Description:

This course comprehends/deals methods of biosignal generation, biosignal acquisition and basic parameters of biosignals required for diagnostics. Methods and algorithms for biosignal processing, analysis and evaluation used for biological signals, mainly electrophysiological signals. Preprocessing, filtering, time and frequency analysis. Use of modern spectral analysis methods. Visualisation of results, topographic mapping, method of compressed spectral arrays (CSA). Adaptive segmentation of non-stationary signals is discussed. Application of methods using artificial intelligence. Methods of automated signal classification - supervised/unsupervised, cluster analysis, learning classifier. Artificial neural networks (ANN). Practical application of biosignal processing. Case studies of ANN application on epileptogenic recordings and neural recordings in general. Genetic algorithms and simulated annealing is presented.

Contents:

1. Introduction to biosignal processing. Motivation. Characteristics of basic biosignals: EEG, EOG, EMG, EKG, fMRI. Simultaneous recording. Artefacts. Genesis, source, diagnostic application. Application fields.
2. Fixed and adaptive segmentation. Stationary and non-stationary signals. Basic methods. Multichannel on-line adaptive segmentation. Feature extraction. Parameter settings. Advantages and limitations of the methods. Other segmentation algorithms.
3. Spectral analysis of biosignals - application. Basic methods. Parametric and non-parametric methods. Periodogram, AR model, LDR algorithm. Practical problems of spectral estimation. Cross-spectrum, coherence and phase spectrum. Methods of compressed spectral arrays (CSA). Digital filtering of biosignals, design of IIR and FIR filters.
4. Case studies of biosignal processing in practice.
5. Topographic mapping of electro-physiological activity. Visualisation. Principle of brain mapping. Amplitude and frequency mapping. Interpolation. Iterative method for generating maps. Animation. 3D-spline interpolation. Isopotential mapping. Application in clinical diagnostics. Dynamic mapping. LORETA.
6. Methods of automated classification I.: Unsupervised learning. Metrics. Normalisation. Basic algorithms of cluster analysis. K-means algorithm. Density based methods of classification. Examples of classification on simulated data and on real EEG recordings. Application of fuzzy set approach for increasing homogeneity of the classes. Optimal number of classes. Limits and restrictions of cluster analysis.
7. Methods of automated classification II.: Learning classifiers. Comparison of supervised and unsupervised learning. Examples of application of basic learning algorithms for classification on simulated and real data. Neural networks and methods of artificial intelligence.

Seminar contents:

1. Hidden information extraction methods and reduction of dimension.
2. EEG signal acquisition.
3. Spectrum estimation using parametric methods / ARMA models for EEG prediction.
4. Spectral analysis and synthesis. Digital filtering of the signal.
5. Visualisation of results of spectral analysis, method of compressed spectral arrays (CSA).
6. Amplitude and frequency brain mapping.
7. Learning classifiers, simulated data.

Recommended literature:

Mandatory:
1. SÖRNMO, Leif a Pablo LAGUNA. Bioelectrical signal processing in cardiac and neurological applications. Amsterdam: Elsevier Academic Press, ?2005. xiii, 668 s. ISBN 0-12-437552-9
2. SANEI, Saeid a Jonathon A. CHAMBERS. EEG signal processing. Chichester: Wiley, ?2007. xxii, 289 s. ISBN 978-0-470-02581-9
3. COHEN, Mike X. Analyzing neural time series data: theory and practice. Cambridge, Massachusetts: MIT, ?2014. xv, 578 s. Issues in clinical and cognitive neuropsychology. ISBN 978-0-262-01987-3

Recommended:
4. MALMIVUO, Jaakko. a Robert PLONSEY. Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields. New York: Oxford University Press, 1995. xxii, 482 s., příl. ISBN 0-19-505823-2
5. PRINCIPE, José C., Neil R. EULIANO a W. Curt LEFEBVRE. Neural and adaptive systems: fundamentals through simulations. New York: Wiley, c2000. ISBN 0-471-35167-9.
6. HAYKIN, Simon S. Neural networks and learning machines. 3rd ed. New York: Pearson, c2009. ISBN 978-0-13-147139-9.

Keywords:

Digital signal processing, spectral analysis, signal classification, artificial neural networks, biosignal processing, topographic mapping, brain activity.

Abbreviations used:

Semester:

• W ... winter semester (usually October - February)
• S ... spring semester (usually March - June)
• W,S ... both semesters

Mode of completion of the course:

• A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
• GA ... Graded Assessment (a grade is awarded for this course)
• EX ... Examination (a grade is awarded for this course)
• A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)

Weekly load (hours per week):

• P ... lecture
• C ... seminar
• L ... laboratory
• R ... proseminar
• S ... seminar