Publications by authors named "J Tessadori"

Article Synopsis
  • Multiple sclerosis (MS) is a neurological condition that leads to severe brain damage and changes in brain function, with variations in these effects depending on the MS phase.
  • The study discusses a machine learning system that analyzes resting-state functional connectivity (RS FC) data to differentiate between various MS phenotypes and pinpoint crucial functional connections for identifying disease stages.
  • The framework demonstrated strong classification performance across all MS types and effectively identified significant RS FC changes that aid in accurate phenotype classification.
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Enhancing the embodiment of artificial limbs-the individuals' feeling that a virtual or robotic limb is integrated in their own body scheme-is an impactful strategy for improving prosthetic technology acceptance and human-machine interaction. Most studies so far focused on visuo-tactile strategies to empower the embodiment processes. However, novel approaches could emerge from self-regulation techniques able to change the psychophysiological conditions of an individual.

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Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem.

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This work presents an implementation of Error-related Potential (ErrP) detection to produce progressive adaptation of a motor imagery task classifier. The main contribution is in the evaluation of the effect of vibrotactile feedback on both ErrP and motor imagery detection. Results confirm the potential of self-adaptive techniques to improve motor imagery classification, and support the design of vibratory and in general tactile feedback into Brain-Computer Interfaces to improve both static and adaptive performance.

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Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing.

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