Biomedical Engineering

1. Biosignals Measurement systems Transducers Amplifiers/Detectors Signal processing (overview of analog filters) 2. Electroencephalographic Signal (EEG) Physiological background of the EEG signal Time and frequency analysis Brain-Computer Interface EEG signal processing chain EEG signal acquisition instrumentation 3. Digital Filters Z-transform FIR filters IIR filters Wiener filter 4. Multivariate Analysis: Principal Component Analysis (PCA) and Independent Component Analysis (ICA) Principal Component Analysis Independent Component Analysis 5. Time-Frequency Spectral Methods Short-Time Fourier Transform (STFT) Spectrogram Instantaneous autocorrelation function Wigner-Ville distribution 6. Wavelet Transform Continuous Wavelet Transform (CWT) Time-frequency characteristics of wavelet transform Discrete Wavelet Transform 7. Parametric Spectral Estimation Methods AR methods and Yule-Walker equation Spectral estimation methods through autoregressive analysis 8. Electrocardiographic Signal (ECG) Physiological background of the ECG signal ECG signal acquisition instrumentation ECG signal processing 9. Electrodermal Activity (EDA) Physiological background of the EDA signal EDA signal acquisition instrumentation EDA signal processing 10. Elements of Machine Learning Overview of ROC curves Linear Discriminant Analysis (LDA) Support Vector Machine (SVM) Elements of clustering

- Fourier series analysis - Mathematical Analysis - Geometry - Elements of stitistics

Slides, recordings and Matlab code Shared google drive folder Textbooks Semmlow and Griffel, Biosignal and medical Imaging Processing, Third Edition, CRC Press, 2014

Lectures will be conducted in-person. Exercises will be carried out in the Matlab environment, through practical examples of processing and classification of real biomedical data. Attendance is strongly recommended.

Multiple choice questions Open-ended questions

1. Biosignals Measurement systems Transducers Amplifiers/Detectors Signal processing (overview of analog filters) 2. Electroencephalographic Signal (EEG) Physiological background of the EEG signal Time and frequency analysis Brain-Computer Interface EEG signal processing chain EEG signal acquisition instrumentation 3. Digital Filters Z-transform FIR filters IIR filters Wiener filter 4. Multivariate Analysis: Principal Component Analysis (PCA) and Independent Component Analysis (ICA) Principal Component Analysis Independent Component Analysis 5. Time-Frequency Spectral Methods Short-Time Fourier Transform (STFT) Spectrogram Instantaneous autocorrelation function Wigner-Ville distribution 6. Wavelet Transform Continuous Wavelet Transform (CWT) Time-frequency characteristics of wavelet transform Discrete Wavelet Transform 7. Parametric Spectral Estimation Methods AR methods and Yule-Walker equation Spectral estimation methods through autoregressive analysis 8. Electrocardiographic Signal (ECG) Physiological background of the ECG signal ECG signal acquisition instrumentation ECG signal processing 9. Electrodermal Activity (EDA) Physiological background of the EDA signal EDA signal acquisition instrumentation EDA signal processing 10. Elements of Machine Learning Overview of ROC curves Linear Discriminant Analysis (LDA) Support Vector Machine (SVM) Elements of clustering

- Fourier series analysis - Mathematical Analysis - Geometry - Elements of stitistics

Slides, recordings and Matlab code Shared google drive folder Textbooks Semmlow and Griffel, Biosignal and medical Imaging Processing, Third Edition, CRC Press, 2014

Lectures will be conducted in-person. Exercises will be carried out in the Matlab environment, through practical examples of processing and classification of real biomedical data. Attendance is strongly recommended.

Multiple choice questions Open-ended questions