With the rise of advanced computerized data collection systems, monitoring devices, and instrumentation technologies, large and complex datasets accrue as an inevitable part of biomedical enterprise. The availability of these massive amounts of data offers unprecedented opportunities to advance our understanding of underlying biological and physiological functions, structures, and dynamics. Biosignal Processing: Principles and Practices provides state-of-the-art coverage of contemporary methods in biosignal processing with an emphasis on brain signal analysis. After introducing the fundamentals, it presents emerging methods for brain signal processing, focusing on specific non-invasive imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), magnetic resonance imaging (MRI), and functional near-infrared spectroscopy (fNIR). In addition, the book presents recent advances, reflecting the evolution of biosignal processing. As biomedical datasets grow larger and more complicated, the development and use of signal processing methods to analyze and interpret these data has become a matter of course. This book is one step in the development of biosignal analysis and is designed to stimulate new ideas and opportunities in the development of cutting-edge computational methods for biosignal processing.
Author(s): Hualou Liang, Joseph D. Bronzino, Donald R. Peterson
Publisher: CRC Press
Year: 2013
Language: English
Pages: xii+198
Biosignal Processing: Principles and Practices......Page 4
Contents......Page 6
Preface......Page 8
Editors......Page 10
Contributors......Page 12
1.1 Acquisition......Page 14
1.1.1 Sampling Theorem......Page 15
1.1.2 Quantization Effects......Page 17
1.2 Signal Processing......Page 18
1.2.1 Digital Filters......Page 19
1.2.1.2 Transfer Function in z Domain......Page 20
1.2.1.3 FIR and IIR Filters......Page 23
1.2.1.5 Examples......Page 24
1.2.2 Signal Averaging......Page 26
1.2.2.1 Example......Page 29
1.2.3.2 Parametric Estimators......Page 31
Defining Terms......Page 34
References......Page 35
Further Information......Page 36
2 Time–Frequency Signal Representations for Biomedical Signals......Page 38
2.2 Desirable Properties of Time–Frequency Representations......Page 45
2.3.1 Cohen’s Class of TFRs......Page 48
2.3.2 Affine Class of TFRs......Page 49
2.3.3 Hyperbolic Class of TFRs......Page 54
2.4.2 Smoothed Wigner Distributions......Page 55
2.4.5 Scalogram or Wavelet Transform Squared Magnitude......Page 56
2.4.6 Biomedical Applications......Page 57
References......Page 59
Further Information......Page 61
3.1 Introduction......Page 62
3.2 Synaptic Action Generates EEG......Page 63
3.3 Time Domain Spectral Analysis......Page 65
3.4 Impact of Spatial Filtering on EEG Power Spectra......Page 68
3.5 Spatial Statistics of EEG......Page 71
3.6 Effects of Volume Conduction on EEG Coherence......Page 73
3.7 Second-Order Blind Identification......Page 78
3.8 Multivariate Spectral Analysis Using SOBI......Page 79
3.9 Summary......Page 82
References......Page 83
4.1 Introduction......Page 86
4.2 Special Considerations in MEG General Linear Modeling......Page 87
4.3 Observations......Page 88
4.4 Simple General Linear Modeling Example......Page 90
4.5 Contrast Statistic and Normalization......Page 91
4.6 Multisubject Studies......Page 92
4.7 Univariate versus Multivariate GLMs......Page 94
4.8 Canonical Correlation Analysis......Page 95
4.9 Thresholding Statistical Maps......Page 97
4.9.1 False-Positive Measures......Page 98
4.9.3 Bonferroni Correction......Page 99
4.9.4 Random Field Methods......Page 100
4.9.5 Permutation Methods......Page 101
4.9.6 Control of False Discovery Rate......Page 103
4.10 Discussion......Page 105
References......Page 106
5.1.1 Pairwise Registration......Page 114
5.1.3 Emergence of Groupwise Registration......Page 116
5.2 Pairwise Registration-Derived Groupwise Registration......Page 118
5.3 Population Center-Guided Groupwise Registration......Page 119
5.4 Hidden Common Space-Based Groupwise Registration......Page 122
5.5 Applications......Page 124
5.5.1 Typical Applications......Page 125
5.5.2 Advantages of Groupwise Registration......Page 126
5.5.3 Multiple Modes in Groupwise Registration......Page 128
5.5.4 Atlas Building for Infants......Page 129
References......Page 131
6.1 Introduction......Page 136
6.2.1 Physiological Principles: How Can Brain Activity Be Measured through Hemodynamic Changes?......Page 137
6.2.2 Physical Principles: How Can Hemodynamic Activity Be Measured by Optics?......Page 138
6.3 Instrumentation......Page 139
6.4.1 Fast Neuronal Signal......Page 140
6.4.2 Slow Hemodynamic Signal......Page 141
6.5.1.1 Noise of Nonphysiological Basis......Page 142
6.5.1.2 Artifacts That Arise from Systemic Physiological Origins......Page 144
6.5.2.1 Diffusion Theory......Page 146
6.5.2.2 Modified Beer–Lambert Law......Page 147
6.6.2.1 fNIR Device......Page 149
6.6.2.2.1 Attention......Page 150
6.6.2.2.2 Working Memory......Page 151
6.6.2.2.3 Learning and Memory......Page 152
6.6.2.2.4 Problem Solving......Page 153
6.6.2.2.5 Emotion......Page 154
6.6.3.1 Cognitive Performance Assessment......Page 155
6.6.3.2 Brain–Computer Interface......Page 157
6.6.3.3 Enhancement of Unmanned Aerial Vehicle Operator Training, Evaluation, and Interface Development......Page 158
6.6.3.4 Cognitive Workload Assessment of Air Traffic Operators......Page 159
6.6.3.6 Cognitive Activity Assessment Following TBI on Working Memory Domain......Page 161
6.6.4 Future Directions......Page 162
References......Page 163
7.1 Introduction......Page 170
7.2 Cross-Estimators, Time, and Frequency Domain......Page 171
7.3.2 AR Model Introduction......Page 172
7.3.3 Model Parameters Estimation......Page 173
7.3.4 Concept of Granger Causality......Page 174
7.3.5 Directional Measures......Page 175
7.3.5.1 Measures Derived from Granger Causality......Page 176
7.3.6 Concept of Partial Measures......Page 177
7.3.7 Dynamical (Nonstationary) Data......Page 180
7.3.10 Linear versus Nonlinear......Page 181
7.4.1 Spike Train Data Analysis......Page 184
7.4.2 Connectivity of Active Areas Found by fMRI Data Analysis......Page 185
7.4.3 Dynamics of Transmissions during a Cognitive Task: Scalp EEG Data Analysis......Page 186
7.4.4 Localization of Epileptic Foci: LFP Data Analysis......Page 188
7.5 Summary......Page 189
References......Page 191
Color Plates......Page 196
