Paper Info
Title
Time-Frequency Analysis for Characterizing EMG Signals During fMRI Acquisitions.
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.
Year
DOI
Venue
2004
10.1007/1-4020-3432-6_37
Biological and Artificial Intelligence Environments
Keywords
Field
DocType
SEMG,FMRI,time-frequency analysis,wavelet transform
Functional magnetic resonance imaging,Pattern recognition,Software analysis pattern,Computer science,Motor control,Electromagnetic compatibility,Time–frequency analysis,Artificial intelligence,Magnetic resonance imaging,Wavelet,Wavelet transform
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Bruno Azzerboni1255.31
Maurizio Ipsale272.49
Mario Carpentieri3143.47
Fabio La Foresta49315.69