Welcome to Litsy!

This book is devoted to the application of advanced signal processing on event-related potentials (ERPs) in the context of electroencephalography (EEG) for the cognitive neuroscience. ERPs are usually produced through averaging single-trials of preprocessed EEG, and then, the interpretation of underlying brain activities is based on the ordinarily averaged EEG. We find that randomly fluctuating activities and artifacts can still present in the averaged EEG data, and that constant brain activities over single trials can overlap with each other in time, frequency and spatial domains. Therefore, before interpretation, it will be beneficial to further separate the averaged EEG into individual brain activities. The book proposes systematic approaches pre-process wavelet transform (WT), independent component analysis (ICA), and nonnegative tensor factorization (NTF) to filter averaged EEG in time, frequency and space domains to sequentially and simultaneously obtain the pure ERP of interest. Software of the proposed approaches will be open-accessed. Contents:IntroductionWavelet Filter Design Based on Frequency Responses for Filtering ERP Data With Duration of One EpochIndividual-Level ICA to Extract the ERP Components from the Averaged EEG DataMulti-Domain Feature of the ERP Extracted by NTF: New Approach for Group-Level Analysis of ERPsAnalysis of Ongoing EEG by NTF During Real-World Music ExperiencesAppendix: Introduction to Basic Knowledge of Mismatch Negativity Readership: Undergraduate, graduate, researchers and professionals in the field of neurology/neuroscience, medical imaging, psychology, biomedical engineering and computer science. Key Features:Advanced signal processing approaches can be applied on averaged EEG to extract ERPs' componentsFiltering ERPs in time, frequency and space domains sequentially and simultaneouslyDemo of ERP data and MATLAB codes are open-access for the advanced signal processing approaches on ERPsKeywords:Event-Related Potentials (ERPs);Digital Filter;Wavelet Filter;Independent Component Analysis;Tensor Decomposition;Nonnegative Tensor Factorization;Time-Frequency Representation