Towards a Simplified Estimation of Muscle Activation Pattern from MRI and EMG Using Electrical Network and Graph Theory Muscle functional MRI (mfMRI) is an imaging technique that assess musclesxe2x80x99 activity, exploiting a shift in the T2relaxation time between resting and active state on muscles. It is accompanied by the use of electromyography (EMG) to have a better understanding of the muscle electrophysiology; however, a technique merging MRI and EMG information has not been defined yet. In this paper, we present an anatomical and quantitative evaluation of the method our group introduced in to quantify its validity in terms of muscle pattern estimation for four subjects during four isometric tasks. Muscle activation pattern are estimated using a resistive network to model the morphology in the MRI. An inverse problem is solved from sEMG data to assess muscle activation. The results have been validated with a comparison with physiological information and with the fitting on the electrodes space. On average, over 90% of the input sEMG information was able to be explained with the estimated muscle patterns. There is a match with anatomical information, even if a strong subjectivity is observed among subjects. With this paper we want to proof the methodxe2x80x99s validity showing its potential in diagnostic and rehabilitation fields.

Generalization of a waveletbased algorithm to adaptively detect activation intervals in weak and noisy myoelectric signals Abstract   This study introduces an adaptive implementation of a Continuous Wavelet Transform (CWT) decomposition technique used to estimate the timing of muscular activation in weak and noisy myoelectric signals. The algorithm updates automatically the threshold based on the statistical properties of the EMG data, through an iterative estimation of the SignaltoNoise Ratio (SNR). Moreover, it includes a stopping criterion for the number of CWT decomposition levels, and this allows a relevant decrease of the computational burden. This algorithm was applied to both synthetic and semisynthetic signals, and compared against the original formulation of the CWTbased technique and a common thresholdbased technique for the detection of muscle activations. Performance of these techniques was assessed by using Bias, Relative Timing Error and Accuracy of the detection. Bias values resulted lower than 18xe2x80xafms, Relative Timing Error lower than 5% and Accuracy greater than 97% for all the tested SNR (ranging from xe2x88x922xe2x80xafdB to 10xe2x80xafdB), and with a substantial independence from SNR levels. The performance was shown to hold also if the hypothesis of absence of muscular activation in the reference window cannot be guaranteed. The results show that the proposed approach, which is adaptive, operatorindependent and iterative, performs properly when applied to weak and noisy myoelectric signals, and is thus a valid general solution when dealing with clinical conditions where muscular activity is low, and when recording conditions cannot be entirely controlled.