BCI-Based User-Centered Design for Emotionally-Driven User Experience

In order to develop a complex interactive system, user-centered evaluation (UCE) is an essential component. The new interaction paradigms encourage exploring new variables for accounting the users’ experience in terms of their needs and preferences. This is especially important for Adaptable Virtual Environments (AVE). In this context, to obtain a more engaging overall user’s experience, a good designer should perform proper formative and summative usability tests based on the user’s emotional level, which become a UCE activity. Our methodology tries to overcome the weaknesses of traditional methods by employing a Brain Computer Interface (BCI) to collect additional information on user’s needs and preferences. A set of preliminary usability experiments has been conducted for (i) determining if the outcome of a BCI is suitable to drive the designer in organizing the user-system dialog within AVE and (ii) evaluating the user-system dialog, in terms of dynamic increase of the emotionally-driven interaction’s customization.</jats:p

A rough BCI-based Assessment of User&apos;s Emotions for Interface Adaptation: Application to a 3D-Virtual- Environment Exploration Task

Abstract. In order to develop an Adaptive Virtual Environment (AVE), users ’ experience, their needs and preferences should be accounted. In the methodology we presented in this paper, to build a 3D Adaptive Virtual environment (AVE), we overcome the weaknesses of user-centered evaluation (UCE) traditional methods by employing a Brain Computer Interface (BCI): The proposed methodology aims at (I) supporting the design of an engaging overall experience for potential users, (II) enhancing the user experience by dynamically adapting the interaction to the user emotional state so that a more immersive interaction could result.

Improving the Genetic Algorithms by means of a Cooperative Model

In this work the authors propose a method to integrate the optimization algorithm introduced by M. Dorigo, V. Maniezzo and A. Colorni and known as the Ant System (AS), with Genetic Algorithms (GA), exploiting the cooperative effect of the former and the evolutionary effect of GA. A Genetic Algorithm aims to solve problems of combinatorial optimization by means of a population of individuals that work in parallel without a supervisor in an evolutive manner. An Ant System could be used to speed up the search process, of the GA, in the solutions space. In this work, the approach has been applied to the Traveling Salesman Problem; results and comparisons with the GA are presented. 1. Introduction During recent years new algorithms have been introduced to solve problems of combinatorial optimization. Some examples are genetic algorithms [6], immune networks [2], ant systems [3] and neural networks [7]; these algorithms have been called natural algorithms in [4] since they are inspired by ..

Shaping Personal Information Spaces from Collaborative Tagging Systems

Abstract. The appearance of powerful tools for lightweight metadata creation, such as collaborative tagging systems, is harnessing the power of online communities, although such metadata are limited to human consumption only. In this paper we first propose an abstract model for representing a generic collaborative tagging system which uses RDF as the underlying technology to store metadata created by different online communities. Then, we present a scenario with the purpose of illustrating how a service able to retrieve tags from different folksonomies can support users in the organization of their personal information spaces within the context of a digital library