A generic framework for adaptive EEG-based BCI training and operation

International audienceThere are numerous possibilities and motivations for an adaptive BCI, which may not be easy to clarify and organize for a newcomer to the field. To our knowledge, there has not been any work done in classifying the literature on adaptive BCI in a comprehensive and structured way. We propose a conceptual framework, a taxonomy of adaptive BCI methods which encompasses most important approaches to fit them in such a way that a reader can clearly visualize which elements are being adapted and for what reason. In the interest of having a clear review of existing adaptive BCIs, this framework considers adaptation approaches for both the user and the machine, i.e., using instructional design observations as well as the usual machine learning techniques. This framework not only provides a coherent review of such extensive literature but also enables the reader to perceive gaps and flaws in the current BCI systems, which would hopefully bring novel solutions for an overall improvement

A framework for user training adaptation in Brain-Computer Interfaces based on mental tasks (MT-BCIs)

International audienceMental Task (MT-)based BCIs allow for spontaneous and asynchronous interactions with external devices solely through mental tasks such as motor imagery or mental math. Such BCIs require their users to develop the ability to encode mental commands that are as stable, clear and distinct as possible-making them easy to recognize by a computer. Despite their promises and achievements, traditional closed-loop training programs are suboptimal [1] and could be further improved. Some aspects of training programs were studied in depth in light of methods from the fields of educational sciences, ergonomics, or user-centered design [1, 2]. However, the best way to train users is still unknown and some aspects of user training protocols possibly impacting skill acquisition may not have been sufficiently explored yet. Although successful additions of a human perspective in the traditional BCI interaction model were already possible (e.g. [3, 4, 5]), these representations might not sufficiently depict the many aspects that could be improved/adapted in BCI human training protocols. Therefore, we propose a framework identifying and defining the various parameters composing a BCI user training program. Method, Results: Based on the existing literature [6], we propose a framework describing, at different time scales, the different aspects of BCI user training. As seen in Fig. 1, training is composed of one or more sessions (days) whose order, number or duration can vary. Sessions are themselves composed of runs that can vary as well, etc. In this framework, a training program consists of practicing exercises, which refer to what MT-BCI users are expected to do and how to practice it. Although traditional training usually requires users to practice the same exercise over and over, exercises can vary in many ways across experiments and they can also be adapted within trials, runs or sessions. This representation emphasizes the multiple entry points that allow for training adaptation, for example what skill users should practice (e.g. training for speed or accuracy, etc.), in which spontaneity mode (e.g. cue-based vs. self-decided, synchronous trials vs. self-paced exercise), with which instructions or feedback (e.g. content, modality, timing), or in which environment (i.e. the context in which training takes place). Discussion: Not only the properties of training aspects should be questioned, but also their presence. For example, there is no indication that the uniform presence of feedback at each step throughout the entire training is the best way to train users. Besides, rather than universally refining training parameters, it may be preferable to adapt the choice of parameters to the user before and/or throughout sessions [5] based e.g. on changes in users' understanding, perceptions, motivation, fatigue, performances, etc. Significance: Future work should investigate further whether the variation of different training aspects has an influence on behavioral BCI performance, user-related metrics [4] or users' understanding of instructions, self-instructed cognitive strategy, perception of trial-specific quality, willingness to change/redo tasks, etc. This is a preliminary step on the way to designing new training programs composed of exercise sequences adapted to human learning and/or adaptive according to users' experience or performances. Figure 1. Representation of MT-BCI training in decreasing order of time scales. Different aspects of the training feedback, instructions or exercises can be modulated-including their goal, modality, content, duration, variety, frequency, number and/or order

Reviewing open challenges in Mental Tasks (MT-)based BCI user training

International audienc

Competing at the Cybathlon championship for people with disabilities: Long-term motor imagery brain-computer interface training of a cybathlete who has tetraplegia

BACKGROUND: The brain–computer interface (BCI) race at the Cybathlon championship, for people with disabilities, challenges teams (BCI researchers, developers and pilots with spinal cord injury) to control an avatar on a virtual racetrack without movement. Here we describe the training regime and results of the Ulster University BCI Team pilot who has tetraplegia and was trained to use an electroencephalography (EEG)-based BCI intermittently over 10 years, to compete in three Cybathlon events. METHODS: A multi-class, multiple binary classifier framework was used to decode three kinesthetically imagined movements (motor imagery of left arm, right arm, and feet), and relaxed state. Three game paradigms were used for training i.e., NeuroSensi, Triad, and Cybathlon Race: BrainDriver. An evaluation of the pilot’s performance is presented for two Cybathlon competition training periods—spanning 20 sessions over 5 weeks prior to the 2019 competition, and 25 sessions over 5 weeks in the run up to the 2020 competition. RESULTS: Having participated in BCI training in 2009 and competed in Cybathlon 2016, the experienced pilot achieved high two-class accuracy on all class pairs when training began in 2019 (decoding accuracy > 90%, resulting in efficient NeuroSensi and Triad game control). The BrainDriver performance (i.e., Cybathlon race completion time) improved significantly during the training period, leading up to the competition day, ranging from 274–156 s (255 ± 24 s to 191 ± 14 s mean ± std), over 17 days (10 sessions) in 2019, and from 230–168 s (214 ± 14 s to 181 ± 4 s), over 18 days (13 sessions) in 2020. However, on both competition occasions, towards the race date, the performance deteriorated significantly. CONCLUSIONS: The training regime and framework applied were highly effective in achieving competitive race completion times. The BCI framework did not cope with significant deviation in electroencephalography (EEG) observed in the sessions occurring shortly before and during the race day. Changes in cognitive state as a result of stress, arousal level, and fatigue, associated with the competition challenge and performance pressure, were likely contributing factors to the non-stationary effects that resulted in the BCI and pilot achieving suboptimal performance on race day. Trial registration not registered SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12984-022-01073-9

A BCI challenge for the signal processing community: considering the user in the loop

Chapter 8International audienc

Standardization of Protocol Design for User Training in EEG-based Brain-Computer Interface

International audienceBrain-computer interfaces (BCIs) are systems that enable a personto interact with a machine using only neural activity. Such interaction canbe non-intuitive for the user hence training methods are developed to increaseone’s understanding, confidence and motivation, which would in parallel increasesystem performance. To clearly address the current issues in the BCI usertraining protocol design, here it is divided intointroductoryperiod and BCIinteractionperiod. First, theintroductoryperiod (before BCI interaction) mustbe considered as equally important as the BCI interaction for user training. Tosupport this claim, a review of papers show that BCI performance can dependon the methodologies presented in such introductory period. To standardize itsdesign, the literature from human-computer interaction (HCI) is adjusted to theBCI context. Second, during the user-BCI interaction, the interface can takea large spectrum of forms (2D, 3D, size, color etc.) and modalities (visual,auditory or haptic etc.) without following any design standard or guidelines.Namely, studies that explore perceptual affordance on neural activity show thatmotor neurons can be triggered from a simple observation of certain objects, anddepending on objects’ properties (size, location etc.) neural reactions can varygreatly. Surprisingly, the effects of perceptual affordance were not investigatedin the BCI context. Both inconsistent introductions to BCI as well as variableinterface designs make it difficult to reproduce experiments, predict their outcomesand compare results between them. To address these issues, a protocol designstandardization for user training is proposed

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed

A generic framework for adaptive EEG-based BCI training and operation

International audienceThere are numerous possibilities and motivations for an adaptive BCI, which may not be easy to clarify and organize for a newcomer to the field. To our knowledge, there has not been any work done in classifying the literature on adaptive BCI in a comprehensive and structured way. We propose a conceptual framework, a taxonomy of adaptive BCI methods which encompasses most important approaches to fit them in such a way that a reader can clearly visualize which elements are being adapted and for what reason. In the interest of having a clear review of existing adaptive BCIs, this framework considers adaptation approaches for both the user and the machine, i.e., using instructional design observations as well as the usual machine learning techniques. This framework not only provides a coherent review of such extensive literature but also enables the reader to perceive gaps and flaws in the current BCI systems, which would hopefully bring novel solutions for an overall improvement