Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to...
01a Articolo in rivista
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The use and creation of machine‐learning‐based solutions to solve problems or reduce their computational costs are becoming increasingly widespread in many domains. Deep Learning plays a large part in this growth. However, it has drawbacks such as a lack of explainability and behaving as a black‐...
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Graph neural networks have proved to be a key tool for dealing with many problems and domains such as chemistry, natural language processing and social networks. While the structure of the layers is simple, it is difficult to identify the patterns learned by the graph neural network. Several works...
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Deep neural networks are the driving force of the recent explosion of machine learning applications in everyday life. However, they usually require a lot of training data to work well, and they act as black-boxes, making predictions without any explanation about them. This paper presents Memory...
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Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose-insulin dynamics based on the sensor data collected by monitoring several aspects of the physiological condition and...
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Digital transformation is currently an essential condition for companies to operate in most markets, since it provides a whole new set of competitive skills and strategic tools. On the other hand, the same digitalization puts companies in the face of a whole new series of threats coming from the...
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When dealing with complex functional brain networks, group analysis still represents an open issue. In this paper, we investigated the potential of an innovative approach based on PARAllel FActorization (PARAFAC) for the extraction of the grand average connectivity matrices from both simulated and...
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Background: Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke...
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Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to...
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This paper investigates the transformation of Hamiltonian structures under sampling. It is shown that the exact sampled-data equivalent model associated to a given port-Hamiltonian continuous-time dynamics exhibits a discrete-time representation in terms of the discrete gradient, with the same...