Syllabus
Concept of random variables – Stochastic processes – Relations among random variables – correlation, multiple correlation, and partial correlation – Univariate and multivariate Gaussian distributions – Univariate Time Series – Time domain approach – Frequency domain approach. Time series models – AR Models, ARMA Models – Multivariate Time Series – Time domain approach and spectral domain approach – Assessing relations among time series in the spectral domain – Data based estimation versus model based estimation – Principal Component Analysis (PCA) – Signal decorrelation – Independent Component Analysis (ICA).
Data compression of EEG and ECG signals – EMG Source signal separation techniques – EEG signal separation and Pattern Classification – Correlation of Biomedical signals – Evaluating causal relations in biomedical systems – Case studies – ICA based analysis on neurological disorders using EEG – Deep learning-based arrhythmia classification using EEG.