# Course Identification

Fundamentals of Signal Processing for Neuroscience
20203431

## Lecturers and Teaching Assistants

Dr. Gal Ben David
Shir Rachel Maimon

## Course Schedule and Location

2020
First Semester
Monday, 09:15 - 13:00, FGS, Rm 5
04/11/2019

## Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Elective; Regular; 3.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; Elective; Regular; 3.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 3.00 points
Life Sciences (Computational and Systems Biology Track): Lecture; Elective; Regular; 3.00 points

## Comments

Special schedule (not weekly), details- TBA

## Prerequisites

Mathematical knowledge in linear algebra (Vector spaces, Matrices, Complex numbers, Inner Product) and ordinary differential equations (with fixed coefficients).
Working knowledge of Matlab programming (many self-reading materials can we found over the Internet).

30

English

## Attendance and participation

Expected and Recommended

## Grade Type

Numerical (out of 100)

40%
60%

Final assignment

N/A
N/A
-
N/A

3

## Syllabus

Subjects
Motivation and Introduction to Digital Signal Processing
Short Mathematical reminder – Vector spaces and inner products. Orthogonal families. Example: Fourier series
Continuous time Signals and Systems - The Dirac impulse function, System characterization: memory, linearity, time-dependence, causality, Fourier and Laplace analysis, Impulse response and Convolution, Differential equations as an I/O system. Time and Frequency domains
Discrete time Signals and Systems - System characterization: memory, linearity, time-dependence, causality, FIR vs. IIR, Fourier and Z analysis, Unit sample response and Convolution, Difference equations as an I/O. Time and Frequency domains
Sampling – Shannon-Nyquist sampling and  reconstruction. Practical considerations. A/D, D/A, Quantization.
The Discrete Fourier Transform and the FFT algorithms. Spectral Analysis for random and deterministic signals. Windows
Filtering – Concepts and design methods
FIR filters – Linear phase, Window design methods, Park McLellan algorithm.
IIR filters – Families: Butterworth, Chebychev, Elliptic, Bessel. Bilinear transform design method
If time permits – Short Time Fourier Transform, Gabor Transform, Wavelets and Multi resolution analysis, Multi-rate systems

## Learning Outcomes

Upon successful completion of this course students should be able to:
1. Understand and utilize signal representation for continuous time and discrete time signals in both time and frequency domain.
2. Characterize and analyze systems in the time domain (differential and difference equations, impulse response) and in the frequency domain (Fourier, Laplace, Z).
3. Understand signal sampling and reconstruction, practical sampling and quantization.
4. Understand and utilize the discrete Fourier transform and the FFT algorithm. Implement the windowed/averaged transform for spectral analysis of signals.
5. Utilize digital filters and design both finite impulse response and infinite impulse response filters.
6. Gain proficiency with Matlab, and utilize this language to solve problems on a wide-range of signal processing scenarios
7. Optional – be able to understand and utilize advanced time-frequency transforms

## Reading List

B. Porat, A Course in Digital Signal Processing, J. Wiley, 1997

"מבוא לעיבוד ספרתי של אותות" חוברת עזר להרצאות מאת ד. מלאך וש. רז, טכניון 2005

מבוא לעיבוד ספרתי של אותות הרצאות של גל בן דוד, Youtube Technion channel

Signals and Systems (2nd Edition) 2nd Edition
by Alan V. Oppenheim (Author), Alan S. Willsky (Author), with S. Hamid (Author)

EEG Signal Processing 1st Edition
by Saeid Sanei (Author), Jonathon A. Chambers (Author)

Signal Processing for Neuroscientists 2nd Edition
by Wim van Drongelen (Author)

N/A