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Brain-machine interfaces (BMI) allow to decode motor commands from paralyzed patients' brains and use those commands with a rehabilitative or assistive purpose. However, brain non-stationarities can affect BMI performance over time in multi-session interventions. The amount and type of data used for calibration may play an important role on the posterior decoding performance. This paper studies six...
Feedback stimuli are fundamental components in Brain-Computer Interfaces. It is known that the presentation of feedback stimuli elicits certain brain potentials that can be measured and classified. As stimuli can be given through different sensory modalities, it is important to understand the effects of different types of feedback on brain responses and their impact on classification. This paper presents...
Neurofeedback therapies are an emerging technique used to treat neuropsychological disorders and to enhance cognitive performance. The feedback stimuli presented during the therapy are a key factor, serving as guidance throughout the entire learning process of the brain rhythms. Online decoding of these stimuli could be of great value to measure the compliance and adherence of the subject to the training...
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