Filtered Virtual Reference: A New Method for the Reduction of Power Line Interference With Minimal Distortion of Monopolar Surface EMG

Botter, Alberto; Vieira, Taian

doi:10.1109/tbme.2015.2438335

Cited by 16 publications

(6 citation statements)

References 29 publications

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“…For the in vivo data, HDsEMG signals were bandpass filtered in the 20–400 Hz band and, when required, residual power line interference were removed 48 . Afterwards, signals were decomposed into individual MU firing patterns with a validated method based on convolution kernel compensation (CKC) 35 , 49 .…”

Section: Methodsmentioning

confidence: 99%

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Carbonaro

Meiburger

Seoni

et al. 2022

Sci Rep

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Electromyography and ultrasonography provide complementary information about electrophysiological and physical (i.e. anatomical and mechanical) muscle properties. In this study, we propose a method to assess the electrical and physical properties of single motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted from HDsEMG were used to identify the corresponding region of muscle tissue displacement in US videos. The time evolution of the tissue velocity in the identified region was regarded as the MU tissue displacement velocity. The method was tested in simulated conditions and applied to experimental signals to study the local association between the amplitude distribution of single MU action potentials and the identified displacement area. We were able to identify the location of simulated MUs in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU displacement velocity (cc > 0.85). Multiple regression analysis of 180 experimental MUs detected during isometric contractions of the biceps brachii revealed a significant association between the identified location of MU displacement areas and the centroid of the EMG amplitude distribution. The proposed approach has the potential to enable non-invasive assessment of the electrical, anatomical, and mechanical properties of single MUs in voluntary contractions.

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Section: Methodsmentioning

confidence: 99%

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Carbonaro

Meiburger

Seoni

et al. 2022

Sci Rep

Self Cite

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“…Since the noise out of the interesting band of the sEMG signal was usually filtered and removed in the analog conditioning performed in the signal acquisition stage of the process, the digital filtering often aimed to remove artifacts within the bandwidth of the sEMG signal through the application of adaptive filters [18,19,20,21,22,23]. To provide a straightforward and time efficient filter alternative for the sEMG signal filtering, we developed the AVT filter.…”

Section: Methodsmentioning

confidence: 99%

Open Database for Accurate Upper-Limb Intent Detection Using Electromyography and Reliable Extreme Learning Machines

Cene

Machado

Balbinot

2019

Sensors

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Surface Electromyography (sEMG) signal processing has a disruptive technology potential to enable a natural human interface with artificial limbs and assistive devices. However, this biosignal real-time control interface still presents several restrictions such as control limitations due to a lack of reliable signal prediction and standards for signal processing among research groups. Our paper aims to present and validate our sEMG database through the signal classification performed by the reliable forms of our Extreme Learning Machines (ELM) classifiers, used to maintain a more consistent signal classification. To perform the signal processing, we explore the use of a stochastic filter based on the Antonyan Vardan Transform (AVT) in combination with two variations of our Reliable classifiers (denoted R-ELM and R-Regularized ELM (RELM), respectively), to derive a reliability metric from the system, which autonomously selects the most reliable samples for the signal classification. To validate and compare our database and classifiers with related papers, we performed the classification of the whole of Databases 1, 2, and 6 (DB1, DB2, and DB6) of the NINAProdatabase. Our database presented consistent results, while the reliable forms of ELM classifiers matched or outperformed related papers, reaching average accuracies higher than 99 % for the IEEdatabase, while average accuracies of 75 . 1 % , 79 . 77 % , and 69 . 83 % were achieved for NINAPro DB1, DB2, and DB6, respectively.

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“…This evidence is particularly important given the high relevance of PLI-related problems in biopotential recordings [25], [44], [45]. Although several hardware and signal processing techniques aimed to remove power line interference were proposed in the last twenty years [46]- [48], it is required to design biopotential amplifiers with high PLI rejection to avoid saturation at the input/output of the amplifier and improve the overall SNR of the collected signals [15], [49].…”

Section: B Results and Discussionmentioning

confidence: 99%

Design and Validation of a Wireless Body Sensor Network for Integrated EEG and HD-sEMG Acquisitions

Cerone

Giangrande

Ghislieri

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Sensorimotor integration is the process through which the human brain plans the motor program execution according to external sources. Within this context, corticomuscular and corticokinematic coherence analyses are common methods to investigate the mechanism underlying the central control of muscle activation. This requires the synchronous acquisition of several physiological signals, including EEG and sEMG. Nevertheless, physical constraints of the current, mostly wired, technologies limit their application in dynamic and naturalistic contexts. In fact, although many efforts were made in the development of biomedical instrumentation for EEG and HD-sEMG signal acquisition, the need for an integrated wireless system is emerging. We hereby describe the design and validation of a new fully wireless body sensor network for the integrated acquisition of EEG and HD-sEMG signals. This Body Sensor Network is composed of wireless bio-signal acquisition modules, named sensor units, and a set of synchronization modules used as a general-purpose system for time-locked recordings. The system was characterized in terms of accuracy of the synchronization and quality of the collected signals. An in-depth characterization of the entire system and an end-to-end comparison of the wireless EEG sensor unit with a wired benchmark EEG device were performed. The proposed device represents an advancement of the State-of-the-Art technology allowing the integrated acquisition of EEG and HD-sEMG signals for the study of sensorimotor integration.

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Filtered Virtual Reference: A New Method for the Reduction of Power Line Interference With Minimal Distortion of Monopolar Surface EMG

Cited by 16 publications

References 29 publications

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Open Database for Accurate Upper-Limb Intent Detection Using Electromyography and Reliable Extreme Learning Machines

Design and Validation of a Wireless Body Sensor Network for Integrated EEG and HD-sEMG Acquisitions

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