Exploring the relationship between listening development and strategy use

This article reports on an investigation into the development of the listening proficiency and strategic behaviour of 15 lower-intermediate learners of French in England. We consider whether listeners remain in the same listening proficiency group after six months, and whether changes in strategy use are related to movement or non-movement between listening proficiency groups. We also examine whether learners’ strategic behaviour reflects their teachers’ approaches to listening. Data were gathered at two time points from a recall protocol which learners completed after listening to short passages and from verbal reports made by learners while they completed a multiple choice listening task. Teacher interviews provided information on how listening had been presented in learners’ classrooms. We detected little movement by students across the listening proficiency groups between the two time points. In spite of some changes in frequency of strategy use, we also observed stability in manner of use by some learners. Differences in strategy use were more evident between groups (non-movers, improvers and decliners) than between uses from Time 1 to Time 2. We conclude by discussing the pedagogical implications of these findings

EEG neurofeedback research: A fertile ground for psychiatry?

International audienceThe clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human–computer interaction that enables subjects to train voluntarily and modify functional biomarkers that are related to a defined mental disorder. We investigate three kinds of factors related to this definition of neurofeedback. We focus this article on EEG NFB. The first part of the paper investigates neurophysiological factors underlying the brain mechanisms driving NFB training and learning to modify a functional biomarker voluntarily. Two kinds of neuroplasticity involved in neurofeedback are analyzed Hebbian neuroplasticity, i.e. long-term modification of neural membrane excitability and/or synaptic potentiation, and homeostatic neuroplasticity, i.e. homeostasis attempts to stabilize network activity. The second part investigates psychophysiological factors related to the targeted biomarker. It is demonstrated that neurofeedback involves clearly defining which kind of relationship between EEG biomarkers and clinical dimensions (symptoms or cognitive processes) is to be targeted. A nomenclature of accurate EEG biomarkers is proposed in the form of a short EEG encyclopedia (EEGcopia). The third part investigates human–computer interaction factors for optimizing NFB training and learning during the closed loop interaction. A model is proposed to summarize the different features that should be controlled to optimize learning. The need for accurate and reliable metrics of training and learning in line with human–computer interaction is also emphasized, including targeted biomarkers and neuroplasticity. All these factors related to neurofeedback show that it can be considered as a fertile ground for innovative research in psychiatry

Defining and Quantifying Users' Mental Imagery-based BCI skills: a first step

International audienceObjective: While promising for many applications, Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are still scarcely used outside laboratories , due to a poor reliability. It is thus necessary to study and fix this reliability issue. Doing so requires the use of appropriate reliability metrics to quantify both the classification algorithm and the BCI user's performances. So far, Classification Accuracy (CA) is the typical metric used for both aspects. However, we argue in this paper that CA is a poor metric to study BCI users' skills. Here, we propose a definition and new metrics to quantify such BCI skills for Mental Imagery (MI) BCIs, independently of any classification algorithm. Approach: We first show in this paper that CA is notably unspecific, discrete, training data and classifier dependent, and as such may not always reflect successful self-modulation of EEG patterns by the user. We then propose a definition of MI-BCI skills that reflects how well the user can self-modulate EEG patterns, and thus how well he could control an MI-BCI. Finally, we propose new performance metrics, classDis, restDist and classStab that specifically measure how distinct and stable the EEG patterns produced by the user are, independently of any classifier. Main results: By re-analyzing EEG data sets with such new metrics, we indeed confirmed that CA may hide some increase in MI-BCI skills or hide the user inability to self-modulate a given EEG pattern. On the other hand, our new met-rics could reveal such skill improvements as well as identify when a mental task performed by a user was no different than rest EEG. Significance: Our results showed that when studying MI-BCI users' skills, CA should be used with care, and complemented with metrics such as the new ones proposed. Our results also stressed the need to redefine BCI user training by considering the different BCI subskills and their measures. To promote the complementary use of our new metrics, we provide the Matlab code to compute them for free and open-source

A User-Centred Approach to Unlock the Potential of Non-Invasive Bcis: An Unprecedented International Translational Effort

Non-invasive Mental Task-based Brain-Computer Interfaces (MT-BCIs) enable their users to interact with the environment through their brain activity alone (measured using electroencephalography for example), by performing mental tasks such as mental calculation or motor imagery. Current developments in technology hint at a wide range of possible applications, both in the clinical and non-clinical domains. MT-BCIs can be used to control (neuro)prostheses or interact with video games, among many other applications. They can also be used to restore cognitive and motor abilities for stroke rehabilitation, or even improve athletic performance. Nonetheless, the expected transfer of MT-BCIs from the lab to the marketplace will be greatly impeded if all resources are allocated to technological aspects alone. We cannot neglect the Human End-User that sits in the centre of the loop. Indeed, self-regulating one’s brain activity through mental tasks to interact is an acquired skill that requires appropriate training. Yet several studies have shown that current training procedures do not enable MT-BCI users to reach adequate levels of performance. Therefore, one significant challenge for the community is that of improving end-user training. To do so, another fundamental challenge must be taken into account: we need to understand the processes that underlie MT-BCI performance and user learning. It is currently estimated that 10 to 30% of people cannot control an MT-BCI. These people are often referred to as “BCI inefficient”. But the concept of “BCI inefficiency” is debated. Does it really exist? Or, are low performances due to insufficient training, training procedures that are unsuited to these users or is the BCI data processing not sensitive enough? The currently available literature does not allow for a definitive answer to these questions as most published studies either include a limited number of participants (i.e., 10 to 20 participants) and/or training sessions (i.e., 1 or 2). We still have very little insight into what the MT-BCI learning curve looks like, and into which factors (including both user-related and machine-related factors) influence this learning curve. Finding answers will require a large number of experiments, involving a large number of participants taking part in multiple training sessions. It is not feasible for one research lab or even a small consortium to undertake such experiments alone. Therefore, an unprecedented coordinated effort from the research community is necessary. We are convinced that combining forces will allow us to characterise in detail MT-BCI user learning, and thereby provide a mandatory step toward transferring BCIs “out of the lab”. This is why we gathered an international, interdisciplinary consortium of BCI researchers from more than 20 different labs across Europe and Japan, including pioneers in the field. This collaboration will enable us to collect considerable amounts of data (at least 100 participants for 20 training sessions each) and establish a large open database. Based on this precious resource, we could then lead sound analyses to answer the previously mentioned questions. Using this data, our consortium could offer solutions on how to improve MT-BCI training procedures using innovative approaches (e.g., personalisation using intelligent tutoring systems) and technologies (e.g., virtual reality). The CHIST-ERA programme represents a unique opportunity to conduct this ambitious project, which will foster innovation in our field and strengthen our community