Katarzyna J. Cieslak-BLINOWSKA

Ph.D , Sc.D. Professor

Field of research

Analysis and modeling of biological signals in particular electrical brain activity - electroencefalogram (EEG) and electrocorticogram (EcoG), also otoacoustic emissions (OAE)

• multivariate parametric models of EEG, ECoG applied for:

  • finding causal dependencies between brain electrical activity in different structures and assessment of the dynamic propagation of brain signals (Determination of information flow - Directed Transfer Function).

• Animations of EEG activity transmition:

  • Propagation of gamma and beta activity during real and imaginary motor task

  • Propagation of electrocortical activity during fist clenching

  • Propagation of electrocortical activity during CAT experiment

  • Propagation of electrocortical activity during cognitive experiment

• time-frequency methods of signal analysis in particular adaptive approximations by Matching Pursuit (Time-frequency signal processing intro) applied for:

  • parametrisation of EEG, ECoG and evoked potentials and finding their time-frequency representation,

• analysis of signals of otoacoustic emissions, in particular determination of their resonant modes.

• Lectures on: Bioelectric Phenomena and Biocybernetics

• Promoted: more than 50 MSc and 10 Ph D

• over 180 publications

• Expert-evaluator of the European Commission, Swedish Research Council, Spanish Ministry of Health, Marsden Fund of New Zealand, Qatar National Fund

My recent book: Practical Biomedical Signal Analysis Using MATLAB®

Katarzyna J. Blinowska, Jarosław Żygierewicz,

Practical Biomedical Signal Analysis Using MATLAB® assists readers in choosing the appropriate methods for solving concrete problems. It first describes in simple terms various methods of signal analysi.s    The next
sections indicate which methods are the most appropriate for the given signal in a particular context. The authors cover both basic and advanced methods and use M ATLAB®to discuss applications.

• Provides an introductory approach to biomedical signal analysis
  
• Discusses how and when to apply a wide range of methods
  
• Refers to MATLAB routinesand Toolboxes
  
• Outlines the advantages and disadvantages of the methods in the context of applications

• Introductory concepts: linear time invariant systems, duality of time and frequency domain, uncertainty principle, hypothesis testing, surrogate data techniques.

• Single channel methods:
  • filters, hidden Markov model, Kalman filter;
  • stationary signals: Fourier transform and parametric autoregressive model;
  • non-stationary signals: time-frequency distributions, wavelet transform, matching pursuit;
  • non-linear methods, influence of noise.
• Multiple channel methods:
  • multivariate autoregressive model, Granger causality, directed transfer function (DTF), partial directed coherence (PDC),
  • principle component analysis (PCA), independent component analysis (ICA),
  • influence of volume conduction and problems with bivariate methods for multivariate systems.
• Application:
  • brain electric and magnetic signals: EEG, MEG, iEEG, ERP;
  • heart signals: ECG, MCG, HRV, fECG, fMCG;
  • muscle (EMG);
  • acoustic signals: phonocardiograms and otoacoustic emissions.

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  kjbli@fuw.edu.pl