#stayhome and join our virtual Spring School 5 days long and full of talks and keynotes from international experts and g.tec medical engineering.

April 20-24, 2020



In these workshops, you will learn everything about brain-computer interfaces, and how to conduct BCI experiments and perform EEG recordings. Every workshop can be booked half-day or full-day.

  • Workshop 1: Brain-computer interface
  • Workshop 2: Spike and ECoG recordings
  • Workshop 3: Coma/Consciousness assessment
  • Workshop 4: Passive functional mapping
  • Workshop 5: Stroke rehabilitation


gtec’s lectures are interactive written tutorials for researchers, professors and students to quickly learn how to record EEG signals and how to run a BCI experiments. The lectures are perfectly suited for teaching classes.

  • Lecture 1: The Electroencephalogram (EEG)
  • Lecture 2: The Brain-Computer Interface (BCI)
  • Lecture 3: The Electrocardiogram (ECG)
  • Lecture 4: The Physio Observer
  • Lecture 5: Evoked Potentials (EP)
  • Lecture 6: g.Nautilus Sports


This lecture teaches how to perform EEG recordings and how to use analysis methods. The reader will learn how to assemble electrodes correctly, set up the recording equipment appropriately. In addition, EEG experiments have to be performed to provide hands-on experience.

  • Average time to perform the lecture: 450 min
  • Pages of lecture: 47
  • Pages of solutions for lecture: 24


  • Learn to assemble electrodes according to the 10-20 system
  • Learn to assemble electrodes with EEG caps and active electrodes
  • Test the impedance of the EEG electrodes
  • Learn how to connect the electrodes to the amplifier and to make monopolar and bipolar recordings
  • Learn how to test the recording setup
  • Learn to recognize alpha and beta rhythms
  • Learn to recognize artifacts in the EEG recording
  • Learn to eliminate artifacts from the EEG recording
  • Investigate the alpha block during a mental task
  • Investigate hemispheric differences during language and spatial processing
  • Learn how hyperventilation affects the EEG
  • Learn the EEG differences of introverts and extraverts


This lecture shows how to analyze EEG signals in offline and online mode and to set up real-time Simulink models for BCI experiments. Furthermore, interactive experiments include EEG recordings for training the computer on subject specific patterns, and for real-time feedback to control a cursor. Several examples e.g. parameter extraction algorithms like bandpower, Hjorth and adaptive autoregressive models (AAR) will be explained. Classification algorithms like linear discriminant analysis (LDA) and neural networks (NN) are also part of the lecture.

  • Average time to perform the lecture: 465 min
  • Pages of lecture: 89
  • Pages of solutions for lecture: 28


  • Learn pre-processing steps for BCI data analysis
  • Calculate the power spectrum and event-related desynchronization of EEG data
  • Extract features of the different EEG channels
  • Train different classifiers to discriminate the features
  • Compare feature extraction and classification algorithms
  • Conduct BCI experiments without feedback to get data for pattern recognition
  • Perform real-time BCI experiments with cursor feedback
  • Learn to write processing batches for fast off-line analysis
  • Extract reactive frequency components out of the EEG data
  • Modify real-time analysis models for optimal performance
  • Train yourself to reach a high BCI classification accuracy


This lecture gives a practical introduction to state-of-the-art ECG recordings. The reader is learns about common tasks of modern ECG analysis and how to practically solve the problems. So, each lesson starts with a theoretical part to provide enough knowledge to solve the tasks.

  • Average time to perform the lecture: 760 min
  • Pages of lecture: 58
  • Pages of solutions for lecture: 71


  • Measure Einthoven-, Goldberger- and Wilson-derivations
  • Perform 12 lead derivations
  • Learn to identify and avoid artifacts in the ECG signals
  • Calculate single beat intervals and amplitudes
  • Perform automatic QRS complex detection
  • Program an off-line and on-line QRS complex detector
  • Analyze tilt table experiments
  • Detect arrhythmias and abnormalities


This lecture introduces the recording and analysis of auditory steady-state responses (ASSRs), the auditory P300 response and brainstem auditory evoked potentials (BAEP). Each of these methods is important in clinical electroencephalography and for Brain Computer Interface (BCI) research.

  • Average time to perform the lecture: 430 min
  • Pages of lecture: 85


  • Configure the auditory stimulator correctly for EPs
  • Record and analyze P300 responses
  • Record and analyze MMN
  • Record and analyze ASSRs
  • Record and analyze BAEPs
  • Record and analyze SSEPs
  • Perform step-by-step the off-line analysis
  • Run analysis batches to evaluate the captured EPs


In this lecture, you learn how to record and evaluate biosignals e.g. ECG, GSR, respiration, EEG and physiological parameters e.g. heart-rate and cognitive indices like the EEG band power. This knowledge is key if you study and develop human-computer interaction and human-robot cooperation.

  • Average time to perform the lecture: 240 min
  • Pages of lecture: 65
  • Pages of solutions for lecture: 19


  • Configure the Physioobserver to run experimental paradigms
  • Perform a circle training experiment
  • Perform high-altitude medicine experiments


This lecture demonstrates how the g.Nautilus wearable EEG headsets can be used during sports exercise. Auditory paradigms similar to the ones presented in the Evoked Potentials lecture demonstrate the stability and low number of artifacts in the EEG data.

  • Average time to perform the lecture: 120 min
  • Pages of lecture: 39
  • Pages of solutions for lecture: 15


  • Configure the g.Nautilus wearable EEG headset to run experimental paradigms
  • Record the EEG while the subject simultaneously performs physical exercise and follows an EP paradigm
  • Calculate the jitter in displaying the auditory stimuli and display the observed EP signals.


g.tec’s biosignal amplifiers and software environment are powerful tools and open a wide range of possible applications. For that reason, training at one of g.tec’s offices in Austria, Barcelona and New York support customers to perfectly use the g.tec’s hardware and software for individual experiments and applications. Training is most effective when customers use their own equipment needed.

  • COURSE 1: Off-line biosignal analysis (EEG, ECG, GSR, respiration) with g.BSanalyze
  • COURSE 2: Measuring biosignal data (EEG, ECG, GSR, respiration, EMG, EOG, ECoG, pulse, SpO2, etc.) with g.USBamp
  • COURSE 3: Running BCI (P300, motor imagery, SSVEP) experiments in real-time
  • COURSE 4: Measuring EPs (BAEP, ASSR, P300, N200,…)
  • COURSE 5: Extending the biosignal analysis with custom software modules under MATLAB/Simulink
  • COURSE 6: Acquiring and analyzing spikes
  • COURSE 7: Running Virtual Reality and physiology experiments
  • COURSE 8: Coma assessment and communication with BCIs
  • COURSE 9: Passive functional mapping with ECoG
  • COURSE 10: Motor rehabilitation with BCIs


  • g.Nautilus fNIRS: Wireless EEG and fNIRS in one device
  • Electroencephalogram (EEG) and Functional Electrical Stimulation (FES) systems
  • Recording bio-signals from different and multiple g.tec amplifiers
  • g.tec’s brain-computer interfaces
  • Eyetracking and EEG recording using Tobii and g.tec’s neurotechnologies
  • Simultaneous recording of multiple bio-signals
  • Optimizing deep brain stimulation (DBS) procedure
  • Combination of TMS with Simultaneous EEG recording



Unicorn Education Kit is a complete teaching solution that is designed for schools and universities. Professors and Teachers make biomedical engineering, neuroscience, neurotechnologies, signal processing or media arts accessible in 1 semester only. The Unicorn Education Kit includes 8 pieces of Unicorn Hybrid Black and the complete Unicorn Suite software environment which serves 40 students in teams of 5.  Students learn quickly how to run a brain-computer interface experiment with P300 or motor imagery. They can even develop their own BCI projects within a few hours.



The BR41N.IO Brain-Computer Interface Designers’ Hackathon teaches current and future developments and unlimited possibilities of Brain-Computer Interfaces in creative or scientific fields. BR41N.IO aims to help students to understand brain-computer interface technology and how artificial intelligence, life science, art, technology and neuroscience come together, leading to new BCI applications. They build and program their own fully functional EEG-based BCI to control external devices, robots or different applications. Participation only requires basic knowledge in Brain-Computer Interfaces, machine learning, programming or design. Hardware and software will the provided at the event.



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