model. We describe the structure and behavior of an AMF-based interactive system that provides multiple user interfaces.

Evaluation and Comparison of a Multimodal Combination of BCI Paradigms and Eye Tracking With Affordable Consumer-Grade Hardware in a Gaming Context

This paper evaluates the usability and efficiency of three multimodal combinations of brain-computer interface (BCI) and eye tracking in the context of a simple puzzle game involving tile selection and rotations using affordable consumer-grade hardware. It presents preliminary results indicating that the BCI interaction is interesting but very tiring and imprecise, and may be better suited as an optional and complementary modality to other interaction techniques.

Bidirectional feedback in motor imagery BCIs: learn to control a drone within 5 minutes

Brain Computer Interface systems rely on lengthy training phases that can last up to months due to the inherent variability in brainwave activity between users. We propose a BCI architecture based on the co-learning between the user and the system through different feedback strategies. Thus, we achieve an operational BCI within minutes. We apply our system to the piloting of an AR.Drone 2.0 quadricopter. We show that our architecture provides better task performance than traditional BCI paradigms within a shorter time frame. We further demonstrate the enthusiasm of users towards our BCI-based interaction modality and how they find it much more enjoyable than traditional interaction modalities.

CUMAPH: Cognitive User Modeling for Adaptive Presentation of Hyper-documents. An Experimental Study

In this paper we present the CUMAPH environment (Cognitive User Modeling for Adaptive Presentation of Hyper-document). The aim of this environment is to adapt hyper-document presentation to the cognitive user profile. The architecture of this environment is based on four main components: a cognitive user model, a hyper-document generation process, an adaptive process and a generic style sheet. These components and their combinations are described in detail. To validate our approach, an experimental study was conducted upon a population of 50 students. The experimental steps and the main results obtained are discussed in this paper.

Adding Human Learning in Brain--Computer Interfaces (BCIs): Towards a Practical Control Modality

In this article, we introduce CLBCI (Co-Learning for Brain--Computer Interfaces), a BCI architecture based on co-learning in which users can give explicit feedback to the system rather than just receiving feedback. CLBCI is based on minimum distance classification with Independent Component Analysis (ICA) and allows for shorter training times compared to classical BCIs, as well as faster learning in users and a good performance progression. We further propose a new scheme for real-time two-dimensional visualization of classification outcomes using Wachspress coordinate interpolation. It allows us to represent classification outcomes for n classes in simple regular polygons. Our objective is to devise a BCI system that constitutes a practical interaction modality that can be deployed rapidly and used on a regular basis. We apply our system to an event-based control task in the form of a simple shooter game in which we evaluate the learning effect induced by our architecture compared to a classical approach. We also evaluate how much user feedback and our visualization method contribute to the performance of the system.

Towards a general architecture for a co-learning of brain computer interfaces

In this article we propose a software architecture for asynchronous BCIs based on co-learning, where both the system and the user jointly learn by providing feedback to one another. We propose the use of recent filtering techniques such as Riemann Geometry and ICA followed by multiple classifications, by both incremental supervised classifiers and minimally supervised classifiers. The classifier outputs are then combined adaptively according to the feedback using recursive neural networks.

Feasibility of BCI Control in a Realistic Smart Home Environment

Smart homes have been an active area of research, however despite considerable investment, they are not yet a reality for end-users. Moreover, there are still accessibility challenges for the elderly or the disabled, two of the main potential targets for home automation. In this exploratory study we design a control mechanism for smart homes based on Brain Computer Interfaces (BCI) and apply it in the “Domus”1 smart home platform in order to evaluate the potential interest of users about BCIs at home. We enable users to control lighting, a TV set, a coffee machine and the shutters of the smart home. We evaluate the performance (accuracy, interaction time), usability and feasibility (USE questionnaire) on 12 healthy subjects and 2 disabled subjects. We find that healthy subjects achieve 77% task accuracy. However, disabled subjects achieved a better accuracy (81% compared to 77%).

Achieving Usability of Adaptable Software: The AMF-based Approach

This chapter proposes a novel model-based approach for adapting interactive applications to various contexts while ensuring its usability. After a brief overview of the existing software architecture models for HCI and strategies for adaptation, we detail the models we are proposing. This includes task, concept, platform, and user models as well as an interaction model. All these models are linked via an underlying architecture called AMF. It ensures the relationships between all the other models and encapsulates the key usability attributes. We will also show how these models are embedded in a process and a method for building adaptive software.