2,908 research outputs found

    Metrological performance of a single-channel brain-computer interface based on motor imagery

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    In this paper, the accuracy in classifying Motor Imagery (MI) tasks for a Brain-Computer Interface (BCI) is analyzed. Electroencephalographic (EEG) signals were taken into account, notably by employing one channel per time. Four classes were to distinguish, i.e. imagining the movement of left hand, right hand, feet, or tongue. The dataset '2a' of BCI Competition IV (2008) was considered. Brain signals were processed by applying a short-time Fourier transform, a common spatial pattern filter for feature extraction, and a support vector machine for classification. With this work, the aim is to give a contribution to the development of wearable MI-based BCIs by relying on single channel EEG

    Rehabilitative devices for a top-down approach

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    In recent years, neurorehabilitation has moved from a "bottom-up" to a "top down" approach. This change has also involved the technological devices developed for motor and cognitive rehabilitation. It implies that during a task or during therapeutic exercises, new "top-down" approaches are being used to stimulate the brain in a more direct way to elicit plasticity-mediated motor re-learning. This is opposed to "Bottom up" approaches, which act at the physical level and attempt to bring about changes at the level of the central neural system. Areas covered: In the present unsystematic review, we present the most promising innovative technological devices that can effectively support rehabilitation based on a top-down approach, according to the most recent neuroscientific and neurocognitive findings. In particular, we explore if and how the use of new technological devices comprising serious exergames, virtual reality, robots, brain computer interfaces, rhythmic music and biofeedback devices might provide a top-down based approach. Expert commentary: Motor and cognitive systems are strongly harnessed in humans and thus cannot be separated in neurorehabilitation. Recently developed technologies in motor-cognitive rehabilitation might have a greater positive effect than conventional therapies

    Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System

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    The present study introduces a brain–computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a “neurofeedback” group, which performed motor imagery while receiving feedback, and a “control” group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual’s ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain–computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation

    Seven Capital Devices for the Future of Stroke Rehabilitation

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