Surface electromyographic activity of neck muscles in dental students: Dataset

Published: 31 May 2024| Version 1 | DOI: 10.17632/g54r5t4gsp.1
Manuel Barbosa de Almeida,


Dataset of the surface electromyographic activity of neck muscles in dental students. Includes sociodemographic data and bilateral muscle activity of upper trapezius and sternocleidomastoid muscles.


Steps to reproduce

Electromyography data acquisition Surface electromyography data were recorded for the superficial neck flexors and extensors according to established protocols 20, 21, following the recommendations of the Surface Electromyography for the Non-Invasive Assessment of Muscles (SENIAM). The skin was shaved and cleaned with 70% alcohol prior to electrode placement to ensure sEMG signal quality. Disposable Ag/AgCl Ambu® BlueSensor N electrodes (Ambu A/S, Ballerup, Denmark) were utilized, each with a 10 mm diameter and positioned 20 mm apart center-to-center, were utilized. Electrodes were applied bilaterally over the sternal head of sternocleidomastoid (SCM), on the distal third of the muscle belly to avoid the innervation zone 21, and over the upper trapezius (UT) muscle, on the muscle belly at a midpoint between C7 spinous process and the acromial process 20. The reference electrode was placed over the center of the clavicle bone. Surface electromyography data were amplified and digitized using the biosignalsplux 8-channel hub (PLUX Wireless Biosignals S.A., Lisbon, Portugal), featuring an analog-to-digital converter with a resolution of 16 bits per channel, input impedance exceeding 100GOhm, an amplifier gain of 1,000, a common-mode rejection ratio of 100 dB, and with a sampling frequency of 1000Hz. Maximum voluntary isometric contraction (MVIC) For the MVIC record, participants were in supine with arms alongside the body and they were instructed to perform a combined head and neck flexion able to gentle lift the head off the plinth, maintaining this position for sEMG normalization purposes. Electromyography data processing The sEMG signals were wirelessly transmitted to a laptop (AMD Ryzen 5, 8GB RAM, 500GB SATA HDD, Windows 11 64-bit operating system) with Opensignals® software (Plux Wireless Biosignals S.A., Lisboa, Portugal) to monitor and store the retrieved data. A 10-second recording from the four channels was saved in a single file for each participant and level of the CCFT. Electromyographic signals were preprocessed with Matlab R2023b (The Mathworks Inc., USA) with a customized script based on a standard procedure 24 to ensure signal quality and integrity during recordings. Finally, the raw sEMG signals were processed using scientific Python development environment (SPYDER) version 5.4.3 with a customized script that followed standard procedures 24. A 4th-order Butterworth filter was used, with a bandpass between 20Hz and 500Hz. The root mean square (RMS) was calculated for each muscle from a 5-second window in the middle of the 10-second recording for each CCFT level 22. SEMG data was normalized and expressed as a percentage of the maximum RMS obtained during a 2-second MVIC for neck flexion and extension. The maximum RMS was determined using a 200ms window (-100ms and +100ms) centered on the sEMG peak in the MVIC signal for each channel.


Escola Superior de Saude Egas Moniz, Universidade de Lisboa Faculdade de Motricidade Humana


Electromyography, Musculoskeletal Disorder