BM4071 Brain Computer Interface Syllabus:

BM4071 Brain Computer Interface Syllabus – Anna University PG Syllabus Regulation 2021

COURSE OBJECTIVES:

The objective of this course is to enable the student to
 Understand the basic concepts of brain computer interface.
 Explore the various signal acquisition methods.
 Understand the signal processing methods used in BCI.
 Understand the various machine learning methods of BCI.
 Learn the various applications of BCI.

UNIT I INTRODUCTION TO BCI

Introduction – Brain structure and function, Brain Computer Interface Types – Synchronous and Asynchronous -Invasive BCI -Partially Invasive BCI – Non Invasive BCI, Structure of BCI System, BCI Monitoring Hardware, EEG, MEG, fMRI.

UNIT II BRAIN ACTIVATION

Brain activation patterns – Oscillatory potential and ERD, Slow cortical potentials, Movement related potentials-Mu rhythms, motor imagery, Stimulus related potentials – Visual Evoked Potentials – P300 and Auditory Evoked Potentials.

UNIT III FEATURE EXTRACTION METHODS

Data Processing – Spike sorting, Frequency domain analysis, Wavelet analysis, Time domain analysis, Spatial filtering -Principal Component Analysis (PCA), Independent Component Analysis (ICA), Artefacts reduction, Feature Extraction – Phase synchronization.

UNIT IV MACHINE LEARNING METHODS FOR BCI

Classification techniques –Binary classification, Multiclass Classification, Evaluation of classification performance, Regression – Linear, Polynomial, RBF’s, Support vector machine, Graph theoretical functional connectivity analysis.

UNIT V APPLICATIONS OF BCI

Case Studies – Invasive BCIs: decoding and tracking arm (hand) position, controlling prosthetic devices such as orthotic hands, Cursor and robotic control using multi electrode array implant, Cortical control of muscles via functional electrical stimulation. Ethics of Brain Computer Interfacing.

TOTAL: 45 PERIODS

COURSE OUTCOMES

On successful completion of this course, the student will be able to
CO1:Evaluate concept of BCI.
CO2:Describe the different brain activation signals.
CO3:Select appropriate feature extraction methods.
CO4:Use machine learning algorithms for translation.
CO5:Develop high-fidelity BCI prototypes.

REFERENCE BOOKS:

1. Rajesh P.N. Rao, Brain-Computer Interfacing: An Introduction, Cambridge University Press, 1st Edition, 2013.
2. Ella Hassianien A and Azar A.T Ed, Brain-Computer Interfaces Current Trends an Applications, Springer, 2015.
3. Jonathan Wolpaw and Elizabeth Winter Wolpaw, Brain Computer Interfaces: Principles and practice, Oxford University Press, USA, 1stEdition, 2012.
4. Bernhard Graimann, Brendan Allison and Gert Pfurtscheller, Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction, Springer, 2010
5. Ali Bashashati, Mehrdad Fatourechi, Rabab K Ward and Gary E Birch, A survey of signal Processing algorithms in brain–computer interfaces based on electrical brain signals, Journal of Neural Engineering, Vol.4, 2007, pp.32-57.
6. Arnon Kohen, Biomedical Signal Processing, Vol I and II, CRC Press Inc, Boca Rato, Florida.
7. Bishop C.M., Neural networks for Pattern Recognition, Oxford, Clarendon Press, 1995.
8. Andrew Webb, Statistical Pattern Recognition, Wiley International, 2nd Edition, 2002.