Silvia Marchesotti

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Despite technical advances in brain machine interfaces (BMI), for as-yet unknown reasons the ability to control a BMI remains limited to a subset of users. We investigate whether individual differences in BMI control based on motor imagery (MI) are related to differences in MI ability. We assessed whether differences in kinesthetic and visual MI, in the(More)
BACKGROUND Both a large lesion volume and abnormalities in diffusion tensor imaging are independently associated with a poor prognosis after cerebral infarctions. Therefore, we assume that they are associated. This study assessed the associations between lesion volumes and diffusion tensor imaging in patients with a right-sided cerebral infarction. (More)
Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Markovian decision processes, where the next state depends on only the current state and action. Little(More)
Background Both a large lesion volume and abnormalities in diffusion tensor imaging are individually independently associated with a poor prognosis in patients with attention deficit after cerebral infarctions. Therefore, we assume that they are associated. This study assessed the correlations associations between large lesion volumes and diffusion tensor(More)
Humans can learn under a wide variety of feedback conditions. Particularly important types of learning fall under the category of reinforcement learning (RL) where a series of decisions must be made and a sparse feedback signal is obtained. Computational and behavioral studies of RL have focused mainly on Markovian decision processes (MDPs), where the next(More)
1) My main concern is related to how the authors deal with the large number of statistical tests performed. The lack of corrections for multiple comparisons seems remiss and undermines the interpretations and validity of the results. For example, Table 2 shows the results from 52 correlation analyses, but the authors still regard an uncorrected p < .05 as(More)
Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex,(More)
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