09:00 – 10:00 | Inaugural Talk by Prof. Srinivasa Chakravarthy 

10.00-11.00 | Introduction to EEG and system setup:

• Overview of EEG: Principles and Applications
• Understanding the 10–20 montage,
  cap fitting and impedance check demonstration

11:00 – 12:00 | EEG data collection:

• Resting state Recording: eyes open/closed 

12:00 – 13:00 | EEG pre-processing:

• Signal cleaning using Notch and Bandpass filter
• Artifact removal: Independent Component Analysis (ICA) using MNE-Python

13:00 - 14:00 | Lunch

14:00 – 17:15 | Introduction to ML and DL for EEG:

• Feature extraction: Power Spectral Density (PSD), Phase Lag Index (PLI)
• Models: SVM, CNN, LSTM
• Jupyter pipeline demo: walkthrough of an end-to-end classification task

17:15 – 17:30 | Wrap-Up & Q&A

9:00-11:00 | Motor Imagery Theory and Lab:

• Guided lab session using L/R hand imagination tasks
• Classification of Motor Imagery data offline

11:00-13:00 | Visual Imagery Theory and Lab:

• Explore how imagined objects or scenes are reflected in EEG patterns
• Signal analysis and classification approaches

13:00-14:00 | Lunch Break

14:00-15:30 | Epilepsy and Seizure Detection:

• Introduction to BONN EEG Dataset
  Basics of spike detection and seizure event annotation

15:30-17:15 | Lab: Seizure Classification

• Comparative modelling using Random Forest and CNN architectures

17:15 – 17:30 | Wrap-Up & Q&A

9:00-11:00 | Neurodegenerative Disorder Detection (Parkinson's vs Healthy); Extract Relevant EEG markers, Build and Evaluate ML Classifier

11:00-13.00 | Neurodegenerative Disorder Detection (Alzheimer's vs Healthy); Extract Relevant EEG markers, Build and
Evaluate ML Classifier

13:00-14:00 | Lunch Break

14:00-17:15 | Talks by Faculty & Neurotechnology Experts

17:15 – 17:30 | Closing ceremony