Katarzyna J. Cieslak-BLINOWSKA
Ph.D , Sc.D. Professor
Field of research
Analysis and modeling of biological signals in particular electrical
brain activity - electroencephalogram (EEG) and electrocorticogram (EcoG), also
otoacoustic emissions (OAE)
·
multivariate
parametric models of EEG, ECoG applied for:
o 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:
o Propagation of gamma and beta activity during
real and imaginary motor task
o Propagation of electrocortical activity during
fist clenching
o Propagation of electrocortical activity during
CAT experiment
o Propagation of electrocortical activity during
cognitive experiment
o Propagation of EEG activity in WM auditory and visual
experiments
·
time-frequency
methods of signal analysis in particular adaptive approximations by Matching
Pursuit (Time-frequency signal processing intro) applied for:
o parameterization 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
·
Hirsch index h = 23
·
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 analysis. 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 MATLAB® 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 routines and Toolboxes
- Outlines the advantages and disadvantages
of the methods in the context of applications
Contents:
- 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.
