EEG Signal Processing: Feature extraction, selection and classification methods
- Length: 296 pages
- Edition: 1
- Language: English
- Publisher: The Institution of Engineering and Technology
- Publication Date: 2019-05-01
- ISBN-10: 1785613707
- ISBN-13: 9781785613708
- Sales Rank: #0 (See Top 100 Books)
Electroencephalography (EEG) is an electrophysiological monitoring method used to record the brain activity in brain-computer interface (BCI) systems. It records the electrical activity of the brain, is typically non-invasive with electrodes placed along the scalp, requires relatively simple and inexpensive equipment, and is easier to use than other methods.
EEG-based BCI methods provide modest speed and accuracy which is why multichannel systems and proper signal processing methods are used for feature extraction, feature selection and feature classification to discriminate among several mental tasks. This edited book presents state of the art aspects of EEG signal processing methods, with an emphasis on advanced strategies, case studies, clinical practices and applications such as EEG for meditation, auditory selective attention, sleep apnoea; person authentication; handedness detection, Parkinson’s disease, motor imagery, smart air travel support and brain signal classification.
Cover Title Copyright Contents Foreword 1 EEG extraction for meditation 1.1 Introduction 1.2 EEG signal processing 1.2.1 Data collection 1.2.2 Data preparation 1.2.3 Filters 1.2.4 Principal component analysis 1.2.5 Independent component analysis 1.2.6 Segmentation and manual artifact deletion 1.3 Feature extraction 1.3.1 Review of some feature extraction methods 1.4 Conclusion Appendix A References 2 EEG in auditory selective attention 2.1 Pioneer studies on neural correlates of auditory selective attention 2.2 Single sweep ALRs and wavelet-phase stability (WPS) 2.3 WPS and the neural correlates of selective attention 2.3.1 Study on the normal healthy subjects 2.3.2 Study on tinnitus patients (pre- and post-music therapy) 2.3.3 Study on tinnitus decompensated patients 2.4 Remarks 2.5 Conclusion References 3 Investigating EEG signal detection, feature optimisation, and extraction method for sleep apnea 3.1 Introduction 3.2 Literature review 3.3 Research methodology 3.4 Experimental results 3.4.1 The experimental setup of sleep apnea study 3.4.2 Effect of forebody 3.4.3 Performance analysis using index of orthogonality 3.4.4 Extracting sleep bands using wavelet 3.4.5 Extracting sleep bands using EMD 3.5 Conclusion References 4 Person authentication using electroencephalogram (EEG) brainwaves signals 4.1 Introduction 4.2 The human brain 4.3 Electroencephalogram (EEG) 4.3.1 Event-related potentials 4.3.2 Visual-evoked potential 4.3.3 Electrode placements 4.4 Experimentation 4.4.1 EEG signal recording and segmentation 4.4.2 Feature extraction 4.4.3 Feature selection 4.4.4 Classification 4.5 Results and discussion 4.6 Conclusion References 5 Handedness detection system 5.1 Introduction 5.2 Methodology 5.2.1 Lifting-based discrete Wavelet Transform 5.2.2 Reconstructed empirical mode decomposition 5.2.3 Finite impulse response filter 5.2.4 Mean EEG coherence 5.3 Results and discussion 5.3.1 Analysis of the MEC 5.3.2 Analysis on spectrogram and power spectral density 5.3.3 Analysis on instantaneous frequency 5.3.4 Analysis on dynamic time warp 5.4 Conclusion References 6 Parkinson’s disease feature extraction 6.1 Introduction 6.2 Literature review 6.3 Methodology 6.3.1 Discrete Wavelet Transform 6.3.2 Haar Wavelet 6.3.3 Daubechies Wavelet 6.4 Experiment setup 6.5 Results and discussion 6.5.1 Index of orthogonality 6.5.2 Stride time signal 6.5.3 Sleep EEG 6.5.4 Sleep band extraction 6.6 Conclusion References 7 Source analysis in motor imagery EEG BCI applications 7.1 Introduction to source localization 7.1.1 Inverse solutions 7.1.2 Point-spread function 7.1.3 Spatial component decorrelation 7.2 Application of source localization to BCI feature extraction 7.2.1 EEG dataset 7.2.2 Overview of the signal processing sequence 7.2.3 Stage 1. Analysis in sensor space 7.2.4 Stage 2. Analysis in source space References 8 Evaluation for smart air travel support system 8.1 Introduction 8.2 Seat comfort and discomfort 8.2.1 Identifying factors of seating comfort 8.2.2 Survey of relationship between seat location and sitting posture 8.2.3 Validation of aircraft cabin simulator 8.2.4 Validation experiment for smart neck support system (SNes) 8.3 Discussion and conclusion References 9 Brain signal classification using normalisation 9.1 Introduction to brain signal classification 9.1.1 The SSVEP response 9.1.2 Normalisation 9.2 SSVEP detection methods 9.2.1 Correlation-based classification 9.2.2 Power-based classification 9.3 Previous work 9.4 Comparison of normalisation methods 9.4.1 Comparison: CCA-based normalisation 9.4.2 Comparison: PSD-based normalisation 9.5 Discussion 9.6 Summary References 10 The biometric brain dermatoglyphic neural architecture (DNA): brain power at your fingertips 10.1 Introduction 10.1.1 Brain and physiology of fingerprints 10.2 Connections of brain to fingers 10.2.1 Ridges patterns on the fingers 10.2.2 Fingerprints and human behaviour 10.3 Dermatoglyphics 10.3.1 Dermatoglyphics and intelligence 10.3.2 Dermatoglyphics and personal identification 10.3.3 Dermatoglyphics and biometric assessment 10.3.4 Biometric dermatoglyphic neural architecture (DNA) report 10.3.5 Dermatoglyphics and left–right brain dominance 10.3.6 Connection of brain locations to fingers 10.3.7 Ridges on the fingers connected to Neocortex 10.3.8 Dermatoglyphics and brain lobes functionality 10.3.9 Dermatoglyphics and learning styles 10.3.10 The DNA assessment and multiple intelligence 10.4 Dermatoglyphics and its applications 10.4.1 Dermatoglyphics as genetic markers 10.4.2 Early detection as indicator for prevention of schizophrenia 10.4.3 Dermatoglyphics and medical conditions 10.4.4 Dermatoglyphics and diseases 10.4.5 Dermatoglyphics and sports 10.4.6 Dermatoglyphics and applications to daily life References Further reading 11 Electrocardiography: overview, preparation, and technique 11.1 Introduction 11.2 History 11.3 Overview 11.4 Interpreting the ECG: a six-step approach 11.4.1 Interpret ECG using rate and rhythm 11.4.2 Axis determination in axial reference system (methods for determining the QRS axis) 11.4.3 Methods for determining the interval 11.4.4 Morphology 11.4.5 STE-mimics 11.4.6 Ischemia, injury and infarct 11.5 Computer-assisted ECG interpretation 11.5.1 Detection of limb lead misplacements 11.5.2 Pre-processing of ECG 11.5.3 Feature extraction 11.5.4 Classification 11.5.5 Deep learning 11.6 Conclusion Acknowledgment References 12 Blind source separation for OSAS: data extraction 12.1 Introduction 12.2 Methodology 12.2.1 Second-order blind identification algorithm 12.2.2 Robust SOBI algorithm 12.2.3 Wavelet denoising 12.3 The evaluation criteria 12.4 The experimental results 12.5 The ICA approach on real EEG signals References Index
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