A brain-computer interface (BCI) is a technology that enables direct communication between a user and external devices through brain activity. BCIs can be particularly beneficial for individuals with severe motor impairments who are unable to use conventional assistive technologies that require muscular control. The present study evaluates a preliminary ERP-based BCI designed as an augmentative and alternative communication (AAC) system for this population. Ten ablebodied participants were asked to operate the AAC-BCI system, which enabled stepwise selections through pictogram-based hierarchical menus to address various communication needs. Both objective performance metrics and subjective user feedback were collected. The results validate the feasibility of the proposed system as an AAC-BCI solution. However, further improvements are necessary to optimize its usability and effectiveness for the target population. Future research should focus on refining the system’s performance and adaptability to enhance its practical application in real-world scenarios.

As a part of a research project concerning software maintainability assessment in collaboration with the development team, we wanted to explore dissensions between developers and the confounding effect of size. To this end, this study replicated and extended a recent study from Schnappinger et al. with the public part of its dataset and the metrics extracted from the graph-based tool Javanalyser. The entire processing pipeline was automated, from metrics extraction to the training of machine learning models. The study was extended by predicting the continuous maintainability to take account of dissensions. Then, all experimental shots were duplicated to evaluate the overall influence of the class size. In the end, the original study was successfully replicated. Moreover, good performance was achieved on the continuous maintainability prediction. Finally, the class size was not sufficient for fine-grained maintainability prediction. This study shows the necessity to explore the nature of what is measured by code metrics, and is also the first step in the construction of a maintainability model.

As a part of a research project concerning software maintainability assessment in collaboration with the development team, we were interested in the frequent use of metrics as predictors. Many metrics exist, often with opaque and arguable implementations. We claim metrics mix the assessment of presentation, structure and model. In order to focus on true detectable maintainability defects, we computed metrics solely based on the structure of the program. Our approach was to parse the source code of Java programs as a graph, and to compute metrics in a declarative query language. To this end, we developed Javanalyser and implemented 34 metrics using Spoon to parse Java programs and Neo4j as graph database. We will show that the program graph constitutes a steady basis to compute metrics and conduct future machine-learning studies to assess maintainability.

International audience

International audience

By sending a vibration signal on a touch panel equipped with piezoelectric actuators, this study has a double purpose: analyzing the influence of technical characteristics of vibration signals on perception thresholds and analyzing the influence of sex on those thresholds. During the experiment, 46 participants were asked to leave their finger pressed on a touch panel and to inform the experimenter when perceiving a vibration. This work allowed identifying the minimum perceptual thresholds of haptic signals for 95% of a representative population, on a given vibration range. This study also particularly revealed perception differences depending on waveform. Finally, it provides significant results regarding the effect of sex on perceptual sensations: Female participants tend to get lower perceptual thresholds.