Francesco Cavrini

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In this paper we propose a framework for combination of classifiers using fuzzy measures and integrals that aims at providing researchers and practitioners with a simple and structured approach to deal with two issues that often arise in many pattern recognition applications: (i) the need for an automatic and user-specific selection of the best performing(More)
We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI) based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to(More)
In this study we evaluated the effect of subject-related variables, i.e. hand dominance, gender and experience in using, on the performances of an EMG-based system for virtual upper limb and prosthesis control. The proposed system consists in a low density EMG sensors arrangement, a purpose-built signal-conditioning electronic circuitry and a software able(More)
An electronic demonstrator was designed and developed to automatically interpret the signalman's arm-and-hand visual signals. It was based on an “extended” sensory glove, which is a glove equipped with sensors to measure fingers/wrist/forearm movements, an electronic circuitry to acquire/condition/feed measured data to a personal computer, SVM(More)
One of the key issues in the development of brain-computer interfaces (BCIs) is the improvement of their current information transfer rate. In order to achieve that objective at least two aspects of BCI design should be considered: classification accuracy and protocol specification. In this paper we show how combination of classifiers using fuzzy measures(More)
Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are(More)
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