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20: T-test 190 Net Station Waveform Tools Technical Manual S-MAN-200-WFTR-001 • December 21, 2006
chapter 21 CHAPTER 21WAVELET Concepts T he Wavelet tool is designed to operate on Net Station segmented or Combination files. It operates only on data from good channels. The tool performs a wavelet transformation on Net Station data, decomposing the EEG signal into its temporal and frequency components and outputting this information in a Wavelet file. Wavelet Analysis Wavelet analysis can provide both temporal and frequency information because it uses a scale-varying basis function: the wavelet. Net Station uses the Morlet wavelet, which is created by multiplying a sinusoidal wave (Figure 21-1; left) by a Gaussian envelope (middle). The result is a small wave that oscillates with varying amplitude within a finite period, concentrating its energy around a point in time (right). X = sine wave envelope function wavelet Figure 21-1. Sine wave (left) multiplied by an envelope function (middle) produces a wavelet (right) Net Station Waveform Tools Technical Manual S-MAN-200-WFTR-001 • December 21, 2006 191
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Electrical Geodesics, Inc. Net Stat
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Electrical Geodesics makes no warra
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vi Contents chapter 3 chapter 4 cha
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viii Contents chapter 18 chapter 19
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List of Figures chapter 5 Averaging
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List of Figures 17-16 Temporal rela
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List of Figures xiv Net Station Wav
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List of Tables chapter 17 Segmentat
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Preface • The Net Station Viewer
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Preface Troubleshooting, Support, a
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1: The Waveform Tools Window Table
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1: The Waveform Tools Window Creati
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1: The Waveform Tools Window • To
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1: The Waveform Tools Window Tips o
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1: The Waveform Tools Window In an
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1: The Waveform Tools Window File M
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1: The Waveform Tools Window Docs M
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1: The Waveform Tools Window 36 Net
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2: Jobs and Results Window Jobs and
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2: Jobs and Results Window Results
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2: Jobs and Results Window 42 Net S
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3: Common Controls 2. Output Option
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3: Common Controls Table 3-2. Outpu
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3: Common Controls • Category lis
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4: Artifact Detection Threshold Art
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4: Artifact Detection The tool can
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4: Artifact Detection Eye Movements
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4: Artifact Detection 2. “Perform
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4: Artifact Detection 3. “Output
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4: Artifact Detection Eye-Blink Thr
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4: Artifact Detection Data Pane: Se
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4: Artifact Detection 64 Net Statio
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5: Averaging Averaging Specificatio
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5: Averaging 5. Exclude Subjects an
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6: Bad Channel Replacement For more
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6: Bad Channel Replacement 72 Net S
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7: Baseline Correction The followin
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7: Baseline Correction 76 Net Stati
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8: Combine Files Combine Files Spec
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8: Combine Files Stacking Files Sta
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8: Combine Files Merge Just the Sel
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8: Combine Files 2. Carry Selected
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9: Difference Wave You can choose f
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9: Difference Wave 88 Net Station W
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10: Event Export The spreadsheet co
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10: Event Export 92 Net Station Wav
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11: File Export The File Export spe
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11: File Export 96 Net Station Wave
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12: Filtering • Stopband gain: co
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12: Filtering Filtering Specificati
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12: Filtering Filter Settings Pane
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13: Markup from File Event File For
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13: Markup from File Marking Channe
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13: Markup from File Net Station EE
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Net Station EEG file with mark-up e
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13: Markup from File Net Station EE
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13: Markup from File 2. Track Name.
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14: Montage Operations There are se
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14: Montage Operations 2. Montage l
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15: Ocular Artifact Removal The OAR
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15: Ocular Artifact Removal Table 1
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15: Ocular Artifact Removal 124 Net
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16: Script be detected only at run
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16: Script 128 Net Station Waveform
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17: Segmentation Segmentation Overv
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17: Segmentation Segmentation Proce
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17: Segmentation 5 A category can c
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17: Segmentation Figure 17-8 shows
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17: Segmentation Double-Checking Yo
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Page 142 and 143: 17: Segmentation Table 17-4. Criter
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Page 146 and 147: 17: Segmentation The following are
Page 148 and 149: 17: Segmentation Temporal Relations
Page 150 and 151: 17: Segmentation Table 17-8. Criter
Page 152 and 153: 17: Segmentation Temporal Relations
Page 154 and 155: 17: Segmentation • Beyond: specif
Page 156 and 157: 17: Segmentation • Generate Event
Page 158 and 159: 17: Segmentation We base the segmen
Page 160 and 161: 17: Segmentation Example 2: Advance
Page 162 and 163: 17: Segmentation Criteria Set 1 Cri
Page 164 and 165: 17: Segmentation 164 Net Station Wa
Page 166 and 167: 18: Short Epoch Filtering FIR versu
Page 168 and 169: 18: Short Epoch Filtering The follo
Page 170 and 171: 19: Statistic Extraction You specif
Page 172 and 173: 19: Statistic Extraction 2. Add/Clo
Page 174 and 175: 19: Statistic Extraction Note: By
Page 176 and 177: 19: Statistic Extraction Wavelet Da
Page 178 and 179: 19: Statistic Extraction • Maximu
Page 180 and 181: 19: Statistic Extraction Note: If t
Page 182 and 183: 19: Statistic Extraction Note: For
Page 184 and 185: 20: T-test T-test Waveform Tool The
Page 186 and 187: 20: T-test The two-cell t-test woul
Page 188 and 189: 20: T-test The results are output t
Page 192 and 193: 21: Wavelet In wavelet transformati
Page 194 and 195: 21: Wavelet The top images show a w
Page 196 and 197: 21: Wavelet Net Station’s Values
Page 198 and 199: 21: Wavelet 3. “Frequency scale f
Page 200 and 201: 21: Wavelet before or after the eve
Page 202 and 203: A: Software Technical Support 202 N
Page 204 and 205: B: Updating EGI Licenses Tips on Up
Page 206 and 207: B: Updating EGI Licenses Generating
Page 208 and 209: B: Updating EGI Licenses Questions
Page 210 and 211: Index E Edit menu 33 editing specif
Page 212: Index Statistic Extraction specific
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