3 research outputs found

    Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms

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    Past, Present, and Future of EEG-Based BCI Applications

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    An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed

    The role of somatosensory feedback for brain-machine interfaces applications

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    Brain-machine interfaces (BMI) based on motor imagery (MI) have emerged as a promising approach to enhance motor skills and restore motor functions. However, the efficacy and efficiency of BMI systems remain limited. The current lack of usability can be explained by the fact that significant efforts have been dedicated to improve decoding efficiency and accuracy, but BMI studies have generally ignored the user-training component of BMI operation. It has been suggested that somatosensory feedback would be more suitable than standard visual feedback to train subjects to control a BMI. In this thesis, a novel feedback modality has been explored to improve BMI usability, namely sensory-threshold neuromuscular electrical stimulation (St-NMES). St-NMES delivers transcutaneous electrical stimulation that depolarizes sensory and motor axons without eliciting any muscular contraction. In order to assess the effect of this new feedback modality on BMI skill learning this thesis is composed of four experiments. In a first experiment, the effect of St-NMES on MI performance was investigated. Twelve healthy subjects participated in a cross-over design experiment comparing St-NMES with visual feedback. Offline analyses showed that St-NMES not only enhanced MI brain patterns, but also improved classification accuracy. Importantly, St-NMES alone did not induce detectable artefacts. In a second experiment, physiological impact of online BMI training on corticospinal tract (CST) plasticity was studied according to the feedback modality âeither St-NMES or visual feedback. Ten healthy participants were enrolled in a cross-over design experiment testing both BMI systems. Results showed that BMI based on St-NMES significantly enhanced CST excitability compared to BMI based on visual feedback. Moreover, BMI system based on St-NMES was significantly more robust and accurate over days. A third experiment further explored the parallelism between BMI learning based on St-NMES feedback and natural motor learning, putting particular attention on the underlying physiology of the process. Apart from analyzing the evolution of BMI performance, we also examined changes in CST excitability and modulation of intracortical inhibition in the early learning phase (after one BMI session) as well as later learning stage (after 2 weeks training). Ten healthy participants were trained to control a BMI based on St-NMES feedback. Results showed that subjects improved their BMI control with practice, what might be explained by the adaptation of the central nervous system over time. Finally, the last experiment explored the feasibility of BMI-St-NMES for upper limb rehabilitation after stroke. A chronic stroke patient with a severe motor disability was trained with BMI-St-NMES over 3 weeks. After training, upper-limb motor function improved, reaching clinical relevance. Based on our previous observations, we believe that BMI-St-NMES training enhanced CST projections leading to motor recovery. As a conclusion, this thesis showcases that a contingent activation of central nervous system with somatosensory stimulation through BMI-St-NMES is a promising solution to enhance BMI control and to induce cortico and corticospinal changes. This new BMI modality could become a future opportunity for several fields of research including mental training assistive scenarios as well as motor rehabilitation of patients with lesions within central nervous system
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