EEG signal processing : feature extraction, selection and classification methods / edited by Wai Yie Leong.

Format:

Book

Contributor:

Leong, Wai Yie, editor.

Series:

Healthcare technologies series ; Volume 16.

Healthcare Technologies Series ; Volume 16

Language:

English

Subjects (All):

Electroencephalography.

Signal processing.

Physical Description:

1 online resource (322 pages).

Edition:

1st ed.

Place of Publication:

London, England : The Institution of Engineering and Technology, [2019]

Summary:

This book focuses on the feature extraction methods used in Electroencephalographic (EEG) signal processing. It presents state-of-the-art aspects of EEG interpretation and the value of EEG; gives practical tips on interpretation and covers specific areas where EEG is most useful.

Contents:

Intro

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

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

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

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

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

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

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

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

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

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

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

Index.

Notes:

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

1-83724-771-4

1-78561-371-5

OCLC:

1101301599