Mathematics in Independent Component Analysis

More documents

Recommendations

Info

258 Chapter 19. LNCS 3195:977-984, 2004 3D Spatial Analysis of fMRI Data on a Word Perception Task 983 Comparison of the Regression Analysis Versus ICA. To compare the results of the fixed-effect analysis with the results of the ICA the correlation coefficients between the expected time-trends of the fixed-effect analysis and the time-trends of the ICs were calculated. No substantial correlation was found: 87 % of all these coefficients were in the range of −0.1 to0.1, the highest coefficient found being 0.36 for an IC within the auditory cortex (figure 2). The correlation coefficients for the proposed word detection network (figure 3) were 0.14, 0.08, 0.19 and 0.18 for FB1–FB4. Therefor it is quite obvious that this network of areas in the inferior frontal gyrus cannot be detected with a classic fixed-effect regression analysis. While the reasons for the differences between the activation-trends of the ICs and the assumed time-trends are still subject to on-going research, it can be expected that the results of this ICA will help to gain further information about the work flow of the brain concerning the task of word detection. Fig. 5. The activation of the ICs shown in figure 3 (dotted) and 4 (solid), plotted for scan no. 25–75. While these time-trends obviously appear to be correlated, their correlation coefficient remains very low due to temporary baseline- and time-shifts in the trends. 3 Conclusions We have shown that ICA can be a valuable tool to detect hidden or suspected links and activity in the brain that cannot be found using the classical approach of a model-based analysis like the general linear model. While clearly ICA cannot be used to validate a model (being in itself model-free), it can give useful hints to understand the internal organization of the brain and help to develop new models and study designs which then can be validated using a classic regression analysis.
Chapter 19. LNCS 3195:977-984, 2004 259 984 Ingo R. Keck et al. Acknowledgment This work was supported by the BMBF (project ModKog). References 1. K. K. Kwong, J. W. Belliveau, D. A. Chester, I. E. Goldberg, R. M. Weisskoff, B. P. Poncelet, D. N. Kennedy, B. E. Hoppel, M. S. Cohen, R. Turner, H-M. Cheng, T. J. Brady, B. R. Rosen, “Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation”, Proc. Natl. Acad. USA 89, 5675– 5679 (1992). 2. S. Ogawa, T. M. Lee, A. R. Kay, D. W. Tank, “Brain magnetic-resonance-imaging with contrast dependent on blood oxygenation”, Proc. Natl Acad. Sci. USA 87, 9868–9872 (1990). 3. R. S. J. Frackowiak, K. J. Friston, Ch. D. Frith, R. J. Dolan, J. C. Mazziotta, “Human Brain Function”, Academic Press, San Diego, USA, 1997. 4. A.J. Bell, T.J. Sejnowski, “An information-maximisation approach to blind separation and blind deconvolution”, Neural Computation, 7(6), 1129–1159 (1995). 5. M.J. McKeown, T.J. Sejnowski, “Independent Component Analysis of FMRI Data: Examining the Assumptions”, Human Brain Mapping 6, 368–372 (1998). 6. A. Hyvärinnen, “Fast and Robust Fixed-Point Algorithms for Independent Component Analysis”, IEEE Transactions on Neural Networks 10(3), 626–634 (1999). 7. F. Esposito, E. Formisano, E. Seifritz, R. Goebel, R. Morrone, G. Tedeschi, F. Di Salle, “Spatial Independent Component Analysis of Functional MRI Time-Series: To What Extent Do Results Depend on the Algorithm Used?”, Human Brain Mapping 16, 146–157 (2002). 8. K. Specht, J. Reul, “Function segregation of the temporal lobes into highly differentiated subsystems for auditory perception: an auditory rapid event-related fMRI-task”, NeuroImage 20, 1944–1954 (2003). 9. SPM2: http://www.fil.ion.ulc.ac.uk/spm/spm2.html, July 2003. 10. V.D. Calhoun, T. Adali, G.D. Pearlson, J.J. Pekar, “A Method for Making Group Inferences from Functional MRI Data Using Independent Component Analysis”, Human Brain Mapping 14, 140–151 (2001).
Page 1 and 2:
Statistical machine learning of bio
Page 3:
to Jakob
Page 6 and 7:
vi Preface
Page 8 and 9:
viii CONTENTS 3 Signal Processing 8
Page 11 and 12:
Chapter 1 Statistical machine learn
Page 13 and 14:
1.1. Introduction 5 auditory cortex
Page 15 and 16:
1.2. Uniqueness issues in independe
Page 17 and 18:
1.2. Uniqueness issues in independe
Page 19 and 20:
S U M M A R Y T his �le contains
Page 21 and 22:
1.3. Dependent component analysis 1
Page 23 and 24:
Table 1.1: BSS algorithms based on
Page 25 and 26:
Page 27 and 28:
Page 29 and 30:
Page 31 and 32:
Page 33 and 34:
1.4. Sparseness 25 1.4 Sparseness O
Page 35 and 36:
1.4. Sparseness 27 Theorem 1.4.3 (S
Page 37 and 38:
1.4. Sparseness 29 R 3 R 3 A BSRA R
Page 39 and 40:
1.4. Sparseness 31 Sparse projectio
Page 41 and 42:
1.5. Machine learning for data prep
Page 43 and 44:
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
1.6. Applications to biomedical dat
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
1.7. Outlook 51 noise data signal i
Page 61:
Part II Papers 53
Page 64 and 65:
56 Chapter 2. Neural Computation 16
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Page 72 and 73:
Page 74 and 75:
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
82 Chapter 3. Signal Processing 84(
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
90 Chapter 4. Neurocomputing 64:223
Page 100 and 101:
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
Page 108 and 109:
Page 110 and 111:
Page 112 and 113:
104 Chapter 5. IEICE TF E87-A(9):23
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
114 Chapter 6. LNCS 3195:726-733, 2
Page 124 and 125:
116 Chapter 6. LNCS 3195:726-733, 2
Page 126 and 127:
118 Chapter 6. LNCS 3195:726-733, 2
Page 128 and 129:
120 Chapter 6. LNCS 3195:726-733, 2
Page 130 and 131:
122 Chapter 6. LNCS 3195:726-733, 2
Page 132 and 133:
124 Chapter 7. Proc. ISCAS 2005, pa
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
130 Chapter 8. Proc. NIPS 2006 Towa
Page 140 and 141:
132 Chapter 8. Proc. NIPS 2006 1.3
Page 142 and 143:
134 Chapter 8. Proc. NIPS 2006 stru
Page 144 and 145:
136 Chapter 8. Proc. NIPS 2006 5.5
Page 146 and 147:
138 Chapter 8. Proc. NIPS 2006
Page 148 and 149:
140 Chapter 9. Neurocomputing (in p
Page 150 and 151:
Page 152 and 153:
Page 154 and 155:
Page 156 and 157:
Page 158 and 159:
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
156 Chapter 10. IEEE TNN 16(4):992-
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
162 Chapter 11. EURASIP JASP, 2007
Page 172 and 173:
Page 174 and 175:
Page 176 and 177:
Page 178 and 179:
Page 180 and 181:
Page 182 and 183:
Page 184 and 185:
176 Chapter 12. LNCS 3195:718-725,
Page 186 and 187:
178 Chapter 12. LNCS 3195:718-725,
Page 188 and 189:
180 Chapter 12. LNCS 3195:718-725,
Page 190 and 191:
182 Chapter 12. LNCS 3195:718-725,
Page 192 and 193:
184 Chapter 12. LNCS 3195:718-725,
Page 194 and 195:
186 Chapter 13. Proc. EUSIPCO 2005
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
192 Chapter 14. Proc. ICASSP 2006 S
Page 202 and 203:
194 Chapter 14. Proc. ICASSP 2006 A
Page 204 and 205:
196 Chapter 14. Proc. ICASSP 2006
Page 206 and 207:
198 Chapter 15. Neurocomputing, 69:
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
Page 214 and 215:
Page 216 and 217: 208 Chapter 15. Neurocomputing, 69:
Page 238 and 239: 230 Chapter 16. Proc. ICA 2006, pag
Page 248 and 249: 240 Chapter 17. IEEE SPL 13(2):96-9
Page 254 and 255: 246 Chapter 18. Proc. EUSIPCO 2006
Page 260 and 261: 252 Chapter 19. LNCS 3195:977-984,
Page 270 and 271: 262 Chapter 20. Signal Processing 8
Page 300 and 301: 292 Chapter 21. Proc. BIOMED 2005,
Page 306 and 307: 298 BIBLIOGRAPHY Barber, C., Dobkin
Page 308 and 309: 300 BIBLIOGRAPHY Georgiev, P. (2001
Page 310 and 311: 302 BIBLIOGRAPHY Keck, I., Theis, F
Page 312 and 313: 304 BIBLIOGRAPHY Schießl, I., Sch
Page 314 and 315: 306 BIBLIOGRAPHY Theis, F. and Inou
show all

Mathematics in Independent Component Analysis

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?