Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This intensive 3-day course explores the cutting edge of AI-driven brain–computer interfaces, focusing on Transformer architectures for EEG motor imagery decoding. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This intensive 3-day course explores the cutting edge of AI-driven brain–computer interfaces, focusing on Transformer architectures for EEG motor imagery decoding.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
EEG and What It Measures
• Cortical sources, volume conduction and the spatial resolution limit
• Electrode montages, referencing and impedance in practice
• Frequency bands and the sensorimotor rhythms that motor imagery relies on
Cleaning Neural Data
• Filtering, artefact rejection and ICA for ocular and muscular contamination
• Epoching, baseline correction and the leakage a careless filter introduces
• MNE-Python and public datasets such as BCI Competition IV
Classical Decoding
• Common spatial patterns with a linear classifier as the standing baseline
• Riemannian geometry methods that remain competitive with deep learning
• Why a strong baseline must be reported before any transformer claim
Networks for EEG
• EEGNet and compact convolutional architectures for small datasets
• Attention and transformer variants adapted to short, noisy, low-channel data
• Overfitting when trials number in the hundreds, and the regularisation required
Claims That Survive Scrutiny
• Within-subject, cross-session and cross-subject splits and their very different scores
• Transfer learning and calibration time as the real usability metric
• BCI illiteracy, online against offline performance, and ethical use of neural data
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | CUDA |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Weights & Biases |
Frequently Asked Questions
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