Tutorials
Welcome to our Tutorials section — a carefully curated collection of resources designed to guide you through advanced methods used in neural and physiological signal analysis. Dive in and sharpen your analytical toolkit.
Baseline correction
Baseline correction is a common procedure for the analysis of neural time-domain data or time-frequency data. This tutorial describes different types of baseline corrections, potential problems with baseline corrections, and simulations.
Detrended Fluctuation Analysis (DFA)
Variability in neural (or other physiological) signals may carry important information about neural function. Detrended fluctuation analysis approaches the analysis of variablity by quantifing the scaling behavior (or long-term correlation) in time series.
Multi-scale entropy
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).
Linear-nonlinear Poisson model
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.
Decoding from neural phase
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.