Signalhunter: Software For Electrohysiological Data Analysis And Visualization

Souza, Victor H.; Peres, André; Zacharias, Leonardo Rakauskas; Baffa, Oswaldo

doi:10.5281/zenodo.1326308

Cited by 3 publications

(2 citation statements)

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“…The sEMG signals were processed by the SignalHunter software program 11 written in MATLAB R2015a (Math- Works, Natick, MA, USA). A 2-s window was extracted from the 10th second of the sEMG signal acquisition, to record data in the mandibular rest position, and another 2-s window was selected from the dynamometer to assess the MVBF and SVBF conditions.…”

Section: Methodsmentioning

confidence: 99%

Response of masticatory muscles to treatment with orthodontic aligners: a preliminary prospective longitudinal study

Paes-Souza

Garcia

Souza

et al. 2023

Dental Press J. Orthod.

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Introduction: The emergence of orthodontic aligners has provided an aesthetic and comfortable option for orthodontic treatment. However, the encapsulated design of the aligners can influence the masticatory muscles, and might compromise safe treatment. Objective: This preliminary longitudinal study aimed to investigate whether the use of orthodontic aligners affects the biting force and myoelectric activity of the superficial masseter and anterior temporal muscles. Methods: Ten subjects participated in the study and underwent treatment during an 8-month follow-up period. The root mean square (RMS), the median power frequency (MPF) of the surface electromyography (sEMG) signals, and the biting force (kgf) were recorded and normalized relative to the pretreatment condition. The data were analyzed by repeated-measure analysis of variance (ANOVA), with the significance level set at 5%. Results: Both the superficial masseter and the anterior temporal muscles presented an increase in sEMG signal activity during the treatment, with a marked increase in the latter compared to the former (p<0.05). Moreover, a significant decrease in bite force was evidenced (p<0.05). Conclusions: This preliminary study observed that the orthodontic aligners affected the muscle recruitment pattern of masticatory muscles, and reduced biting performance during the 8-month follow-up period.

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Section: Methodsmentioning

confidence: 99%

Response of masticatory muscles to treatment with orthodontic aligners: a preliminary prospective longitudinal study

Paes-Souza

Garcia

Souza

et al. 2023

Dental Press J. Orthod.

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“…The EMG signals were processed using the SignalHunter software (Souza et al 2018b) written in Matlab R2017a (MathWorks Inc., Natick, USA). The peak-to-peak amplitude was computed for MEPs extracted from the EMG signal in a time window 10-60 ms after the TMS pulse.…”

Section: Motor Map Processingmentioning

confidence: 99%

Forearm and Hand Muscles Exhibit High Coactivation and Overlapping of Cortical Motor Representations

et al. 2022

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Most of the motor mapping procedures using navigated transcranial magnetic stimulation (nTMS) follow the conventional somatotopic organization of the primary motor cortex (M1) by assessing the representation of a particular target muscle, disregarding the possible coactivation of synergistic muscles. In turn, multiple reports describe a functional organization of the M1 with an overlapping among motor representations acting together to execute movements. In this context, the overlap degree among cortical representations of synergistic hand and forearm muscles remains an open question. This study aimed to evaluate the muscle coactivation and representation overlapping common to the grasping movement and its dependence on the stimulation parameters. The nTMS motor maps were obtained from one carpal muscle and two intrinsic hand muscles during rest. We quantified the overlapping motor maps in size (area and volume overlap degree) and topography (similarity and centroid Euclidean distance) parameters. We demonstrated that these muscle representations are highly overlapped and similar in shape. The overlap degrees involving the forearm muscle were significantly higher than only among the intrinsic hand muscles. Moreover, the stimulation intensity had a stronger effect on the size compared to the topography parameters. Our study contributes to a more detailed cortical motor representation towards a synergistic, functional arrangement of M1. Understanding the muscle group coactivation may provide more accurate motor maps when delineating the eloquent brain tissue during pre-surgical planning.

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DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

Milardovich

Souza

Zubarev

et al. 2023

Sci Rep

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The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characterization of thousands of MEPs. Given the difficulty of developing reliable and accurate algorithms, currently the assessment of MEPs is performed with visual inspection and manual annotation by a medical expert; making it a time-consuming, inaccurate, and error-prone process. In this study, we developed DELMEP, a deep learning-based algorithm to automate the estimation of MEP latency. Our algorithm resulted in a mean absolute error of about 0.5 ms and an accuracy that was practically independent of the MEP amplitude. The low computational cost of the DELMEP algorithm allows employing it in on-the-fly characterization of MEPs for brain-state-dependent and closed-loop brain stimulation protocols. Moreover, its learning ability makes it a particularly promising option for artificial-intelligence-based personalized clinical applications.

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Signalhunter: Software For Electrohysiological Data Analysis And Visualization

Cited by 3 publications

References 0 publications

Response of masticatory muscles to treatment with orthodontic aligners: a preliminary prospective longitudinal study

Response of masticatory muscles to treatment with orthodontic aligners: a preliminary prospective longitudinal study

Forearm and Hand Muscles Exhibit High Coactivation and Overlapping of Cortical Motor Representations

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

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