Baseline correction is a common procedure for the analysis of time-domain data or time-frequency data. Find here descriptions of different types of baseline corrections, potential problems with baseline corrections, and simulations.
Variability in neural (or other physiological) signals is not necessarily noise, but may provide important information about complexity and function in the brain. One way to assess complexity in physiological signal is multi-scale entropy (MSE).
Classification and decoding allows distinguishing between different stimulus conditions based on brain activity. Find out more about how one can decode experimental conditions from neural oscillatory phase.
The adaptive exponential integrate-and-fire (aEIF) model is a single neuron spiking model. This tutorial shows example firing patterns that can be modeled using the aEIF model. Matlab code is provided for the aEIF model as well.
LNP models aim to characterize the functional response properties of neurons using stochastic stimuli. This tutorial describes the steps involved to calculate an LNP model and how to predict neural activity for stochastic stimuli.