EEG Signal Processing Techniques and Applications

doi:10.3390/books978-3-7258-0082-7

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Keywords

CHB-MIT dataset; deep learning; epilepsy; seizure detection; XLtek EEG; electroencephalography (EEG); epilepsy; long short-term memory (LSTM); theta frequency band; longitudinal bipolar montage (LB); signal processing; classification; sensor fusion; mental workload; n-back; artificial intelligence; feature engineering; EEG; functional connectivity; manipulability; object observation; phase locking value; motor imagery; circulant singular spectrum analysis (CiSSA); common spatial patterns (CSP); time-frequency-spatial features; brain–computer interface; motor imagery; gamification; stroke rehabilitation; frustration; perceived control; performance accommodation mechanisms; game design; neuromarketing; EEG; SMOTE; LSTM; DWT; PSD; electromyography; electroencephalography; satisfaction; subjective response; robot control; EEG; emotion recognition; EEG feature extraction; valence; arousal; pattern recognition; deep learning; depth of anesthesia; electroencephalogram; patient state index; classification; EEG; emotion recognition; prefrontal channels; time and frequency features; cross-domain transfer learning; electroencephalography (EEG); electrocardiography (ECG); convolutional neural network (CNN); seizure prediction; sleep staging; sparse representation classification; brain computer interfaces; neuromarketing; electroencephalography; individual gamma frequency (IGF); auditory steady-state response (ASSR); dry electrodes; electroencephalography; center-out reaching; event-related spectral perturbation (ERSP); event-related desynchronization (ERD); spectral Granger causality; in degree and out degree; ensemble learning; machine learning; EEG; pilot deficiencies; artifact detection; tangent space; EEG preprocessing; heterogeneous data; mental states classification; feature extraction; n/a

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EEG Signal Processing Techniques and Applications

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January 2024

300 pages

ISBN 978-3-7258-0081-0 (Hardback)
ISBN 978-3-7258-0082-7 (PDF)

https://doi.org/10.3390/books978-3-7258-0082-7

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This is a Reprint of the Special Issue EEG Signal Processing Techniques and Applications that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Summary

This Special Issue provides a forum for original high-quality research in Electroencephalography (EEG) signal pre-processing, modelling, and analysis in the time, space, frequency, or time–frequency domains. It focuses particularly on the utilization of machine learning and deep learning techniques. The range of applications covers healthcare, emotion, motor imagery, external stimulation, mental workload, and satisfaction.