Wavelets in neuroscience
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| Format: | Buch |
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| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2015
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| Schriftenreihe: | Springer series in synergetics
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| 245 | 1 | 0 | |a Wavelets in neuroscience |c Alexander E. Hramov ... |
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| 300 | |a XVI, 318 S. |b Ill., graph. Darst. |c 24 cm | ||
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| 653 | |a Research | ||
| 653 | |a Wavelet Theory and Analysis | ||
| 653 | |a Neuronal Networks | ||
| 653 | |a Neuronal Spikes and Activity | ||
| 653 | |a Data Analysis in Neurophysics | ||
| 653 | |a Neurodynamics and Neurophysiology | ||
| 653 | |a Dynamical Systems in Neuroscience | ||
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Datensatz im Suchindex
| _version_ | 1819731158745743360 |
|---|---|
| adam_text | CONTENTS
1 MATHEMATICAL METHODS OF SIGNAL PROCESSING IN NEUROSCIENCE 1
1.1 GENERAL REMARKS 1
1.2 NONSTATIONARITY OF NEUROPHYSIOLOGICAL DATA 2
1.3 WAVELETS IN BASIC SCIENCES AND NEUROSCIENCE 4
1.4 AUTOMATIC PROCESSING OF EXPERIMENTAL DATA IN NEUROSCIENCE 5
1.5 BRAIN-COMPUTER INTERFACES 6
1.6 TOPICS TO CONSIDER 7
REFERENCES 9
2 BRIEF TOUR OF WAVELET THEORY 15
2.1 INTRODUCTION 15
2.2 FROM FOURIER ANALYSIS TO WAVELETS 16
2.3 CONTINUOUS WAVELET TRANSFORM 26
2.3.1 MAIN DEFINITIONS: PROPERTIES OF THE CONTINUOUS
WAVELET TRANSFORM 26
2.3.2 MOTHER WAVELETS 32
2.3.3 NUMERICAL IMPLEMENTATION OF THE CONTINUOUS
WAVELET TRANSFORM 35
2.3.4 VISUALISATION OF WAVELET SPECTRA: WAVELET
SPECTRA OF MODEL SIGNALS 45
2.3.5 PHASE OF THE WAVELET TRANSFORM 52
2.4 DISCRETE WAVELET TRANSFORM 63
2.4.1 COMPARISON OF THE DISCRETE AND CONTINUOUS
WAVELET TRANSFORMS 63
2.4.2 GENERAL PROPERTIES 65
REFERENCES 72
3 ANALYSIS OF SINGLE NEURON RECORDINGS 77
3.1 INTRODUCTION 77
3.2 WAVELET ANALYSIS OF INTRACELLULAR DYNAMICS 78
3.2.1 INTERFERENCE MICROSCOPY AND SUBCELLULAR DYNAMICS 78
IX
HTTP://D-NB.INFO/1051801923
X CONTENTS
3.2.2 MODULATION OF HIGH FREQUENCY OSCILLATION
BY LOW FREQUENCY PROCESSES 80
3.2.3 DOUBLE WAVELET TRANSFORM AND ANALYSIS
OF MODULATION 81
3.2.4 MODULATION OF SPIKE TRAINS BY INTRINSIC NEURON
DYNAMICS 83
3.3 INFORMATION ENCODING BY INDIVIDUAL NEURONS 86
3.3.1 VIBRISSAE SOMATOSENSORY PATHWAY 86
3.3.2 CLASSIFICATION OF NEURONS BY FIRING PATTERNS 88
3.3.3 DRAWBACKS OF THE TRADITIONAL APPROACH
TO INFORMATION PROCESSING 89
3.3.4 WAVELET TRANSFORM OF SPIKE TRAINS 90
3.3.5 DYNAMICAL STABILITY OF THE NEURONAL RESPONSE 93
3.3.6 STIMULUS RESPONSES OF TRIGEMINAL NEURONS 96
3.4 WAVELET COHERENCE FOR SPIKE TRAINS: A WAY TO QUANTIFY
FUNCTIONAL CONNECTIVITY 101
3.4.1 WAVELET COHERENCE OF TWO POINT PROCESSES 102
3.4.2 MEASURE OF FUNCTIONAL COUPLING BETWEEN
STIMULUS AND NEURONAL RESPONSE 104
3.4.3 FUNCTIONAL CONNECTIVITY OF GRACILIS NEURONS
TO TACTILE STIMULUS 105
REFERENCES 117
4 CLASSIFICATION OF NEURONAL SPIKES
FROM EXTRACELLULAR RECORDINGS 121
4.1 INTRODUCTION 121
4.2 GENERAL PRINCIPLES OF SPIKE SORTING 122
4.3 SPIKE DETECTION OVER A BROADBAND FREQUENCY ACTIVITY 124
4.4 NAIVE SPIKE SORTING 127
4.5 PRINCIPAL COMPONENT ANALYSIS AS SPIKE-FEATURE EXTRACTOR 130
4.5.1 HOW IT WORKS 130
4.5.2 POSSIBLE PITFALLS 132
4.6 WAVELET TRANSFORM AS SPIKE-FEATURE EXTRACTOR 135
4.6.1 WAVELET SPIKE CLASSIFIER 135
4.6.2 POTENTIAL PROBLEMS 136
4.7 WAVELET SHAPE-ACCOUNTING CLASSIFIER 138
4.8 PERFORMANCE OF PC A VS WT FOR FEATURE EXTRACTION 139
4.9 SENSITIVITY OF SPIKE SORTING TO NOISE 142
4.9.1 IMPACT OF HIGH/LOW FREQUENCY NOISE ON PCA
AND WT 143
4.9.2 PROPER NOISE FILTERING MAY IMPROVE SPIKE SORTING 145
4.10 OPTIMAL SORTING OF SPIKES WITH WAVELETS AND ADAPTIVE
FILTERING 147
4.10.1 NOISE STATISTICS AND SPIKE SORTING 147
4.10.2 PARAMETRIC WAVELET SORTING WITH ADVANCED FILTERING 148
CONTENTS XI
4.11 SPIKE SORTING BY ARTIFICIAL NEURAL NETWORKS 154
4.11.1 GENERAL APPROACH 154
4.11.2 ARTIFICIAL NEURAL NETWORKS 156
4.11.3 TRAINING THE ARTIFICIAL NEURAL NETWORK 159
4.11.4 ALGORITHM FOR SPIKE SORTING USING NEURAL NETWORKS 160
4.12 ARTIFICIAL WAVELET NEURAL NETWORKS FOR SPIKE SORTING 163
4.12.1 STRUCTURE OF WAVELET NEURAL NETWORKS 164
4.12.2 WAVELET NEURAL NETWORKS 164
REFERENCES 174
5 WAVELET APPROACH TO THE STUDY OF RHYTHMIC NEURONAL ACTIVITY 177
5.1 INTRODUCTION 177
5.2 BASIC PRINCIPLES OF ELECTROENCEPHALOGRAPHY 178
5.2.1 ELECTRICAL BIOPOTENTIAL: FROM NEURON TO BRAIN 179
5.2.2 APPLICATION OF EEG IN EPILEPSY RESEARCH 180
5.3 GENERAL PRINCIPLES OF TIME-FREQUENCY ANALYSIS OF EEG 182
5.3.1 THE NEED FOR MATHEMATICAL ANALYSIS OF EEG 182
5.3.2 TIME-FREQUENCY ANALYSIS OF EEG: FROM
FOURIER TRANSFORM TO WAVELETS 183
5.3.3 TIME-FREQUENCY ANALYSIS OF SPIKE-WAVE
DISCHARGES BY MEANS OF DIFFERENT MOTHER WAVELETS 187
5.4 APPLICATIONS OF WAVELETS IN ELECTROENCEPHALOGRAPHY 195
5.4.1 TIME-FREQUENCY ANALYSIS OF EEG STRUCTURE 196
5.4.2 AUTOMATIC DETECTION OF OSCILLATORY PATTERNS
AND DIFFERENT RHYTHMS IN PRE-RECORDED EEG 196
5.4.3 CLASSIFICATION OF OSCILLATORY PATTERNS 197
5.4.4 REAL-TIME DETECTION OF OSCILLATORY PATTERNS IN EEG 197
5.4.5 MULTICHANNEL EEG ANALYSIS OF SYNCHRONIZATION
OF BRAIN ACTIVITY 198
5.4.6 ARTIFACT SUPPRESSION IN MULTICHANNEL EEG
USING WAVELETS AND INDEPENDENT COMPONENT ANALYSIS 198
5.4.7 STUDY OF COGNITIVE PROCESSES 199
REFERENCES 201
6 TIME-FREQUENCY ANALYSIS OF EEG: FROM THEORY TO PRACTICE 211
6.1 INTRODUCTION 211
6.2 OSCILLATORY ACTIVITY PRIOR TO EPILEPTIC SEIZURES 212
6.2.1 DESCRIPTION OF EXPERIMENT 212
6.2.2 TIME-FREQUENCY WAVELET ANALYSIS OF CORTICAL
AND THALAMIC SWDS 213
6.2.3 DELTA AND THETA PRECURSORS OF SWD
AS MEASURED IN THE CORTEX AND THALAMUS 216
6.3 TIME-FREQUENCY ANALYSIS OF SLEEP SPINDLES
AND SPINDLE-LIKE OSCILLATORY PATTERNS 221
6.3.1 RELATIONSHIP BETWEEN SLEEP SPINDLES, 5-9 HZ
OSCILLATIONS, AND SWDS 221
6.3.2 EXPERIMENTAL PROCEDURE 222
XII CONTENTS
6.3.3 TIME-FREQUENCY ANALYSIS OF SPINDLE-LIKE
OSCILLATORY PATTERNS: COMPARISON OF DIFFERENT
MOTHER WAVELETS 223
6.3.4 INTRA-SPINDLE FREQUENCY DYNAMICS IN EPILEPTIC
AND NON-EPILEPTIC RAT STRAINS 231
6.3.5 AGE-RELATED CHANGES IN THE TIME-FREQUENCY
STRUCTURE OF SLEEP SPINDLES 233
6.4 PRACTICAL NOTES CONCERNING APPLICATION
OF THE CONTINUOUS WAVELET TRANSFORM
IN TIME-FREQUENCY EEG ANALYSIS 235
6.4.1 COMPLEX OR REAL MOTHER WAVELET 236
6.4.2 SHAPE OF MOTHER WAVELET 236
6.4.3 WIDTH OF MOTHER WAVELET 236
6.5 CLASSIFICATION OF SLEEP SPINDLE EVENTS BY MEANS
OF ADAPTIVE WAVELET ANALYSIS 237
6.5.1 CONSTRUCTION OF ADAPTIVE WAVELET BASIS
( SPINDLE WAVELET ) FOR AUTOMATIC RECOGNITION
OF SLEEP SPINDLES 237
6.5.2 IDENTIFICATION OF TWO SPINDLE TYPES
WITH THE AID OF TWO ADAPTIVE SPINDLE WAVELETS 239
6.5.3 TWO TYPES OF SLEEP SPINDLES AND THEIR
RELEVANCE TO SWD 240
6.6 SYNCHRONOUS DYNAMICS OF EPILEPTIC DISCHARGES:
APPLICATION OF TIME-SCALE SYNCHRONIZATION APPROACH 242
REFERENCES 248
7 AUTOMATIC DIAGNOSTICS AND PROCESSING
OF EEG 253
7.1 INTRODUCTION 253
7.2 AUTOMATIC IDENTIFICATION OF EPILEPTIC SPIKE-WAVE DISCHARGES 255
7.2.1 GENERAL ASPECTS OF AUTOMATIC SWD DETECTION
IN EEG 255
7.2.2 OFF-LINE WAVELET-BASED ALGORITHM
FOR AUTOMATIC DETECTION OF SWDS 256
7.2.3 PERFORMANCE OF THE METHOD 259
7.3 BRAIN-COMPUTER INTERFACE FOR ON-LINE DIAGNOSTICS
OF EPILEPTIC SEIZURES 260
7.3.1 ON-LINE SWD DETECTION ALGORITHM 260
7.3.2 EXPERIMENTAL VERIFICATION OF THE ALGORITHM
AND ON-LINE SWD DIAGNOSTICS 264
7.4 AUTOMATIC DETECTION OF SLEEP SPINDLES BY ADAPTIVE WAVELETS 267
7.5 AUTOMATIC DETECTION AND DISCRIMINATION OF 5-9 HZ
OSCILLATIONS AND SLEEP SPINDLES 271
7.6 ON-OFF INTERMITTENCY OF THALAMO-CORTICAL OSCILLATIONS 273
7.6.1 NONLINEAR DYNAMICS OF SWDS, SLEEP SPINDLES,
AND 5-9 HZ OSCILLATIONS 273
CONTENTS XIII
7.6.2 INTERMITTENT DYNAMICS OF SWDS UNDER
THE INFLUENCE OF A PRO-ABSENCE DRUG (VIGABATRIN) 276
7.6.3 MECHANISMS OF INTERMITTENT BEHAVIOR
IN THE THALAMO-CORTICAL NEURONAL NETWORK 278
7.7 SERIAL IDENTIFICATION OF EEG PATTERNS USING ADAPTIVE
WAVELET ANALYSIS 279
7.7.1 ADAPTIVE WAVELET-BASED TECHNIQUE
AND THE SERIAL METHOD 280
7.7.2 EXPERIMENTAL VALIDATION OF THE SERIAL METHOD 284
7.8 ARTIFACT SUPPRESSION IN MULTICHANNEL EEG USING
WAVELET-ENHANCED INDEPENDENT COMPONENT ANALYSIS 286
7.8.1 INDEPENDENT COMPONENT ANALYSIS IN EEG STUDIES 287
7.8.2 ICA-BASED ARTIFACT SUPPRESSION 288
7.8.3 WAVELET-ENHANCED ICA (WICA) FOR ARTIFACT
SUPPRESSION 290
7.8.4 DATA COLLECTION AND NUMERICAL TOOLS
FOR TESTING CONNECTIVITY 293
7.8.5 SUPPRESSION OF ARTIFACTS BY ICA AND WICA METHODS 295
7.8.6 RECOVERING BRAIN SIGNALS IN THE PRESENCE OF ARTIFACTS 299
7.8.7 POWER SPECTRUM DISTORTION 301
7.8.8 ARTIFACT SUPPRESSION AND NON-LOCAL
CHARACTERISTICS DERIVED FROM EEG 302
7.8.9 CONCLUSION 304
REFERENCES 306
8 CONCLUSION 313
REFERENCE 314
INDEX
315
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| indexdate | 2024-12-24T04:30:56Z |
| institution | BVB |
| isbn | 9783662438497 3662438496 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028106709 |
| oclc_num | 881387113 |
| open_access_boolean | |
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| owner_facet | DE-20 |
| physical | XVI, 318 S. Ill., graph. Darst. 24 cm |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Springer |
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| series2 | Springer series in synergetics |
| spellingShingle | Wavelets in neuroscience Nichtlineare Dynamik (DE-588)4126141-0 gnd Wavelet-Transformation (DE-588)4814181-1 gnd Nervensystem (DE-588)4041643-4 gnd |
| subject_GND | (DE-588)4126141-0 (DE-588)4814181-1 (DE-588)4041643-4 |
| title | Wavelets in neuroscience |
| title_auth | Wavelets in neuroscience |
| title_exact_search | Wavelets in neuroscience |
| title_full | Wavelets in neuroscience Alexander E. Hramov ... |
| title_fullStr | Wavelets in neuroscience Alexander E. Hramov ... |
| title_full_unstemmed | Wavelets in neuroscience Alexander E. Hramov ... |
| title_short | Wavelets in neuroscience |
| title_sort | wavelets in neuroscience |
| topic | Nichtlineare Dynamik (DE-588)4126141-0 gnd Wavelet-Transformation (DE-588)4814181-1 gnd Nervensystem (DE-588)4041643-4 gnd |
| topic_facet | Nichtlineare Dynamik Wavelet-Transformation Nervensystem |
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