The Refa has definitely proven its value, being one of the most stable and reliable systems on the market. It has been available for over a decade. Numerous renowned articles are published using the high-quality data acquired by the Refa. The system has been used for all kinds of electrophysiological measurements. Even though SAGA32/64+ is the successor, the Refa remains popular among researchers, confirming the quality and reliability of this system.

Refa is not intended to be used for diagnosis or treatment of disease. It is not marketed as Medical Devices and are to be used for research purposes only. Our stock is limited, so please contact our sales team to inform about the options.

Read more about the SAGA32/64+ here
EEG, HD EMG, EOG, ECG and many more

Multimodal measurements

The Refa system is available with 32 or 64 ExG unipolar inputs, suitable for various electrophysiological measurements such as EEG and HD-EMG. The system includes 4 auxiliary inputs and 4 bipolar inputs, so your research can easily be extended with EMG, ECG and/or EOG. Active sensors can be connected through the auxiliary inputs. Complete your set-up with the measurement of physiologic parameters like temperature, respiration, acceleration and more.

Active shielding technology assures clean signals, free of mains interference and cable movement artifacts. The Refa provides raw, unfiltered 24-bit resolution data. It does not have hardware filters, other than anti-aliasing. The system is designed in such a way, all channels are amplified against the average of all connected inputs. You can easily refer your data to any other reference you like.

Active shielding

No mains interference, no cable movement artifacts

Fit your needs

We offer a wide range of sensors, caps and electrodes. Do you need something else or have a specific request? Please contact us, to discuss the available options.



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  • A stable and versatile system
  • Available with 32 or 64 unipolar inputs
  • 4 Bipolar inputs to simultaneously measure EOG, EMG, EEG and ECG
  • 4 Auxiliary inputs to connect sensors
  • 8 Isolated digital trigger inputs to synchronize with other devices
  • Sample frequency 2048 Hz
  • Built-in impedance measurement
  • Superior EEG quality because of active shielding technology
  • Real-time data acces through a direct MATLAB interface
  • Easy to use Polybench Quick Recording Application for easy data acquistion
  • No hardware filters, for true signal acquisition and raw data
  • For research applications only. Not intended for use in diagnostic procedures or other medical Purposes
  • Drivers for Windows 7 and Windows 10

TMSi Polybench

Polybench is our software application toolbox, a blank canvas which gives you the opportunity to create your own research application. Choose one of the pre-set functions or build your own process, it’s up to you. The possibilities are only limited by your imagination.

In research, questions that need to be answered are unique and often require individual analyzing methods. Off-the-shelf software is not completely customizable and modification options are limited. With Polybench you will have complete freedom in design. Data collection, measurement and analysis, it is completely within your control and not limited by specific software or systems.

Polybench can receive input from all TMSi amplifiers.

Download TMSi Polybench

TMSi MATLAB Interface

If you are familiar with MATLAB or want to analyze your data with EEGLAB, TMSi offers also a MATLAB Interface. This enables data acquisition, write and read .Poly5 files via MATLAB and export data to EEGLAB.

The functionalities in this library are limited to signal acquisitions and loading data from previously recorded data in TMSi Polybench and our MATLAB interface. The goal of this library is to give an easy programmable interface to TMSi devices, with freedom of programming. This library contains example codes and has everything you need to get you started.

The library provides:
• Sampling from USB, Wi-Fi, network and Bluetooth TMSi devices.
• (Limited) Real-time plotting of sampled data.
• Directly saving sampled data to .Poly5 file format.
• Offline export of .Poly5 data to EEGLAB.
• Support for sampling using SynFi and Fusbi.

– All functionality needed to perform on-board memory recordings (so-called ambulatory measurement) as present in the Mobita, Mobi, Porti and Saga is NOT implemented
– This interface is distributed under MIT license. The use of this interface is not supported by TMSi.

The TMSi MATLAB interface is compatible with the following devices: Refa Extended, Refa, Mobita, Mobi, Porti and Saga.

Download the interface for MATLAB


OpenViBE is a software platform dedicated to designing, testing and using brain-computer interfaces. OpenViBE is open source software, free and compatible with TMSi amplifiers!

More info about OpenViBE

The Refa comes with:

• The Refa amplifier with 32 or 64 unipolar (ExG), 4 bipolar (BIP) and 4 auxiliary (AUX) input channels
• Suitcase
• Optical fiber 10m
• Fiber to USB converter (Fusbi)
• USB Cable (1.8 m)
• Drivers for Windows
• Patient ground cables (1.5 m)
• Patient ground wristbands
• User Manual

Different accessories are available to complete your set-up. We have everything you need to get started.


For EEG-recordings we offer the following accessories:

• 3 EEG head caps available with 24, 32 or 64 electrodes
Sizes: S, M and L
• EBA Multiconnector
• Electrode gel and application syringe



For HD-EMG recordings we offer the following accessories:

• HD-EMG Cable
• EBA Multiconnector
• HD-EMG grids in various shapes and sizes
• Double sided adhesives for HD-EMG Grids
• Electrode gel and skin preparation gel


To extend your research the Refa can accommodate up to 4 sensors.

Available sensors:
• 3D accelerometer
• Respiration sensor
• Temperature
• Goniometers
• Pressure sensor
• Footswitches
• AUX Break Out Box



These leads are compatible with the unipolar inputs of the Refa.

• Snap electrode 1.5m
• Snap electrode 3.0m
• Ag/AgCl cup electrode 1.5m
• Ag/AgCl earclip electrode 2m
• Micro electrode 1.5m
• Fine wire electrode
• Water electrode 1.5m
• Pin electrode for EEG/NIRS
• EEG Ring electrode
• Bipolar cables for EOG, EEG, ECG and EMG

• Optical fiber: 10, 20, 70 meters
• Power Supply
• Patient ground cable
• Patient ground wristband

Other accessories



The Refa has been used in a wide range of applications for over a decade. We are very proud to see that our equipment is being used in many publications. You can find some of the literature over here!

Tamborska, A. et al. ‘Non-invasive measurement of fasciculation frequency demonstrates diagnostic accuracy in amyotrophic lateral sclerosis’, Brain Communications.

Bashford, J. et al. ‘Preprocessing surface EMG data removes voluntary muscle activity and enhances SPiQE fasciculation analysis’, Clinical Neurophysiology.

Gogeascoechea, A. et al. ‘Interfacing with alpha motor neurons in spinal cord injury patients receiving trans-spinal electrical stimulation’, Frontiers in Neurology.

Wolterink, G. et al. ‘Development of Soft sEMG Sensing Structures Using 3D-Printing Technologies’, Sensors.

Xu, Y. et al. ‘Relevance of spectral peaks in electromyographic recordings during force-modulated vibration exercise’, 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society.

Zhu, M. et al. ‘The Effects of Electrode Locations on Silent Speech Recognition using High-Density sEMG’, IEEE International Workshop on Metrology for Industry 4.0 & IoT.

Nizamis, K. et al. ‘Characterization of Forearm Muscle Activation in Duchenne Muscular Dystrophy via High-Density Electromyography: A Case Study on the Implications for Myoelectric Control’, Frontiers in Neurology.

Xu, L. et al. ‘Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography’, Sensors.

Huang, Y. et al. ‘Dynamic changes in rhythmic and arrhythmic neural signatures in the subthalamic nucleus induced by anaesthesia and tracheal intubation’, British Journal of Anaesthesia.

Nikolin, S. et al. ‘Assessing neurophysiological changes associated with combined transcranial direct current stimulation and cognitive‐emotional training for treatment‐resistant depression’, European Journal of Neuroscience.

Bashford, J. A. et al. ‘The rise and fall of fasciculations in amyotrophic lateral sclerosis’, Brain Communications.

Yao, B. et al. ‘The influence of common component on myoelectric pattern recognition’, Journal of International Medical Research.

Bashford, J. et al. ‘Fasciculations demonstrate daytime consistency in amyotrophic lateral sclerosis’, Muscle & Nerve.

Alizadehsaravi, L. et al. ‘Modulation of soleus muscle H reflexes and ankle muscle co contraction with surface compliance during unipedal balancing in young and older adults’, Experimental Brain Research.

Steven Waterstone, T. et al. ‘Functional Connectivity Analysis on Resting-State Electroencephalography Signals Following Chiropractic Spinal Manipulation in Stroke Patients’, Brain Sciences.

Nikolin, S. et al. ‘EEG Correlates of Affective Processing in Major Depressive Disorder’, MedRxiv.



Bashford, J. et al. ‘SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis’, Clinical Neurophysiology.

Galashan, D. et al. ‘Top-down feature-based cueing modulates conflict-specific ERP components in a Stroop-like task with equiprobable conditions’, BioRxiv.

Sanchez, M. et al. ‘Methodological Design for Integration of Human EEG Data with Behavioral Analyses into Human-Human/Robot Interactions in a Real-World Context’, International Conference on Innovative Computing, Information and Control.

Lum, J. A. G. et al. ‘Visuospatial sequence learning on the serial reaction time task modulates the P1 event‐related potential’, Psychophysiology.

Ning, Y. et al. ‘Improve computational efficiency and estimation accuracy of multi-channel surface EMG decomposition via dimensionality reduction’, Computers in Biology and Medicine.

Karellas, A. M. et al. ‘The Influence of Subclinical Neck Pain on Neurophysiological and Behavioral Measures of Multisensory Integration’, Brain Sciences.

Czeszumski, A. et al. ‘The social situation affects how we process feedback about our actions’, Frontiers in Psychology.

Esch, L. et al. ‘MNE: Software for Acquiring, Processing, and Visualizing MEG/EEG Data’, Magnetoencephalography: From Signals to Dynamic Cortical Networks.

Wolff, M. J. et al. ‘Impulse responses reveal unimodal and bimodal access to visual and auditory working memory’, bioRxiv.

Reiss, S. et al. ‘Strength of socio-political attitudes moderates electrophysiological responses to perceptual anomalies’, PLOS ONE.

Bertelsen, A. R. et al. ‘Generic dry-contact ear-EEG’, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Van Beek, N. et al. ‘Single finger movements in the aging hand: changes in finger independence, muscle activation patterns and tendon displacement in older adults’, Experimental Brain Research.

Zhu, M. et al. ‘Comparison of English and Chinese Speech Recognition Using High-Density Electromyography’, 13th International Conference on Sensing Technology.

Navid, M. S. et al. ‘The Effects of Filter’s Class, Cutoff Frequencies, and Independent Component Analysis on the Amplitude of Somatosensory Evoked Potentials Recorded from Healthy Volunteers’, Sensors.

Fu, L. et al. ‘Corticospinal excitability modulation by pairing peripheral nerve stimulation with cortical states of movement initiation’, The Journal of Physiology.

Zhu, M. et al. ‘Contraction Patterns of Facial and Neck Muscles in Speaking Tasks Using High-Density Electromyography’, 13th International Conference on Sensing Technology.

Solis-Escalante, T. et al. ‘Cortical dynamics during preparation and execution of reactive balance responses with distinct postural demands’, NeuroImage.

Afsharipour, B. et al. ‘Variations of Tendon Tap Force Threshold needed to Evoke Surface Electromyogram Responses after Botulinum Toxin Injection in Chronic Stroke Survivors’, 9th International IEEE/EMBS Conference on Neural Engineering.

Zhuang, J. et al. ‘Comparison of Contributions between Facial and Neck Muscles for Speech Recognition Using High-Density surface Electromyography’, IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications.

Tam, S. et al. ‘A wearable wireless armband sensor for high-density surface electromyography recording’, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Klackl, J. and Jonas, E. ‘Effects of mortality salience on physiological arousal’, Frontiers in Psychology.

McCracken, H. S. et al. ‘Audiovisual multisensory integration and evoked potentials in young adults with and without Attention-Deficit/Hyperactivity Disorder’, Frontiers in Human Neuroscience.

Waasdorp, R. et al. ‘Tracking electromechanical muscle dynamics using ultrafast ultrasound and high-density EMG’, IEEE International Ultrasonics Symposium.

Haveman, M. E. et al. ‘Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography’, Critical Care.

Zandvoort, C. S. et al. ‘The human sensorimotor cortex fosters muscle synergies through cortico-synergy coherence’, Neuroimage.

Zhang, C. et al. ‘Global innervation zone identification with high-density surface electromyography’, IEEE Transactions on Biomedical Engineering.

Saes, M. et al. ‘How does upper extremity Fugl-Meyer motor score relate to resting-state EEG in chronic stroke? A power spectral density analysis’, Clinical Neurophysiology.

Zhang, C. et al. ‘Three dimensional innervation zone imaging in spastic muscles of stroke survivors’, Journal of neural engineering.

Jiang, N., Xue, J. and Li, G. ‘Assessment of Lumbar Muscles Coordinated Activity Based on High-Density Surface Electromyography: A Pilot Study’, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Bourdon, E., Graham, R. B. and van Diëen, J. ‘A comparison of methods to quantify control of the spine’, Journal of Biomechanics.

Kappel, S. L., Makeig, S. and Kidmose, P. ‘Ear-EEG forward models: Improved head-models for ear-EEG’, Frontiers in Neuroscience.

Bose, R. et al. ‘Role of Cross-Frequency Coupling in the Frontal and Parieto-Occipital Subnetwork during Creative Ideation’, 9th International IEEE/EMBS Conference on Neural Engineering.

Bauer, P. R. et al. ’Long-interval intracortical inhibition as biomarker for epilepsy: A transcranial magnetic stimulation study’, Brain.

Wagner, L. et al. ‘The Cochlear Implant EEG Artifact Recorded From an Artificial Brain for Complex Acoustic Stimuli’, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

van den Heuvel, M. R. C. et al. ‘Incongruent visual feedback during a postural task enhances cortical alpha and beta modulation in patients with Parkinson’s disease’, Clinical Neurophysiology.

Bose, R. et al. ‘A Multilayer Network Approach for Studying Creative Ideation from EEG’, International Conference on Brain Informatics.

Darvishi, S. et al. ‘Reaction time predicts brain–computer interface aptitude’, IEEE Journal of Translational Engineering in Health and Medicine.

van Dieën, J. H. et al. ‘Sensory contributions to stabilization of trunk posture in the sagittal plane’, Journal of Biomechanics.

Afsharipour, B. et al. ‘Effect of Botulinum Toxin Injections on Stretch Reflex Responses of Spastic Elbow Flexors in Hemispheric Stroke Survivors: Case Study’, International Conference on NeuroRehabilitation.

Afsharipour, B. et al. ‘Effect of Botulinum Toxin on the Spatial Distribution of Biceps Brachii EMG Activity Using a Grid of Surface Electrodes: A Case Study’, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

van Beek, N. et al. ‘Activity patterns of extrinsic finger flexors and extensors during movements of instructed and non-instructed fingers’, Journal of Electromyography and Kinesiology.

Jochumsen, M. et al. ‘Investigation of optimal afferent feedback modality for inducing neural plasticity with a self-paced brain-computer interface’, Sensors.

Mirakhorlo, M., Maas, H. and Veeger, H. E. J. ‘Increased enslaving in elderly is associated with changes in neural control of the extrinsic finger muscles’, Experimental Brain Research.

Kumagai, Y., Matsui, R. and Tanaka, T. ‘Music Familiarity Affects EEG Entrainment When Little Attention Is Paid’, Frontiers in Human Neuroscience.

Faiman, I., Pizzamiglio, S. and Turner, D. L. ‘Resting-state functional connectivity predicts the ability to adapt arm reaching in a robot-mediated force field’, Neuroimage.

Zhu, M. et al. ‘Contraction Patterns of Neck Muscles during Phonating by High-Density Surface Electromyography’, IEEE International Conference on Cyborg and Bionic Systems.

Hu, T., Kuehn, J. and Haddadin, S. ‘Identification of Human Shoulder-Arm Kinematic and Muscular Synergies During Daily-Life Manipulation Tasks’, 7th IEEE International Conference on Biomedical Robotics and Biomechatronics.

Zhu, M. et al. ‘Using Muscle Synergy to Evaluate the Neck Muscular Activities during Normal Swallowing’, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Hu, T. et al. ‘Learning and Identification of human upper-limb muscle synergies in daily-life tasks with autoencoders’, OTWorld Congress 2018.

Nikolin, S. et al. ‘Effects of TDCS dosage on working memory in healthy participants’, Brain Stimulation.

Esch, L. et al. ‘MNE Scan: Software for real-time processing of electrophysiological data’, Journal of Neuroscience Methods.

He, J. et al. ‘Multi-channel sEMG decomposition based on improved linear minimum mean square error’, International Conference on Biological Information and Biomedical Engineering.

van den Berg, B. and Buitenweg, J. R. ‘Analysis of nociceptive evoked potentials during multi-stimulus experiments using linear mixed models’, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Desmyttere, G. et al. ‘Effect of the phase of force production on corticomuscular coherence with agonist and antagonist muscles’, European Journal of Neuroscience.

Samuel, O. W. et al. ‘Pattern recognition of electromyography signals based on novel time domain features for amputees’ limb motion classification’, Computers & Electrical Engineering.

Kalogianni, K. et al. ‘Spatial resolution for EEG source reconstruction—A simulation study on SEPs’, Journal of Neuroscience Methods.


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