The student should be made to:
Understand the basic concepts of brain computer interface
Study the various signal acquisition methods
Learn about 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 9
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, ECoG, MEG, fMRI.
UNIT II BRAIN ACTIVATION 9
Brain activation patterns – Spikes, 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, Potentials related to cognitive tasks.
UNIT III FEATURE EXTRACTION METHODS 9
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 and coherence
UNIT IV MACHINE LEARNING METHODS FOR BCI
9 Classification techniques –Binary classification, Ensemble classification, Multiclass Classification, Evaluation of classification performance, Regression – Linear, Polynomial, RBF‘s, Perceptron‘s, Multilayer neural networks, Support vector machine, Graph theoretical functional connectivity analysis
UNIT V APPLICATIONS OF BCI 9
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. Noninvasive BCIs:P300 Mind Speller, Visual cognitive BCI, Emotion detection. Ethics of Brain Computer Interfacing.
TOTAL : 45 PERIODS
OUTCOMES: At the end of the course, the student should be able to: Comprehend and appreciate the significance and role of this course in the present contemporary world. Evaluate concept of BCI. Assign functions appropriately to the human and to the machine. Select appropriate feature extraction methods Use machine learning algorithms for translation.
TEXT BOOKS: 1. Rajesh.P.N.Rao, ―Brain-Computer Interfacing: An Introduction‖, Cambridge University Press, First edition, 2013. 2. Jonathan Wolpaw, Elizabeth Winter Wolpaw, ―Brain Computer Interfaces: Principles and practice‖, Oxford University Press, USA, Edition 1, January 2012.
REFERENCES: 1. Ella Hassianien, A &Azar.A.T (Editors), ―Brain-Computer Interfaces Current Trends and Applications‖, Springer, 2015. 2. Bernhard Graimann, Brendan Allison, GertPfurtscheller, “Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction”, Springer, 2010 3. Ali Bashashati, MehrdadFatourechi, Rabab K Ward, 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 4. Arnon Kohen, ―Biomedical Signal Processing‖, Vol I and II, CRC Press Inc, Boca Rato, Florida. 5. Bishop C.M., ―Neural networks for Pattern Recognition‖, Oxford, Clarendon Press, 1995. 6. Andrew Webb, ―Statistical Pattern Recognition‖, Wiley International, Second Edition, 2002.