Neural signals and signal processing

Summary

Understanding, processing, and analysis of signals and images obtained from the central and peripheral nervous system

Content

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 with presentations

Expected student activities

Attendance at lectures and exercises

Assessment methods

Attendance and completion of mini-projects with presentations

Written exam

Supervision

Resources

Virtual desktop infrastructure (VDI)

No

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

• Introduction to human neuroimaging / Hans Op de Beeck, Chie Nakatani
• Handbook of Neuroengineering / N. V. Thakor

Moodle Link

• https://go.epfl.ch/NX-421