BN5209/BN6209 Neurosensors and Signal Processing / Neurotechnology Semester 2, 2015/2016
SCHEDULE
Lecture Time:
- Tuesday: 3 pm – 6 pm (EA-06-03)
Syllabus
Note: Information contained in this syllabus may be subject to change.
Week | Topic |
1 Jan12 |
Intro to the Course (NT) Intro to Neurotechnology (NT) |
2 Jan19 |
Introduction of BioSignal Processing (HR) L1-CFT; L2-Stochastic Process/R.V./Moments/PSD |
3 Jan26 |
Neural recording methods: Neural circuits, amplifiers, telemetry, stimulation (NT) |
4 Feb2 |
Prepare Student Seminars – paper selection Time-Frequency-Spatial Analysis STFT (HR) |
5 Feb9 (CNY) |
Holidays |
6 Feb16 |
Neural signals (clinical applications)- EEG, evoked potentials (HR) Lab tutorial for Project I: Neural Signals and Analysis |
Recess | Feb22 |
7 Mar1 |
Multiple Dimensional Signal Processing (HR) Lab Project II: Application in neural systems Student Reading Seminars (HR) |
8 Mar8 |
Neuro Diagnostic and Therapeutic Devices by NT |
9 Mar15 |
Brain machine interfaces (NT) EEG/ECoG |
10 Mar22 |
Neuromorphic Engineering – Brain Inspired Robotics by SK |
11 Mar29 |
Neuroimaging and Image Processing (HR) Neuroimaging fMRI (HR) |
12 Apr5 |
Advanced Neurosignal Processing / Neurosurgical systems (HR) |
13 Apr12 (makeup) |
Project Reports (due before final) & presentations (HR, NT) |
Course Projects
1. EEG for brain state monitoring
2. EEG/EMG Feature Identification Extension
AIMS & OBJECTIVES
This module teaches students the advanced neuroengineering principles ranging from basic neuroscience introduction to neurosensing technology as well as advanced signal processing techniques. Major topics include: introduction to neurosciences, neural recording methods, neural circuits, amplifiers, telemetry, stimulation, sensors for measuring the electric field and magnetic field of the brain in relation to brain activities, digitization of brain activities, neural signal processing, brain machine interfaces, neurosurgical systems and applications of neural interfaces. The module is designed for students at Master and PhD levels in Engineering, Science and Medicine.
PREREQUISITES
Basic probability
Basic circuits
Linear algebra (matrix/vector)
Matlab or other programming
Recommended Textbooks: Neural Engineering, Edited by Bin He
Seminar papers
TEACHING MODES
The majority of the course will be in lecture-tutorial format. Some advanced topics will be in the formats of seminar and research presentations.
ASSESSMENT
Take Home Tests (5 for 50%)
Labs/Projects Reports + Presentations (20%)
Seminars (10%)
Take Home Final Exam(20%)