Lecture: Principles of neural signals
Generation of an action potential; Recording electrophysiological data in humans; examples of EMG, EEG, intracortical data, and audio signals.

Lecture: Speech signals and processing

Lecture: ECG signal and processing

Lecture: EMG signal and processing
Association between EMG and voluntary force; Interpretation of multi-channel EMG signals; Neuronal encoding of behaviour; motor unit physiology in humans; motoneuron properties, longitudinal assessment of neuronal function; voice biomarkers for diagnosis and treatment of neurological disease; automatic speech recognition; speech pathology.

Lecture: MATLAB / Python practical coursework
Biosignal processing; data with high temporal resolution, identification of individual neurons, associations between neuronal discharge times and behaviour; control of prosthetic devices from neural signals. Extraction of neural information from speech and ECG signals, electrophysiological signals, and data mining and neural network model training on these signals.
Practical work: literature overview on these signals and a critical analysis on how to merge these signals for an artificial intelligent system that can detect and prevent neural and/or muscular pathologies.