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33 results on '"Ipsale, M."'

8. Attività del Progetto di Ricerca 'Applicazioni di Metodi per Diagnostica Elettromagnetica (AMDE)'

Author

Tamburrino, A, Ferrigno, L, Laracca, M, Ventre, S, Martone, R, Coccorese, V, Cutrupi, V, Dalena, D, Ferraioli, F, Formisano, A, Greco, O, Lupò, G, Masullo, G, Romagnolo, N, Cardelli, E, Faba, A, Massinelli, A, Raschi, E, Pompei, M, Tellini, A, Tellini, Bernardo, Morabito, F, Azzerboni, B, Calcagno, S, Carpentieri, M, Costantino, D, Finocchio, G, Greco, A, Ipsale, M, La Foresta, F, Mammone, N, Poimeni, D, Versaci, M, Trevisan, F, Savini, A, and Specogna, R.

Published
2005

21. Time-Frequency Analysis for Characterizing EMG Signals During fMRI Acquisitions.

Author

Apolloni, Bruno, Marinaro, Maria, Tagliaferri, Roberto, Azzerboni, B., Ipsale, M., Carpentieri, M., and La Foresta, F.

Abstract

Research on human sensorimotor functions has hugely increased after electromyogram (EMG) analysis was replaced by functional magnetic resonance imaging (fMRI), that allows to obtain a direct visualization of the brain areas involved in motor control. Very meaningful results could be obtained if the two analysis could be correlated. Our goal is to acquire the EMG data during an fMRI task. The main problems in doing this are related to the electromagnetic compatibility between the resonance coils (very high magnetic fields) and the EMG electrodes. In this study we developed a system that can characterize the entire EMG signal corrupted by the magnetic fields generated by the magnetic resonance gradients. The entire system consists in a hardware equipment (shielded cables and wires) and a software analysis (effective mean analysis and wavelet analysis). The results show that a motor task was correctly delivered by our post processing analysis of the signal. [ABSTRACT FROM AUTHOR]

Published
2005
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22. A Comparison of ICA Algorithms in Biomedical Signal Processing.

Author

Apolloni, Bruno, Marinaro, Maria, Tagliaferri, Roberto, Azzerboni, B., Ipsale, M., La Foresta, F., Mammone, N., and Morabito, F. C.

Abstract

In the last years Independent Component Analysis (ICA) has been applied with success in signal processing and many algorithms have been developed in order to perform ICA. In this paper we review some algorithms, like INFOMAX (Bell and Sejnowski 1995), extended-INFOMAX (Lee, Girolami and Sejniowski 1997), FastICA (OjA, and Hyvärinen 1999), that solve the ICA problem under the assumption of the linear mixture model. We also show an overview of the nonlinear ICA algorithms and we discuss the MISEP (Almeida 2003). In order to test the performances of the reviewed algorithms, we present some applications of ICA in biomedical signal processing. In particular the application of ICA to the electroencephalographic (EEG) and surface electromyographic (sEMG) recordings are shown. [ABSTRACT FROM AUTHOR]

Published
2005
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