Biomedical Engineering

• Course Introduction – Application examples of biomedical measurement systems, with a focus on passive Brain-Computer Interface systems based on EEG signals, and live demonstration. • Biomedical Measurement System (review) – Analog filters and sampling. • Digital Filters o Z-transform o FIR filters o IIR filters o Optimal filters, with a particular focus on Wiener filters • Multivariate Analysis o Principal Component Analysis (PCA) o Independent Component Analysis (ICA) • Time-Frequency Spectral Methods o Short-Time Fourier Transform (STFT) o Spectrogram o Wigner-Ville Distributions o Instantaneous autocorrelation function o Choi-Williams Distributions • Wavelet Transform o Continuous Wavelet Transform (CWT) o Time-Frequency features of the Wavelet Transform o Discrete Wavelet Transform (DWT) via filter banks • Parametric Spectral Estimation Methods o Autoregressive Methods (review) o Spectral estimation methods based on eigenanalysis • Electrocardiographic (ECG) and Photoplethysmographic (PPG) Signal Processing o Physiology of ECG and PPG signals o Heart Rate (HR) and Heart Rate Variability (HRV) o Time and frequency domain parameters o Lomb-Scargle Periodogram o Pan-Tompkins Algorithm o Application Examples • Electrodermal Activity (EDA) Signal Processing o Physiology of the EDA signal o Methods for extracting Tonic (SCL) and Phasic (SCR) components o Continuous Decomposition Analysis applied to EDA signals o Ledalab Algorithm o Application Examples • Electrooculographic (EOG) Signal Processing o Physiology of the EOG signal o Methods for extracting the Eye Blink Rate (EBR) parameter o Methods for correcting ocular artifacts in EEG signals o Gratton & Coles Regression Algorithm o Application Examples • Supplementary Seminars on the application (or advancement) of some processing methods covered in class. • Practical Examples in MATLAB implementing the analysis and processing methods discussed during the course.

● Geometry/Mathematical Analysis ● Biomedical Data and Signal Processing 1: o Basic concepts of data transformation and related operators (e.g., Correlation, Cross-correlation, Auto-correlation, Convolution) o Fourier Analysis o Non-parametric Spectral Estimation ● Basic Programming Skills and Introductory Knowledge of the MATLAB Environment

• Semmlow and Griffel, Biosignal and Medical Imaging Processing • Teaching material provided by the instructor: • Lecture slides (current and previous year) • Prerequisite review slides from EDSB1 • Lecture recordings (audio with screen sharing) • MATLAB code related to the in-class examples

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

• Semmlow and Griffel, Biosignal and Medical Imaging Processing • Teaching material provided by the instructor: • Lecture slides (current and previous year) • Prerequisite review slides from EDSB1 • Lecture recordings (audio with screen sharing) • MATLAB code related to the in-class examples

Teaching is delivered through traditional lectures, enriched with numerous practical examples in MATLAB, aimed at demonstrating the practical implementation of the signal processing algorithms covered in class. Lectures are recorded (voice + slide screen sharing) and made available to students, along with the teaching materials, including slides and MATLAB code developed step-by-step during the course. Additional seminars are offered, focusing on practical applications of the algorithms (or advanced versions thereof), to provide students with concrete examples of how the developed techniques are used. Live demonstrations of biomedical measurement systems are also included.

• Course Introduction – Application examples of biomedical measurement systems, with a focus on passive Brain-Computer Interface systems based on EEG signals, and live demonstration. • Biomedical Measurement System (review) – Analog filters and sampling. • Digital Filters o Z-transform o FIR filters o IIR filters o Optimal filters, with a particular focus on Wiener filters • Multivariate Analysis o Principal Component Analysis (PCA) o Independent Component Analysis (ICA) • Time-Frequency Spectral Methods o Short-Time Fourier Transform (STFT) o Spectrogram o Wigner-Ville Distributions o Instantaneous autocorrelation function o Choi-Williams Distributions • Wavelet Transform o Continuous Wavelet Transform (CWT) o Time-Frequency features of the Wavelet Transform o Discrete Wavelet Transform (DWT) via filter banks • Parametric Spectral Estimation Methods o Autoregressive Methods (review) o Spectral estimation methods based on eigenanalysis • Electrocardiographic (ECG) and Photoplethysmographic (PPG) Signal Processing o Physiology of ECG and PPG signals o Heart Rate (HR) and Heart Rate Variability (HRV) o Time and frequency domain parameters o Lomb-Scargle Periodogram o Pan-Tompkins Algorithm o Application Examples • Electrodermal Activity (EDA) Signal Processing o Physiology of the EDA signal o Methods for extracting Tonic (SCL) and Phasic (SCR) components o Continuous Decomposition Analysis applied to EDA signals o Ledalab Algorithm o Application Examples • Electrooculographic (EOG) Signal Processing o Physiology of the EOG signal o Methods for extracting the Eye Blink Rate (EBR) parameter o Methods for correcting ocular artifacts in EEG signals o Gratton & Coles Regression Algorithm o Application Examples • Supplementary Seminars on the application (or advancement) of some processing methods covered in class. • Practical Examples in MATLAB implementing the analysis and processing methods discussed during the course.

● Geometry/Mathematical Analysis ● Biomedical Data and Signal Processing 1: o Basic concepts of data transformation and related operators (e.g., Correlation, Cross-correlation, Auto-correlation, Convolution) o Fourier Analysis o Non-parametric Spectral Estimation ● Basic Programming Skills and Introductory Knowledge of the MATLAB Environment

• Semmlow and Griffel, Biosignal and Medical Imaging Processing • Teaching material provided by the instructor: • Lecture slides (current and previous year) • Prerequisite review slides from EDSB1 • Lecture recordings (audio with screen sharing) • MATLAB code related to the in-class examples

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

• Semmlow and Griffel, Biosignal and Medical Imaging Processing • Teaching material provided by the instructor: • Lecture slides (current and previous year) • Prerequisite review slides from EDSB1 • Lecture recordings (audio with screen sharing) • MATLAB code related to the in-class examples

Teaching is delivered through traditional lectures, enriched with numerous practical examples in MATLAB, aimed at demonstrating the practical implementation of the signal processing algorithms covered in class. Lectures are recorded (voice + slide screen sharing) and made available to students, along with the teaching materials, including slides and MATLAB code developed step-by-step during the course. Additional seminars are offered, focusing on practical applications of the algorithms (or advanced versions thereof), to provide students with concrete examples of how the developed techniques are used. Live demonstrations of biomedical measurement systems are also included.