Unlike traditional textbooks that separate theory from code, Cohen integrates both. Each chapter explains a core signal processing technique (e.g., Fourier analysis, convolution, time-frequency decomposition, phase-amplitude coupling, and connectivity measures) followed by worked examples in MATLAB (with Python equivalents often available via online supplements). The emphasis is on understanding what the analysis actually does to neural data, avoiding black-box usage of toolboxes.
Most signal processing books are either too abstract (heavy on proofs) or too cookbook (no intuition). Cohen strikes a rare balance: you will learn why a Morlet wavelet is complex, what the analytic signal represents, and how to avoid common pitfalls like edge artifacts or spectral leakage. The writing is conversational, often humorous, and deeply pedagogical. Unlike traditional textbooks that separate theory from code,
Cohen, M. X. (2014). Analyzing neural time series data: Theory and practice . MIT Press. If you analyze EEG/MEG/LFP data and want to truly understand what your analysis pipeline does—and avoid hidden mistakes—this book is essential. Access it legally through your university library or a purchased ebook, then use the freely available code to work through the examples. Most signal processing books are either too abstract
Overview
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Analyzing Neural Time Series Data: Theory and Practice is a definitive, hands-on guide for anyone working with electroencephalography (EEG), magnetoencephalography (MEG), local field potentials (LFP), or electrocorticography (ECoG). Written by computational neuroscientist Mike X Cohen, the book bridges the gap between abstract mathematical concepts and practical implementation—making it invaluable for students, postdocs, and experienced researchers alike.
