Analysis and Classification of Neural Signals
Computer Science and Electronic Engineering (School of)
Undergraduate: Level 6
Thursday 05 October 2023
Friday 15 December 2023
08 September 2023
Requisites for this module
CE213 and CE223 and CE225 and CE246
BENGH169 Neural Engineering with Psychology,
BENGH170 Neural Engineering with Psychology (including Placement Year),
BENGH171 Neural Engineering with Psychology (including Year Abroad),
BENGH172 Neural Engineering with Psychology (including Foundation Year),
BSC H167 Neural Technology with Psychology,
BSC H168 Neural Technology with Psychology (including Year Abroad),
BSC H176 Neural Technology with Psychology (including Placement Year)
This module builds on CE246 by providing an in-depth introduction to a wider range of analysis and classification methods required by modern Brain-Computer Interfaces (BCIs) and Peripheral Neural Interfaces (PNIs).
feature reduction and selection,
classification and regression,
Students will also gain practical experience of advanced analysis and classification methods required to operate BCIs/PNIs.
In the Brain-computer Interfaces and Peripheral Neural Interfaces module you will:
1. become familiar with techniques for analysing and classifying neural signals for the operation of a BCI/PNI,
2. understand the benefits and drawbacks of different pre-processing, feature selection, classification and regression methods for BCIs/PNIs.
At the end of the module you will:
1. understand the working principles of the analysis and classification methods used in BCIs and PNIs (both offline and online),
2. be able to select appropriate pre-processing stages, features, classification and regression methods for specific forms of BCI/PNI,
3. be able to carry out analyses of neural signals acquired in a BCI/PNI and to classify them.
1. Advanced pre-processing (re-referencing, temporal and spatial filtering, artefact handling),
2. Feature extraction (temporal features, PCA, ICA, CSP, DFT/FFT, Wavelets, zero crossings, Eigenbrains, auto-regressive models),
3. Feature reduction and selection (mutual information, correlation, greedy/sequential selection, wrapper approach)
4. Cross-validation and performance evaluation (ACC, kappa, ROC, AUC, ITR, ...)
5. Classification (LDA, SVN, standard and logistic Regression, random Forests, Bayes, (C)NN, Clustering)
6. Techniques that are suitable for online (real-time) BCIs/PNIs
7. Performance evaluation of online BCIs/PNIs
The module comprises 2 hours of lectures and 2 hours of practical laboratories or classes each week.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
|Coursework / exam
||Main exam: In-Person, Open Book (Restricted), 120 minutes during Summer (Main Period)
||Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period)
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Module supervisor and teaching staff
Dr Sebastian Halder, email: email@example.com.
Dr Sebastian Halder, Prof Francisco Sepulveda
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Prof Sandra Dudley
London South Bank University
Professor of Communication Systems
Available via Moodle
No lecture recording information available for this module.
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