EEG Signal Processing Techniques and Applications

© 2022 by the authors; CC BY-NC-ND license

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