Leaving the lab: a portable and quickly tunable BCI

Although many systems for palliative communication based on non-invasive BCIs have been developped during the last few years, very few projects aim at leaving the research labs and hospitals for helping patients at home. Jon Wolpaw's team at the Wadworth Center1 has developped a portable BCI that has now been used for more than one year on a daily basis by 5 people suffering from ALS. This experiment shows that highly handicaped people greatly benefit from such BCIs that they tend to use during long periods -- between 5 an 8 hours a day -- for communicating with their loved ones, for surfing the web, or reading and writing emails. We also aim at being able to leave the lab with this now mature technology for screening handicaped people at home. This would allow checking easily if a patient can use efficiently a BCI without requiring him to come to the hospital or to a specialized laboratory. From the hardware point of view, this home-screening requires a BCI setup that can be used in any situation: portable, fully autonomous and battery powered. From the software point of view, the machine learning techniques that adapt the BCI to the individual must provide a "good" result within a few seconds rather than an "optimal" result after several minutes or hours of processing

Interfaces cerveau-ordinateur et rééducation fonctionnelle: étude de cas chez un patient hémiparésique

National audienceLes interfaces cerveau-ordinateur (BCIs: Brain-Computer Interfaces) utilisent l'activité cérébrale d'un individu pour dialoguer avec un ordinateur. L'aide à la communication (outil d'épellation, interface domotique) et la récupération du mouvement (contrôle d'une prothèse ou d'un robot) sont les applications les plus fréquentes des BCIs dans le domaine de l'assistance. L'étude de cas présentée dans cet article montre que les BCIs peuvent également être utilisées dans une approche thérapeutique par neurofeedback pour la rééducation ou la récupération fonctionnelle. Nous décrivons, dans un premier temps, les utilisations thérapeutiques connues des interfaces cerveau-ordinateur. Puis nous présentons une expérience clinique durant laquelle une BCI a été utilisée comme outil d'aide à la rééducation motrice par un patient atteint d'une hémiparésie du côté droit

Monitoring mental workload by EEG during a game in Virtual Reality

During an activity, knowing the mental workload (MWL) of the user allows to improve the Human-Machine Interactions (HMI). Indeed, the MWL has an impact on the individual and its interaction with the environment. Monitoring it is therefore a crucial issue. In this context, we have created the virtual game Back to Pizza which is based on the N-back task (commonly used for measuring MWL). In this more playful variant, users must carry out orders from customers of a pizza food truck. It is an interactive game that involves the audience of the IHM'23 conference, choosing several parameters like the number of ingredients. During this experience, the objective is to measure MWL in real time through an ElectroEncephaloGraph (EEG) and visual feedback on MWL level is given to the audience. With this demonstration, we propose to present a concept of a virtual interactive game that measures MWL in real time.Comment: IHM'23 - 34e Conf{\'e}rence Internationale Francophone sur l'Interaction Humain-Machine, AFIHM; Universit{\'e} de Technologie de Troyes, Apr 2023, Troyes, Franc

Effect of Task Ludification on Subjective Responses to Mental Workload During Digitalized Cognitive Task.

Cognitive tasks usually used in rehabilitation or evaluation of cognitive functions are viewed as effortful or frustrating, which often leads to participant disengagement and high mental workload (MWL). The challenge is to increase the task’s commitment while decreasing MWL and maintaining similar impact on cognitive functions. Introducing playfulness could decrease the part of MWL due to endogenous factors (like demotivation) without affecting the task’s cognitive aspect. Nevertheless, there is no study investigating the link between ludification, commitment, and MWL and the performance’s repercussions. Therefore, we developed a protocol to compare the subjective MWL for three conditions of a cognitive task that differed depending on the degree of ludification. Overall, our results showed that performances were not influenced by ludification, confirming that it did not reduce the effects on cognitive functions solicited during the task. Regarding the impact on engagement and MWL, it will be discussed in light of different models of MWL

Subjective mental workload modeling in tests involving different cognitive functions

Over time, mental workload (MWL) has become a major topic of scientific discussions. Nevertheless, there is no reference model involving standardized cognitive tests with distinct levels of difficulty and allowing to consider the correlation between task performance and MWL based on these levels. Therefore, we have developed an innovative protocol to model the variation of self-reported MWL (via the NASA-TLX and Workload Profile questionnaires) for tests requiring different cognitive functions (memory, mental inhibition, mental flexibility, and divided attention). Each of these tasks is an adaptation of existing cognitive tests, for which three equivalent difficulty levels were established in terms of impact on performance.The results showed that our difficulty levels allowed distinct classes of MWL and that it co-varied negatively with the performance across difficulty. Our protocol has thus provided new benchmarks that can be useful in many domains where it’s essential to be able to infer MWL from test performance

Exploring Large Virtual Environments by Thoughts using a Brain-Computer Interface based on Motor Imagery and High-Level Commands

International audienceBrain-computer interfaces (BCI) are interaction devices which enable users to send commands to a computer by using brain activity only. In this paper, we propose a new interaction technique to enable users to perform complex interaction tasks and to navigate within large virtual environments (VE) by using only a BCI based on imagined movements (motor imagery). This technique enables the user to send high-level mental commands, leaving the application in charge of most of the complex and tedious details of the interaction task. More precisely, it is based on points of interest and enables subjects to send only a few commands to the application in order to navigate from one point of interest to the other. Interestingly enough, the points of interest for a given VE can be generated automatically thanks to the processing of this VE geometry. As the navigation between two points of interest is also automatic, the proposed technique can be used to navigate efficiently by thoughts within any VE. The input of this interaction technique is a newly designed self-paced BCI which enables the user to send 3 different commands based on motor imagery. This BCI is based on a fuzzy inference system with reject options. In order to evaluate the efficiency of the proposed interaction technique, we compared it with the state-of-the-art method during a task of virtual museum exploration. The state-of-the-art method uses low-level commands, which means that each mental state of the user is associated to a simple command such as turning left or moving forwards in the VE. In contrast, our method based on high-level commands enables the user to simply select its destination, leaving the application performing the necessary movements to reach this destination. Our results showed that with our interaction technique, users can navigate within a virtual museum almost twice as fast as with low-level commands, and with nearly twice less commands, meaning with less stress and more comfort for the user. This suggests that our technique enables to use efficiently the limited capacity of current motor imagery-based BCI in order to perform complex interaction tasks in VE, opening the way to promising new applications

Inter-tasks transferability of a subjective cognitive load classification model

Studying cognitive load (CL) presents several challenges, including the need for accurate and generic CL classification models. Previous studies have solely relied on physiological data to measure CL, as in Appel et al. (2021). However, what about subjective CL measure?Thus, we proposed a three-class classification model of subjective CL through five cognitive tasks: N-back, Corsi, Go/No-Go, WCST, and Dual task (Louis et al. 2023). For this conference, we examined whether a model trained on a Task A could accurately predict the subjective CL classes of a Task B.Firstly, the results showed that Corsi was the most effective task for classifying subjective CL based only on performance and complexity levels, achieving 80% accuracy. Moreover, a classification model trained on N-back, WCST, and Dual task could predict the subjective CL classes of Corsi with over 70% accuracy. This perspective would save time in setting up and training a classification model

Cognitive tasks and combined statistical methods to evaluate, model, and predict mental workload

Mental workload (MWL) is a concept that is used as a reference for assessing the mental cost of activities. In recent times, challenges related to user experience are determining the expected MWL value for a given activity and real-time adaptation of task complexity level to achieve or maintain desired MWL. As a consequence, it is important to have at least one task that can reliably predict the MWL level associated with a given complexity level. In this study, we used several cognitive tasks to meet this need, including the N-Back task, the commonly used reference test in the MWL literature, and the Corsi test. Tasks were adapted to generate different MWL classes measured via NASA-TLX and Workload Profile questionnaires. Our first objective was to identify which tasks had the most distinct MWL classes based on combined statistical methods. Our results indicated that the Corsi test satisfied our first objective, obtaining three distinct MWL classes associated with three complexity levels offering therefore a reliable model (about 80% accuracy) to predicted MWL classes. Our second objective was to achieve or maintain the desired MWL, which entailed the use of an algorithm to adapt the MWL class based on an accurate prediction model. This model needed to be based on an objective and real-time indicator of MWL. For this purpose, we identified different performance criteria for each task. The classification models obtained indicated that only the Corsi test would be a good candidate for this aim (more than 50% accuracy compared to a chance level of 33%) but performances were not sufficient to consider identifying and adapting the MWL class online with sufficient accuracy during a task. Thus, performance indicators require to be complemented by other types of measures like physiological ones. Our study also highlights the limitations of the N-back task in favor of the Corsi test which turned out to be the best candidate to model and predict the MWL among several cognitive tasks