Course

• LO1: To introduce the concepts of Brain Computer Interfacing (BCI)
• LO2: To impart knowledge about the data acquisition methods used in BCI
• LO3: To enhance the understanding on BCI signal Processing and parameter extraction
• LO4: To enable the knowledge on classification of cognitive task from BCI parameters

• CO1: Ability to understand the basic concepts of EEG and BCI
• CO2: Ability to apply signal processing techniques in BCI
• CO3: Ability to analyse human cognition using BCI parameters
• CO4: Ability to evaluate machine learning methods in BCI applications

Brain activation patterns – Spikes – Oscillatory potential – Event-Related Potentials (ERP) -Mu rhythms – Stimulus related potentials – Visual evoked potentials and auditory evoked potentials – Potentials related to cognitive tasks – Brain computer interface types – Invasive -Non-invasive – Brain signal for BCI signal – EEG – MEG – fNIRS – fMRI. BCI signal processing – Spatial – Temporal – Spatio-temporal filters – Spike sorting – Time and frequency domain analysis – Wavelet analysis – Principal Component Analysis (PCA) -Independent Component Analysis (ICA) – Artifacts reduction – Feature Extraction – Phase synchronization and coherence – ERP Analysis in BCI. Interfacing Brain and Machine – BCI system monitoring hardware – Machine Learning for feature classification – BCI application – Neuro prosthetic devices – Cursor and robotic control using multi electrode array implant – Visual cognitive BCI – Emotion detection.

1. Ella Hassianien, A &Azar.A.T, Brain-Computer Interfaces Current Trends and Applications, Springer, 2015.
2. Rajesh.P, N.Rao, Brain-Computer Interfacing: An Introduction, Cambridge University Press, First edition, 2013.
3. Jonathan Wolpaw, Elizabeth Winter Wolpaw, Brain Computer Interfaces Principles and practice, Oxford University Press, USA, Edition 1, January 2012.
4. Bernhard Graimann, Brendan Allison, GertPfurtscheller, Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction, Springer, 2010