If you analyze EEG, MEG, or LFP data
Analyzing Neural Time Series Data
A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including MEG, EEG, and LFP recordings.
Signal processing & neural time series
Filtering, Fourier, and multivariate methods for real time series.
Time series are everywhere, from brain recordings to sensor data. These resources cover the core toolkit (filtering, the Fourier transform, convolution, and multivariate source separation) with a special focus on electrophysiology.
Choose your starting point
Different ways into the same material. Any one of them is a complete start.
If you want the core methods, applied
Solved signal processing problems
Applied signal-processing methods, including filtering, denoising, convolution, resampling, interpolation, and outlier detection.
If you want to really understand Fourier
Understand the Fourier transform
Theoretical and computational bases of the Fourier transform, and its use in digital signal processing, data analysis, and image filtering.
Everything on signal processing & neural time series
Courses, books, code, and free material in one list.
Essentials of MATLAB for neuroscience
MATLAB programming for neuroscience data analysis. Each of five modules covers data processing, analysis, and visualization skills that neuroscientists often need.
Analyzing Neural Time Series Data
A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including MEG, EEG, and LFP recordings.
Analyzing neural time series, lecture series
The lecture series that accompanies Analyzing Neural Time Series Data.
Solved challenges in neural time series analysis
Video lectures that supplement Analyzing Neural Time Series Data, with lots of exercises and fully explained solutions.
Solved signal processing problems
Applied signal-processing methods, including filtering, denoising, convolution, resampling, interpolation, and outlier detection.
Understand the Fourier transform
Theoretical and computational bases of the Fourier transform, and its use in digital signal processing, data analysis, and image filtering.
PCA and multivariate signal processing
Theory and implementation of dimension-reduction and source-separation methods for multivariate data, with sample data and a 3-hour linear algebra crash course.
All the signal processing code
Companion code for the courses and books on this page, ready to run and modify.
Nothing in this format yet.