CBM342 Brain Computer Interface and Applications Syllabus:
CBM342 Brain Computer Interface and Applications Syllabus – Anna University Regulation 2021
OBJECTIVES:
The student should be made to:
To understand the basic concepts of brain computer interface
To study the various signal acquisition methods
To study the signal processing methods used in BCI
UNIT I INTRODUCTION TO BCI
Fundamentals of BCI – Structure of BCI system – Classification of BCI – Invasive, Non-invasive and Partially invasive BCI – EEG signal acquisition – Signal Preprocessing – Artifacts removal.
UNIT II ELECTROPHYSIOLOGICAL SOURCES
Sensorimotor activity – Mu rhythm, Movement Related Potentials – Slow Cortical Potentials-P300 – Visual Evoked Potential – Activity of Neural Cells – Multiple Neuromechanisms.
UNIT III FEATURE EXTRACTION METHODS
Time/Space Methods – Fourier Transform, PSD – Wavelets – Parametric Methods – AR,MA,ARMA models – PCA – Linear and Non-Linear Features.
UNIT IV FEATURE TRANSLATION METHODS
Linear Discriminant Analysis – Support Vector Machines – Regression – Vector Quantization– Gaussian Mixture Modeling – Hidden Markov Modeling – Neural Networks.
UNIT V APPLICATIONS OF BCI
Functional restoration using Neuroprosthesis – Functional Electrical Stimulation, Visual Feedback and control – External device control, Case study: Brain actuated control of mobile Robot.
COURSE OUTCOMES:
On successful completion of this course, the student will be able to
CO1: Describe BCI system and its potential applications.
CO2: Analyze event related potentials and sensory motor rhythms.
CO3: Compute features suitable for BCI.
CO4: Design classifier for a BCI system.
CO5: Implement BCI for various applications.
TOTAL PERIODS:45
TEXT BOOKS
1. Bernhard Graimann, Brendan Allison, Gert Pfurtscheller, “Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction”, Springer, 2010
REFERENCES
1. R. Spehlmann, “EEG Primer”, Elsevier Biomedical Press, 1981.
2. Arnon Kohen, “Biomedical Signal Processing”, Vol I and II, CRC Press Inc, Boca Rato, Florida, 1986.
3. Bishop C.M., “Neural Networks for Pattern Recognition”, Oxford, Clarendon Press, 1995.