Part 2 (Prof. Giulia Cisotto)

Topics:

• Computational neuroscience: quantitative modelling of the nervous system through equations, simulations and data analysis, alongside fundamental, applied and cognitive neuroscience.
• Three scales: micro (neurons, synapses), meso (populations, rhythms) and macro (brain networks, behaviour), each with its own techniques such as patch clamp, LFP, EEG and fMRI.
• Clinic and applications: research, clinical practice and health-tech form a cycle that yields biomarkers (e.g. qEEG), closed-loop BCIs, in silico experiments and inspiration for AI.
• DSP recap: A/D conversion through sampling (fs = 1/Ts) and quantization.
• Course organization: tentative schedule, Prof. Wriessnegger classes, labs, hands on activity on Brain-Computer Interface, exam rules and projects.

Teams recordings available

Topics:

• Digitization: sampling, aliasing, quantization, and saturation.
• Spectral analysis: signal as a sum of sinusoids (Fourier Transform), FFT, STFT, wavelets, Welch, multitaper and Hilbert.
• EEG origin and acquisition: from source (synaptic activity of pyramidal cells) to EEG signal on the scalp, international 10-20 system standard, different montages, EEG devices.
• Brain rhythms and uses: delta, theta, alpha, beta and gamma bands, 1/f power "law".
• Artefacts and preprocessing: physiological and non-physiological noise, removed by bad-segment rejection or filtering (see next Lab#1), other methods (to be recalled in next classes).

Teams recordings available: