Neural signals and signal processing
NX-421 / 6 crédits
Enseignant(s): Micera Silvestro, Van De Ville Dimitri Nestor Alice
Langue: Anglais
Summary
Understanding, processing, and analysis of signals and images obtained from the central and peripheral nervous system
Content
Understanding neural signals obtained by electrophysiology and imaging techniques requires knowledge both about their origin and the measurement process. This course will introduce the properties of a wide range of neural signals that are used to study the brain in health and disease. The relevance of these signals for applications in fundamental and clinical neuroscience will be made clear. In addition, a broad range of signal processing tools and their implementations will be presented with the specific focus to implement and tailor analysis of these signals, which typically comes as large, noisy, but richly structured datasets. Exercises and lab exercises will provide insights into the analysis of imaging data and electrophysiological neural signals.
Keywords
Electrophysiology, nervous system, neuroimaging, brain mapping, systems-level neuroscience, MRI
Learning Prerequisites
Required courses
Mathematics at the engineering level (i.e., matrix algebra, probability theory)
Basic signal processing, statistics, and machine-learning concepts
Basic knowledge of programming
Learning Outcomes
By the end of the course, the student must be able to:
- Analyze processing steps of neural signals and imaging data
- Assemble a neural data processing pipeline
- Critique suitability of analysis methods
- Interpret results of neural signals analysis
- Explain choice of methodology
Transversal skills
- Use a work methodology appropriate to the task.
- Make an oral presentation.
- Give feedback (critique) in an appropriate fashion.
Teaching methods
Weekly lectures (4h) and weekly exercise session (2h)
Mini-projects during the semester
Expected student activities
Attendance at lectures and exercises
Assessment methods
Attendance and completion of mini-projects with presentations
Written exam
Supervision
| Office hours | Yes |
| Assistants | Yes |
| Forum | Yes |
Resources
Virtual desktop infrastructure (VDI)
Yes
Bibliography
- H. Op de Beeck, C. Nakatani, "Introduction to Human Neuroimaging", Cambridge University Press, 2019.
- N. V. Thakor, "Handbook of Neuroengineering", Springer, 2020.
Ressources en bibliothèque
- Handbook of Neuroengineering / N. V. Thakor
- Introduction to human neuroimaging / Hans Op de Beeck, Chie Nakatani
Moodle Link
Dans les plans d'études
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Neural signals and signal processing
- Cours: 4 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: obligatoire