g.tec introduces lectures for biosignal recording and analysis. The lectures are divided into a manual which contains the theoretical background, hands-on examples and several tasks to solve. The second part is a manual which contains only the solutions for the tasks.
The lectures allow researchers to get a quick start in the specific field and to perform already state of the art experiments after just a few hours. The lectures are also perfectly suited for teaching because the tasks and the solutions are separated in two manuals.
Lecture 4: Evoked Potentials
The Lecture Evoked Potentials explains the recording and analysis of auditory steady-state responses (ASSRs) and brainstem auditory evoked potentials (BAEP). Both EPs are important in clinical Electroencephalography.
Objectives
- Configure the auditory stimulator correctly for EPs
- Record and analyze ASSRs
- Record and analyze BAEPs
- Perform step-by-step the off-line analysis
- Run analysis batches to generate the EPs
More information could be found here!
| Average time to perform the lecture: | 270 min |
| Pages of lecture: | 34 |
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Lecture 3: The Electrocardiogram
The ECG lecture is intended to give a practical entry to state-of-the-art ECG processing. In the course of 6 lessons, the reader is confronted with common tasks of modern ECG analysis and it is shown how to practically solve the problems. Each lesson starts with a theoretical part to provide enough knowledge to solve the tasks.
Objectives
- 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
- Analyse tilt table experiments
- Detect arrhythmias and abnormalities
| Average time to perform the lecture: | 700 – 760 min |
| Pages of lecture: | 58 |
| Pages of solutions for lecture: | 71 |
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Lecture 2: Brain-Computer Interface
The Brain-Computer Interface (BCI) is a tutorial which introduces the reader into BCI experiments and analysis methods. The reader will learn how to analyze BCI data in off-line and on-line mode and to set up real-time Simulink models for BCI experiments. Experiments will be introduced which can be used to acquire EEG data for training the computer on subject specific patterns and also for real-time feedback in order to control a cursor on the screen. Several examples of 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 subject of the lecture. The reader has to perform several tasks which give a deep insight into state-of-the-art BCI processing steps.
Objectives
- 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
- Contact 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
| Average time to perform the lecture: | 465 min |
| Pages of lecture: | 89 |
| Pages of solutions for lecture: | 28 |
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