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Principles of Neural Engineering

Degree course Electronic Engineering
Curriculum Curriculum unico
Learnings Orientamento unico
Academic Year 2018/2019
Scientific Disciplinary Sector ING-IND/31
Year First year
Time unit Second semester
Class hours 48
Educational activity Related and integrative training activities

Single group

Objectives To improve knowledge of machine learning, with advanced applications and deep learning approach. EEG signal processing techniques and examples are presented, in particular for brain state classification and BCI. An individual application work is required.
Programme Foundations of Neural Engineering

Instructor: Prof. Francesco Carlo Morabito
Master’s Degree in Electronic Engineering (curriculum Bioelectronics)
Class hours: Thursday & Friday from 11:00 to 13:00
Office hours: Thursday 14:00 to 15:00 (by appointment) - The best way to ask questions is via email
E-mail:; Phone: +39-0965 1692.224

1) Introduction (0.5 CFU)
Overview of Course. Overview of Neural Engineering Applications. Need for a novel perspective in model-based approaches. Description of Exam and Student’s Project.
2) Neural Networks (2.5 CFU)
General properties of neural processing systems. Biological model. Synaptic links and strength. Models of a neuron. McCulloch-Pitts formal neuron. Nonlinearities: sigmoidal, hyperbolic tangent, ReLu activation functions. Network architectures: feedforward and feedback models. Competitive and Self-Organizing models. Knowledge representation. Visualization of processes in Neural Networks.
Learning process. Error-Correction. Widrow-Hopf Rule. Hebbian Learning. Competitive Learning. Supervised and Unsupervised learning. Reinforcement Learning. Statistical Nature of the Learning Process.
Perceptrons. Multilayer Perceptrons. Radial-Basis Function Networks. Recurrent Networks. Self-Organizing Systems. Information-Theoretic Models. Temporal processing. Neurodynamics.
Deep Learning.
3) Electrophysiological Signal Processing (1.5 CFU)
Introduction to EEG. Electric fields of the brain. Neural activities. EEG generation. Brain rhythms. EEG recording and acquisition. Normal vs. abnormal EEG patterns. Mental disorders (Epilepsy, Psychogenic crisis, Creutzfeldt-Jacob disease, Alzheimer’s disease, Depression, Mental states).
Fundamentals of EEG signal processing. Linear and nonlinear modelling. Signal analysis and transformation. Spectral and time-frequency analysis. Dynamical analysis and chaos. Entropic analysis. Different types of complexity.
PCA/ICA and sparse component analysis. Classification of brain states through Neural Networks/SVM. Seizure signal analysis. EEG source localization. LORETA algorithm.
Brain-Computer Interfacing. ERD/ERS.
Multidimensional EEG decomposition.
4) Laboratory Experiments (1 CFU)
Use of Neural Works Professional II/+ code and CAD; Matlab Neural Networks toolbox.
5) Project Organization, Preparation, and Discussion (0.5 CFU)

Journal article Presentation
Students will spend the last two hours of the course reviewing a selected journal paper. Each journal paper will have a designated discussion leader who will review the paper using PowerPoint. The discussion leader(s) must present the work as if he or she were the investigative author.
Student’s Projects
The projects is autonomously developed from each student individually. It will be discussed at the exam.

Simon Haykin, Neural Networks, IEEE Press
Sani-Chambers, EEG Signal Processing, IEEE- Wiley
Neural-Works Professional II/+ Manual.
Additional Materials from the Instructor
Books Textbooks:
Additional Materials from the Instructor
Traditional teaching method Yes
Distance teaching method No
Mandatory attendance No
Written examination evaluation No
Oral examination evaluation Yes
Aptitude test evaluation No
Project evaluation Yes
Internship evaluation No
Evaluation in itinere No
Practice Test No

Further information

No document in this course

Office hours list:

Description News
Office hours by: Francesco Carlo Morabito
Il ricevimento degli studenti per tutti i corsi del docente si tiene, di norma, il mercoledì dalle 11 alle 13. Il docente risponde, di norma, alle richieste degli studenti anche alla fine di ciascun modulo di lezione.
No news posted
No class timetable posted
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