E9 306 : Machine Learning in Neuroscience (2020)

Syllabus: 

1. Signal, image processing and machine learning applications to recent trends in neuroscience research, such as auditory neuroscience – response to multilingual stimuli; brain computer

2. Interface - motor imagery, imagined speech, etc.; EEG analysis in comatose patients – detection of sleep/wake cycle and response to sensory stimuli for rehabilitation; Interrelationships between

3. biological signals – respiration and cardiac activity, speech, and EEG; Connectome and functional connectivity analysis in normal, coma and meditation subjects. 

References:

1.  Rao, Rajesh PN. Brain-computer interfacing: an introduction. Cambridge University Press, 2013.

2.  Sebastian Seung. Connectome: How the brain's wiring makes us who we are. HMH, 2013.

3.  Recent literature. 

 Slides of my lectures:

1. Visualising the Neural Activation During Motor Imagery

2. Neural Encoding of Our Names

3. Basics of Digital Signal Processing