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Postgraduate DiplomaHealth, Biomechanics, Wearables & Operational Safety Applications open

Postgraduate Diploma in EMG, Processing and Neuromuscular Control

Online Postgraduate Diploma of 800 h in EMG, Processing and Neuromuscular Control. 8 modules with SENIAM Recommendations (surface EMG), ISEK Standards for EMG data communication and MATLAB. Assigned tutor and enrolment open.

Duration800 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Explain the physiology of the motor unit and the generation of the EMG signal.

02

Acquire surface EMG following the SENIAM recommendations.

03

Filter, rectify and extract features from the EMG signal.

04

Integrate EMG into movement analysis.

05

Apply pattern recognition to classify gestures.

06

Design myoelectric control strategies for prostheses and exoskeletons.

07

Combine EMG and stimulation in rehabilitation protocols.

Profile

Who it's for

  • Biomedical signal processing engineer looking to specialise in EMG, Processing and Neuromuscular Control.
  • Researcher in neuromuscular control looking to specialise in EMG, Processing and Neuromuscular Control.
  • Myoelectric prosthetics engineer looking to specialise in EMG, Processing and Neuromuscular Control.
  • Rehabilitation engineer looking to specialise in EMG, Processing and Neuromuscular Control.
  • Human-machine interface developer looking to specialise in EMG, Processing and Neuromuscular Control.
  • Biomechanics laboratory technician looking to specialise in EMG, Processing and Neuromuscular Control.
  • Graduates in engineering or science looking to enter orthopaedics and prosthetics.
Standards & tools

What you'll work with

  • SENIAM Recommendations (surface EMG)
  • ISEK Standards for EMG data communication
  • IEC 60601-2-40 (electromyographs and evoked potential equipment)
  • IEC 60601-1 (safety of medical electrical equipment)
  • ISO 14971 (risk management)
  • Regulation (EU) 2017/745 (MDR)
Syllabus

8 modules · 800 h

  1. 1.1Motor unit and recruitment
  2. 1.2Muscle action potential
  3. 1.3Muscle fibre types
  4. 1.4Force-length and force-velocity relationships
  5. 1.5Central motor control and reflexes
  6. 1.6Central and peripheral fatigue
  7. 1.7Proprioception
  8. 1.8Neuromuscular plasticity
  9. 1.9Neuromuscular pathologies
  10. 1.10Practice: interpreting motor unit recordings
  1. 2.1Electrodes and bipolar configuration
  2. 2.2SENIAM recommendations
  3. 2.3Skin preparation
  4. 2.4Amplification and CMRR
  5. 2.5Sampling frequency
  6. 2.6High-density EMG
  7. 2.7Wireless EMG
  8. 2.8Artefacts and crosstalk
  9. 2.9Normalisation with MVC
  10. 2.10Laboratory: quadriceps EMG recording
  1. 3.1Band-pass filtering and mains noise
  2. 3.2Rectification and envelope
  3. 3.3RMS and mean rectified value
  4. 3.4Activation onset detection
  5. 3.5Spectral analysis and median frequency
  6. 3.6Wavelets for EMG
  7. 3.7Motor unit decomposition
  8. 3.8Muscle synergies by NMF
  9. 3.9Tools in Python and MATLAB
  10. 3.10Workshop: processing pipeline in Python
  1. 4.1Activation patterns in gait
  2. 4.2Intermuscular coordination
  3. 4.3EMG in sport
  4. 4.4Fatigue in repetitive tasks
  5. 4.5EMG-force relationship
  6. 4.6Synchronisation with kinematics
  7. 4.7EMG in ergonomic assessment
  8. 4.8Clinical interpretation
  9. 4.9Results report
  10. 4.10Case study: muscle activation in the squat
  1. 5.1Feature extraction
  2. 5.2LDA and SVM classifiers
  3. 5.3Neural networks for EMG
  4. 5.4Gesture recognition
  5. 5.5Simultaneous proportional control
  6. 5.6Robustness to electrode changes
  7. 5.7Adaptive training
  8. 5.8Real-time evaluation
  9. 5.9Public datasets
  10. 5.10Laboratory: hand gesture classifier
  1. 6.1Upper-limb myoelectric prostheses
  2. 6.2Targeted muscle reinnervation
  3. 6.3EMG-controlled exoskeletons
  4. 6.4Human-machine interfaces
  5. 6.5Sensory feedback
  6. 6.6Latency and real-time requirements
  7. 6.7Embedded systems
  8. 6.8Safety and regulation
  9. 6.9User experience
  10. 6.10Practice: controlling an actuator with EMG
  1. 7.1Functional electrical stimulation
  2. 7.2EMG biofeedback in rehabilitation
  3. 7.3Neuromuscular control after injury
  4. 7.4Stroke rehabilitation
  5. 7.5Rehabilitation robotics
  6. 7.6Assessment of clinical outcomes
  7. 7.7Clinical trials in rehabilitation
  8. 7.8Telerehabilitation
  9. 7.9Ethics and safety
  10. 7.10Case study: knee biofeedback programme
  1. 8.1Problem definition
  2. 8.2Acquisition protocol
  3. 8.3Signal processing
  4. 8.4Algorithm design
  5. 8.5Real-time implementation
  6. 8.6User evaluation
  7. 8.7Results analysis
  8. 8.8Conclusions
  9. 8.9Report and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

As 100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: the student progresses at their own pace within the intake, which makes it possible to combine study with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB, Python (NeuroKit2), Delsys EMGworks, OpenSim, LabVIEW, in free, academic or evaluation versions. Trials are studied using real surface EMG recording procedures in a movement laboratory, myoelectric prosthesis control on a bench, and rehabilitation protocols with biofeedback. Assessment is continuous per module plus the final integrative project.

Accreditable laboratories(EN ISO/IEC 17025) with rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented Master's Thesis:safety case, test plan, compliance dossier and operating limits.
Industry mentoring:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, ciber-OT, DIH and compliance as core pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Problem definition

Explain the physiology of the motor unit and the generation of the EMG signal.

Acquisition protocol

Acquire surface EMG following the SENIAM recommendations.

Signal processing

Filter, rectify and extract features from the EMG signal.

Algorithm design

Integrate EMG into movement analysis.

Real-time implementation

Apply pattern recognition to classify gestures.

Career opportunities

Where this takes you

Biomedical signal processing engineerResearcher in neuromuscular controlMyoelectric prosthetics engineerRehabilitation engineerHuman-machine interface developerBiomechanics laboratory technician
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient Spanish to follow technical standards documentation.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme consists of 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.

The course uses MATLAB, Python (NeuroKit2), Delsys EMGworks, OpenSim, LabVIEW. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

A SEIUM own diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official qualification from the Ministry.

Yes. Modules passed in a course or diploma in the same area are recognised in the higher programme. Consult the recognition policy when enrolling.

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