Are we ready? Issues in transferring BCI technology from experts to users

Brain-Computer Interfaces (BCIs) are no longer only used by healthy subjects under control conditions in laboratory environments, but by patients controlling applications at their homes, without the BCI experts around. But are the technology and the field mature enough for this? In this work, we want to summarize the experiences gained and the lessons we learned while transferring BCI technologies from the lab to the user's home. These lessons range from pure BCI issues (technical and handling), to common communication problems between different people involved, and lessons encountered while controlling the applications. The points raised are very general and will be faced similarly by other groups, if they move on to bring the BCI technology to the end-user.

Transferring brain-computer interfaces beyond the laboratory: Successful application control for motor-disabled users

Andrea Biasiucci, Tom Edward Carlson, Marco Creatura, Robert Leeb, Alberto Molina Arribere, Serafeim Perdikis, Michele Tavella, Luca Tonin

OBJECTIVES: Brain-computer interfaces (BCIs) are no longer only used by healthy participants under controlled conditions in laboratory environments, but also by patients and end-users, controlling applications in their homes or clinics, without the BCI experts around. But are the technology and the field mature enough for this? Especially the successful operation of applications –like text entry systems or assistive mobility devices such as tele-presence robots– requires a good level of BCI control. How much training is needed to achieve such a level? Is it possible to train naïve end-users in 10 days to successfully control such applications? METHODS AND MATERIALS: In this work, we report our experiences of training 24 motor-disabled participants at rehabilitation clinics or at the end-users’ homes, without BCI experts present. We also share the lessons that we have learned through transferring BCI technologies from the lab to the user's home or clinics. RESULTS: The most important outcome is that fifty percent of the participants achieved good BCI performance and could successfully control the applications (tele-presence robot and text-entry system). In the case of the tele-presence robot the participants achieved an average performance ratio of 0.87 (max. 0.97) and for the text entry application a mean of 0.93 (max. 1.0). The lessons learned and the gathered user feedback range from pure BCI problems (technical and handling), to common communication issues among the different people involved, and issues encountered while controlling the applications. CONCLUSION: The points raised in this paper are very widely applicable and we anticipate that they might be faced similarly by other groups, if they move on to bringing the BCI technology to the end-user, to home environments and towards application prototype control.

Elsevier2013

Moving Brain-Controlled Devices Outside the Lab: Principles and Applications

Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil, Robert Leeb, Serafeim Perdikis

This chapter provides an overview of the functionality and the underlying principles of the brain-computer interfaces (BCI) developed by the Chair in Non-Invasive Brain-Machine Interface (CNBI) of the Swiss Federal Institute of Technology (EPFL), as well as exemplary applications where those have been successfully evaluated. Our laboratory mainly develops \emph{non-invasive} BCI systems based on electroencephalographic (EEG) signals and, thus, devoid of medical hazards, real-time, portable, relatively low-cost and minimally obtrusive. Our research is pushing forward \emph{asynchronous} paradigms offering a spontaneous, user-driven and largely ecological interaction. Furthermore, we stand on the \emph{machine-learning} way to BCI with emphasis on personalization, configurability and adaptability, coupled with \emph{mutual learning} training protocols, so that elaborate signal processing and pattern recognition methods are optimally combined with the user's learnable modulation of brain signals towards high and robust performances and universal usability. Additionally, \emph{cognitive mental state} monitoring is employed to shape or refine the interaction. \emph{Shared-control} approaches allow smart, context-aware robotics to complement the BCI channel for more fine-grained control and reduction of the user's mental workload. Last but not least, \emph{hybrid BCI} designs exploit additional physiological signals to augment the BCI modality and enrich the control paradigm, thus also exploiting potential residual capabilities of disabled end-users. The applicability and effectiveness of the aforementioned principles is hereby demonstrated in four exemplary applications evaluated with both able-bodied and motor-disabled end-users. These applications include a hybrid, motor imagery (MI)-based speller, a telepresence robot equipped with shared-control, cognitive mental state monitoring paradigms able to recognize and correct errors, and, finally, a car driving application where a passive BCI enabled on a smart car assists towards increased safety and improved driving experience. Remarkably, our results show that the performance of end-users with disabilities was similar to that of a group of healthy users, who were more familiar with the experiment and the environment. This demonstrates that end-users are able to successfully use BCI technology.

Springer Netherlands2015

Cognitive language engineering towards robust human-computer interaction

Vincenzo Pallotta

Intelligent information processing seems to be one of the most challenging task among those involved in human-computer interaction. A central issue is how to model the various types of interaction among artificial and natural entities at different levels of abstraction. On the one hand, models of interaction are required to better understand the communication phenomena. On the other, suitable languages and paradigms should provide powerful frameworks for developing computer-based applications. In this dissertation I focus on different aspects of the second problem, trying to develop a methodology for the design of interactive natural language applications (e.g. from question-answering to mixed-initiative dialogue). One of the main aspects I am concerned with in this work is the problem of their robustness. Several methods have been proposed for achieving robustness in natural language understanding, but these methods are sometimes hard to scale up or re-use in different applications. Moreover, they often concentrate on a single linguistic level of the processing rather than offering a global solution. I will set up a Language Engineering environment whose goal is to combine software engineering and cognitive aspects (e.g. aspects related to representation of a mental model of the speaker). Given its complexity, it is apparent that the problem can be solved only partially. I want to stress here that the main contribution of my work is a holistic perspective on the problem of natural language understanding. Rather than focusing on a particular aspect of natural language processing, I tried to benefit from the big amount of work that has been already done in Computational Linguistics and Computer Science merging different ideas and techniques. In the first part of the dissertation I explore the universe of Language Engineering in order to clarify how my contribution can be situated. After a survey on the state of the art on robust methods in analysis of natural language data, I focus on the role that Computational Logic plays in relating the syntactic and semantic analysis of natural language to its practical understanding within specific applications. Robustness is considered from two complementary perspectives, borrowing the terminology from modern software engineering: robustness "in the small" and robustness "in the large". The first perspective is discussed while presenting an application for the Interaction through Speech with Information Systems, where robust semantic parsing is used to extract queries from spoken natural language utterances.The second perspective is examplified by the re-engineering of an existing text analysis system using a new Language Engineering methodology: Agent-Oriented Language Engineering. In the second part of the thesis I discuss how cognitive aspects can be integrated into a Language Engineering environment leading to the notion of Cognitive Language Enginering. I tackle the difficult problem of robust dialogue management from both a cognitive and computational perspective. I propose two frameworks for the semantic representation and assimilation of information into the dialogue information state. The first framework allows us to represent and reason about the dynamic aspects of objects and events. The second framework is centered on the notion of mental space and it is used to build representations of the cognitive processing of information during communication.

EPFL2002