High Density EMG, also referred to as HD EMG, HDEMG or HD-sEMG, is an advanced non-invasive technique for measuring muscle activation patterns. High density electrode grids provide more detailed information of the muscle and can be used for multiple applications. With the SAGA it is easy to measure even under the most challenging circumstances and still obtain the highest data quality possible. With the easy to use connector cable and stick on semi-disposable printed grid electrodes you can start measuring right away.
TMSi understands that signal quality is extremely important, the quality of your research relies on the quality of the acquired data. The SAGA is designed in such a way, highest data quality is ensured. Our active shielding technology guarantees no cable movement artifacts while maintaining extreme thin, flexible and lightweight cabling and connectivity for HD EMG grids. This allows total freedom of movement. The SAGA is the perfect tool/technology for any kind of mobile set up.
HD EMG accessories
Disposable HD EMG grids
HD EMG grids are flexible printed grid arrays that are attached to the subject using a double sided sticker. TMSi offers two sizes of HD EMG grids both in a 8 x 8 configuration. The small grids have a inter-electrode distance of 4 mm and the large grids a inter-electrode distance of 8.5 mm.The Ag/AgCl electrodes guarantee the most stable and best signal quality. Double sided adhesives are easy to apply using our convenient grid-alignment applicator.
HD EMG accessories
High density grid connector-box
Active shielding technology, in contrast to active electrodes, allow for a lightweight connector module. The module only contains the connector for the disposable HD EMG grids and the interface to the active shielded cable. The module can easily be mounted to the subject using some tape. Connect the HD EMG grid and start your measurement right away.
The 3 auxiliary inputs of the SAGA facilitate simultaneous measurement of other parameters, such as acceleration. Our 3D accelerometer, measures acceleration and position in three dimensions: X, Y and Z. Depending on your research set-up, this can be a nice addition or a prerequisite. Also other sensors are available such as respiration, GSR and saturation. All sensors are in house designed and fabricated to perfectly perform together with the SAGA, to complete all of your research purposes.
SAGA32/64+ is not intended to be used for diagnosis or treatment of disease. Our products are not marketed as Medical Devices and are to be used for research purposes only.
Access to raw data
Recording high density EMG data can be done in TMSi Polybench or in the TMSi SAGA Interface for MATLAB. Polybench has a convenient application with an easy to use graphical user interface with visualization of your EMG data and electrode impedances. The MATLAB interface requires your own programming skills but allows online processing of data and quicker availability of the data. TMSi has an open access policy. Our systems provide raw data, including the DC offset. All data is open accessible and ready to use in post-processing.
Optimal data quality in both stationary and mobile settings
Active Shielding technology to prevent cable movement artefacts and mains interference
Impedance mode to check electrode contact quality
16-bit trigger input on Docking Station and 1-bit trigger interface on Data Recorder
Synchronization output for integration with external devices
Lost data recovery in mobile setups
Data Recorder with 32 or 64 unipolar inputs, 2 dual bipolar inputs (4 channels), 3 triple auxiliary inputs (9 channels), digital sensor (1 channel), build-in 3D accelerometer, event marker button.
Premium data quality: o 24-bit resolution o True DC: 0-800 Hz analogue bandwidth o Max 4096 Hz sampling rate, o < 0,8 µVrms noise (0,1 … 100 Hz) o 100 dB CMRR (@50/60 Hz) o +/- 150 mV input range Input range up to +/- 600 mV available on request.
Drivers for Windows 10 (64-bit) and Linux OS
Direct interface to MATLAB available
Separable Docking Station and Data Recorder
We support the various use scenarios with the innovative Data Recorder – Docking Station concept. This gives the optimal data transmission in stationary/lab setups and the optimal wearability in mobile or outdoor setups.
The Data Recorder contains the amplifier that measures the electrophysiological signals from your brain, muscles, heart or any other bio-electrical source. The Data Recorder can connect to the SAGA Docking Station in three ways: docked to each other, via Wi-Fi or via optical fiber. Use the Data Recorder without the Docking Station in ambulatory mode for home monitoring or outdoor use.
The Docking Station is the communication interface to your PC and the main processing unit of data packages. It collects the data from the Data Recorder, together with any additional event triggers on the 16-bit trigger input and keeps track of any lost data in wireless transmission. The data is send to the PC via the USB output.
Software, drivers, documentation and manuals can also be found in the support section.
TMSi Polybench is a software application toolbox, a veritable blank canvas for the creation of customized configurations; from basic measurements to sophisticated research applications. The possibilities are limited only by your imagination.
TMSi Polybench can be considered a personalized “application factory” allowing the user to control the high-quality raw data input from any TMSi amplifier to build the exact system they need. Choose among TMSi Polybench pre-set functions and controls or build customized processes in pursuit of practically any conceivable desired outcome.
In research, by definition, the questions being asked have specific challenges that off-the-shelf software often cannot be adapted to meet. TMSi Polybench allows you complete freedom in design so that data collection, measurement and manipulation is completely under your direction and not limited by the design of any specific software offering or system manufacturer.
TMSi Polybench can accept input from all TMSi amplifiers.
The SAGA Interface for MATLAB is a library for MATLAB that allows direct interfacing to TMSi amplifiers to acquire data, write and read (.poly5) files via MATLAB and/or export data to EEGLAB (http://sccn.ucsd.edu/eeglab/).
The goal of the library is to give an easy programmable interface to SAGA 32+ and SAGA64+. It contains basic examples that should be enough to get you started.
The library provides:
• Sampling from SAGA 32+ and SAGA 64+.
• (Limited) Real-Time plotting of sampled data.
• Directly saving sampled data to .Poly5 file format.
• Offline export of .Poly5 data to EEGLAB.
The SAGA Interface for MATLAB is a free library available for download. TMSi supports the usage of the interface to the extend of the installation of the interface, communication between driver and interface and the interface itself. Note that TMSi cannot support the usage of MATLAB.
The interface is distributed under MIT license. Please respect the license conditions. Note that functionality for on-board (ambulatory) recordings on SAGA is not supported by the SAGA Interface for MATLAB. This requires a TMSi Polybench license.
The SAGA for HD EMG is a package set that gives you all the basic ingredients to set up a HD EMG measurement setup. You can extend this setup with multiple grids, additional sensors etc. Contact us to receive a personal offer!
• SAGA 64+ Data Recorder
• SAGA Docking Station
• Mains power adapter
• USB Cable (1.8m)
• Patient Ground Wristbands
• Patient Ground Cable
• Common Reference Cable
• Optical Fiber (10m)
• Battery and external battery charger
• SAGA Bracket + Straps
• User Manual
• USB Stick with TMS SAGA Device Driver and TMSi Polybench
• Dual bipolar cable for EMG, ECG and/or EOG (snap-on electrodes)
• Single bipolar cable for EMG, ECG and/or EOG (snap-on electrodes)
• Bag of 50x disposable snap electrodes
SAGA 32+ is not mentioned here, as our grids are mainly 64 channels. Technically, the SAGA32+ is fully compatible with the HD EMG package.
HD EMG Package
• 64 channel HD EMG adapter cable with lightweight connector box.
• 64 channel HD EMG grid with 8×8 configuration and 8 mm interelectrode distance
• Double sided adhesive stickers for 64 channel HD EMG grid with 8×8 configuration and 8 mm interelectrode distance
• Double sided adhesive stickers for 64 channel HD EMG grid with 8×8 configuration and 4 mm interelectrode distance.
• HD EMG Basic Kit: tool for aligning double sided adhesives on the grids. Gel and skin preparation gel.
Webinar by Dr. James Bashford (King's College London)
Detecting motor unit pathophysiology in ALS using high-density surface EMG
<![if !supportLists]>·<![endif]>Zhang, C., Dias, N., He, J., Zhou, P., Li, S., and Zhang Y*. (2019). Global innervation zone identification with high-density surface electromyography. IEEE Trans on Biomedical Engineering. https://doi.org/10.1109/TBME.2019.2919906
<![if !supportLists]>·<![endif]>Zhang, C., Chen, Y. T., Liu, Y., Zhou, P., Li, S., & Zhang, Y*. (2019). Three dimensional innervation zone imaging in spastic muscles of stroke survivors. Journal of neural engineering, 16(3), 034001. https://doi.org/10.1088/1741-2552/ab0fe1
<![if !supportLists]>·<![endif]>Dias, N.C., Li, X., Zhang, C., He, J., and Zhang, Y*. (2018). Innervation asymmetry of the external anal sphincter in aging characterized from high-density intra-rectal surface EMG recordings. Neurourology and Urodynamics. https://doi.org/10.1002/nau.23809
<![if !supportLists]>·<![endif]>Zhang, C†., Peng, Y., Li, S., Zhou, P., Rymer, W.Z., & Zhang, Y*. (2017). Characterization of the Three-dimensional Innervation Zone Distribution in Muscles from M-wave Recordings. Journal of Neural Engineering. 14(3), 036011. https://doi.org/10.1088/1741-2552/aa65dd
<![if !supportLists]>·<![endif]>Peng Y†., He J, Khavari, R., Boone, T. B. & Zhang, Y*. (2016). Functional mapping of the pelvic floor and sphincter muscles from high-density surface EMG recordings. International Urogynecology Journal. 1-8. https://dx.doi.org/10.1007%2Fs00192-016-3026-4
<![if !supportLists]>·<![endif]>Liu, Y., Ning, Y., Li, S., Zhou, P., Rymer, W., & Zhang, Y*. (2015). Three-Dimensional Innervation Zone Imaging from Multi-Channel Surface EMG Recordings. International Journal of Neural Systems, 25(06), 1550024. https://doi.org/10.1142/S0129065715500240
<![if !supportLists]>·<![endif]>Ning, Y., Zhu, X., Zhu, S., & Zhang, Y*. (2015). Surface EMG Decomposition Based on K-means Clustering and Convolution Kernel Compensation. Biomedical and Health Informatics, IEEE Journal of, 19(2), 471-477. https://doi.org/10.1109/JBHI.2014.2328497
<![if !supportLists]>·<![endif]>M Al Harrach, S Boudaoud, V Carriou and F Marin. (2017). Multi-muscle Force Estimation using Data Fusion and HD-sEMG: an Experimental Study. 4th IEEE ICABME beirut, Lebanon, October 2017. https://doi.org/10.1109/ICABME.2017.8167529
<![if !supportLists]>·<![endif]>M Al Harrach, S. Boudaoud, V. Carriou, J Laforet, AJ Letocart, JF Grosset, F Marin. (2017). Investigation of the HD-sEMG probability density function shapes with varying muscle force using data fusion and shape descriptors. Comp. Biol Med. 2017 Oct 1; 89:44-58. https://doi.org/10.1016/j.compbiomed.2017.07.023
<![if !supportLists]>·<![endif]>M. Al Harrach, S. Boudaoud, M. Hassan, F. Ayachi, D. Gamet, J.F. Grosset, and F. Marin. (2017). Denoising of HD-sEMG signals using Canonical Correlation Analysis. Medical & Biological Engineering & Computing 2017;55 (3):375-388. http://dx.doi.org/10.1007%2Fs11517-016-1521-x
Publications on HD EMG using TMSi products
Publications on HD EMG using TMSi products
<![if !supportLists]>·<![endif]>M. Al Harrach, S. Boudaoud, D. Gamet, J.F. Grosset, and F. Marin. (2014). “Evaluation of High Order Statistic Trends from HD-sEMG recordings during Ramp Exercise”.36th IEEE EMBS Conf., Chicago, U.S.A, 2014. September. https://doi.org/10.1109/EMBC.2014.6944057
<![if !supportLists]>·<![endif]>J Bashford, U Masood, A Wickham, R Iniesta, E Drakakis, M Boutelle, K Mills, C Shaw. (2020). Fasciculations demonstrate daytime consistency in amyotrophic lateral sclerosis.Muscle Nerve [e-pub ahead of print]. https://doi.org/10.1002/mus.26864
<![if !supportLists]>·<![endif]>J Bashford, A Wickham, R Iniesta, E Drakakis, M Boutelle, K Mills, C Shaw. (2020). The rise and fall of fasciculations in amyotrophic lateral sclerosis. Brain Commun, Volume 2, Issue 1. https://doi.org/10.1093/braincomms/fcaa018
<![if !supportLists]>·<![endif]>J Bashford, K Mills & C Shaw. (2020). The evolving role of surface electromyography in amyotrophic lateral sclerosis. Clin Neurophysiol, Volume 131, Issue 4, p942-950. https://doi.org/10.1016/j.clinph.2019.12.007
<![if !supportLists]>·<![endif]>J Bashford, A Wickham, R Iniesta, E Drakakis, M Boutelle, K Mills, C Shaw. (2020). Preprocessing surface EMG data removes voluntary muscle activity and enhances SPiQE fasciculation analysis. Clin Neurophysiol, Volume 131, January 2020, p265-273. https://doi.org/10.1016/j.clinph.2019.09.015
<![if !supportLists]>·<![endif]>J Bashford, A Wickham, R Iniesta, E Drakakis, M Boutelle, K Mills, C Shaw. (2019). SPiQE: an automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis. Clin Neurophysiol,Volume 130, Issue 7, July 2019, p1083-1090. https://doi.org/10.1016/j.clinph.2019.03.032
<![if !supportLists]>·<![endif]>Afsharipour, B., Sandhu, M. S., Rasool, G., Suresh, N. L., & Rymer, W. Z. (2016). Using surface electromyography to detect changes in innervation zones pattern after human cervical spinal cord injury. 38th International Conference of the IEEE Engineering in Medicine and Biology Society, 3757–3760. https://doi.org/10.1109/EMBC.2016.7591545
<![if !supportLists]>·<![endif]>Staudenmann, D., Stegeman, D. F., & van Dieën, J. H. (2013). Redundancy or heterogeneity in the electric activity of the biceps brachii muscle? Added value of PCA-processed multi-channel EMG muscle activation estimates in a parallel-fibered muscle. Journal of Electromyography and Kinesiology, 23(4), 892–898. https://doi.org/10.1016/j.jelekin.2013.03.004
<![if !supportLists]>·<![endif]>Vries, I. E. J. De, Daffertshofer, A., Stegeman, D. F., & Tjeerd, W. (2016). Functional connectivity in neuromuscular system underlying bimanual muscle synergies. https://doi.org/10.1152/jn.00460.2016
<![if !supportLists]>·<![endif]>Xu, L., Rabotti, C., & Mischi, M. (2013). Novel vibration-exercise instrument with dedicated adaptive filtering for electromyographic investigation of neuromuscular activation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(2), 275–282. https://doi.org/10.1109/TNSRE.2012.2219555
<![if !supportLists]>·<![endif]>Zhu, M., Yang, W., Samuel, O. W., Xiang, Y., Huang, J., Li, G., & Member, S. (2016). A Preliminary Evaluation of Myoelectrical Energy Distribution of the front neck muscles in Pharyngeal Phase during Normal Swallowing, (August), 1700–1703. https://doi.org/10.1109/EMBC.2016.7591043