📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Transformer Models for Non-Invasive BCI & Neural Signal Decoding, from foundations to a certified capstone project.
Signal
• 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
Preprocessing
• 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
Baselines
• 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
Deep Models
• 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
Evaluation
• 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
Earn government-registered certification in Transformer Models for Non-Invasive BCI & Neural Signal Decoding
e-Certificate and e-Marksheet issued on successful completion.
