Identifying significant frequencies in surface EMG signals for localization of neuromuscular activity

LoPresti, Edmund F.; Jesinger, R.A.; Stonick, V.L.

doi:10.1109/iembs.1995.579384

Cited by 9 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Discrete source models [3,25,26,31] do not perform well, and since the number of sources in active muscles can be large, a distributed underdetermined formulation of the inverse problem, also used in EEG [29], seems to be more promising.…”

Section: Introductionmentioning

confidence: 99%

Source localization in electromyography using the inverse potential problem

2011

View full text Add to dashboard Cite

We describe an efficient method for reconstructing the activity in human muscles from an array of voltage sensors on the skin surface. MRI is used to obtain morphometric data which is segmented into muscle tissue, fat, bone and skin, from which a finite element model for volume conduction is constructed. The inverse problem of finding the current sources in the muscles is solved using a careful regularization technique which adds a priori information, yielding physically reasonable solutions from among those that satisfy the basic potential problem. Several regularization functionals are considered and numerical experiments on a 2D test model are performed to determine which performs best. The resulting scheme leads to numerical difficulties when applied to large scale 3D problems. We clarify the nature of these difficulties and provide a method to overcome them, which is shown to perform well in the large scale problem setting.

show abstract

Section: Introductionmentioning

confidence: 99%

Source localization in electromyography using the inverse potential problem

2011

View full text Add to dashboard Cite

show abstract

“…The functions a n ðx; tÞ (referred to as basis waveforms in the following) could be simulated using a model replicating as far as possible the investigated physiological system: for example, the geometry of the tissues could be measured (by ultrasound scanning [22] or MRI [15,16,[18][19][20]23]), their conductivity could be taken from the literature [24,25] and the anatomy of the muscle fibres (positions of the IZ and tendons) could be investigated by preliminary surface EMG recordings [8][9][10][11]. Another possible choice is to measure the surface potential resulting from the activity of a specific region: needle EMG can be recorded from different locations and decomposed in order to identify the activity of single MUs [26]; then the surface response related to each identified MU can be estimated by spike triggered averaging [27], obtaining very selective information on the surface EMG response of the activity of specific muscle regions.…”

Section: Algorithm For Source Localizationmentioning

confidence: 99%

“…The localization of sources within the brain was studied extensively from surface electroencephalogram (EEG) [13]. In the field of EMG, some studies have been proposed [14][15][16][17][18][19][20]. The identification of the source of a single MUAP was addressed by considering that the decay of the potential in the transverse direction with respect to the fibres is slower when the MU is deeper [17].…”

Section: Introductionmentioning

confidence: 99%

“…This method requires the (computationally intensive) decomposition of the surface EMG as a preliminary step, if interference EMG is considered. Sophisticated methods, based on advanced simulations by the finite elements method (FEM), were also proposed to investigate interference EMG [14][15][16][17][18]. More recently, FEM simulations combined with regularization techniques were applied [19][20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real time identification of active regions in muscles from high density surface electromyogram

Mesin¹

2015

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…The only previous work [2,19,23,31] on source localization in sEMG that we are aware of uses an overdetermined discrete source model (see below for details on this type of models). These efforts seem to be of a preliminary nature.…”

Section: Related Workmentioning

confidence: 99%

Computed myography: three-dimensional reconstruction of motor functions from surface EMG data

Doel¹,

Ascher²,

Pai³

2008

Inverse Problems

View full text Add to dashboard Cite

We describe a methodology called computed myography (CMG) to qualitatively and quantitatively determine the activation level of individual muscles by voltage measurements from an array of voltage sensors on the skin surface. A finite element model for electrostatics simulation is constructed from morphometric data. For the inverse problem we utilize a generalized Tikhonov regularization. This imposes smoothness on the reconstructed sources inside the muscles and suppresses sources outside the muscles using a penalty term. Results from experiments with simulated and human data are presented for activation reconstructions of three muscles in the upper arm (biceps brachii, bracialis, and triceps). This approach potentially offers a new clinical tool to sensitively assess muscle function in patients suffering from neurological disorders (e.g., spinal cord injury), and could more accurately guide advances in the evaluation of specific rehabilitation training regimens.

show abstract

Identifying significant frequencies in surface EMG signals for localization of neuromuscular activity

Cited by 9 publications

References 1 publication

Source localization in electromyography using the inverse potential problem

Source localization in electromyography using the inverse potential problem

Real time identification of active regions in muscles from high density surface electromyogram

Computed myography: three-dimensional reconstruction of motor functions from surface EMG data

Contact Info

Product

Resources

About