第 35 回 月例発表会 - 医療情報システム研究室
Contents
1. 2 A2. 2 4 1 OCHS
3. 5 Exif JPEG LAN PO 1 T Kosak and K Miwa and Y Yonemura et al A clinicopathologic study on multiple gastric cancers with s
4. 2 1
5. Table 1 GA parameter in a pair comparison Qa value 0 33 Crossover rate 1 0 Mutation rate 0 33 76 Fig 5 Track bar A pair comparison Select individual The average value in total of three parameters O 0 10 20 30 40 50 60 70 80 90 The evaluation number of times Fig 6 5
6. Tou 1 FRET 2013 2 Martin J Herrmann et al Optical topography during a go nogo task assessed with multi channel near infrared spectroscopy Behavioural Brain Research Vol 160 No 1 pp 135 140 2005 3 Matia Okubo Outliers in reaction time data Methodological considerations and prac tical suggestions Bue ATES ise DEE fa Vol 1 No 1 pp 81 89 2011 4 nirs 2009 5 Mirsky A F Behavioral and psychophysiological markers of disordered attention Environ Health Perspect Vol 74 pp 191 199 1987 49 35
7. 3 22 2 2 2 2 1 3 13 16 D 8OL Doshisha University Smart Office Laboratory 4 23 Table 1 Fig 1
8. ImageJ Cell Profiler Genetic Programming GP
9. 3 3 6 1
10. Visual Search Task VST white fNIRS functional Near infrared Spectoscopy white 4
11. Y Genetic Programing GPS 9 19 11 12 13 14 Fk GP 2 GP filter approach 16 17 18 GP
12. interactive Genetic Algorithm iGA iGA Interactive Evolutionary Computation IEC 9 5 UBL CHS PATE 100 5
13. MaPea CH 3 14 CH 8 14 CH 11 18 1 fNIRS functional Near Infrared Spectroscopy fNIRS NIRS LAL
14. 2 A 6 am 2 fNIRS SE leader follower leader
15. classl class 2 early generation BOOS OS O99 OO 90905 3 2000S 909 OOO 8 3 last generation 0000008005900 gt transformed feature value Fig 6 3 3 2 2 2 SVM 2 2 SVM 62 3 3 1 2 2
16. B E ld id OC CHRIST SRR CHS CHA SNS 6 fNIRS NOME
17. RT GO Enter L NOGO RT RT 2 4 10IHz 116 fNIRS functional Near Infrared Spectroscopy ETG 7100 Hb NIRS Near Infrared Spectroscopy Beer Lambert
18. rIEG Low SR 6 GO NOGO Task
19. 30 mm 1 Modified Lambert Beer Oxy Hb Deoxy Hb 7 27 Oxy Hb Deoxy Hb 9 7 log a Eory A T deor AC deoxy aI AS 1 I X a amp 2 2 BOLD Blood Oxygen Level Dependent Effect BOLD Oxy Hb Deoxy Hb 9
20. B amp H Fig 1 Fig 5 9 3 PRET 9 41
21. MySQL A SD MySQL 1000 iGA 2 3 1iGA iGA lk Genetic Algorithm GA
22. 20 12 1 EEG Electroencephalograph fNIRS functional Near Infrared Spectroscopy 3 BMI Brain Machine Interface BMI ARER WET AT DY 2 BMI BM EEG
23. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 n AHG MBE ME Vol 105 No 304 pp 43 46 2005 S Tsujimura and K Ueno Effect of sound environment on learning efficiency in classrooms Journal of Environmental Engineering Transaction of AIJ Vol 75 No 653 pp 561 568 2010 EVE Vol 13 pp 9 13 1975 NG AHS No 515 pp 25 31 1999
24. 1 AGE NIRS Vol 57 pp 319 324 2009 2 FNIRS 14 pp 409 412 2007 3 Vol 24 No 2 pp 77 93 2003 A Vol 11 No 2 pp 64 73 2004 5
25. 7 FFT FFT 8 13 16Hz 13 30Hz 20 SVM
26. CUT ATH ANC23 Indonetwork Priority Intell Safe AR 814 Sound Level Meter A A weighted sound pressure level 65 0 5 dB fNIRS 36 3s 1s 7s 30 30s 400s 60s Fig 1 x x m TA R Et
27. N 1 N 2 3 8 5 1 Fig 7 a Fig 7 b Fig 7 b 21 3 3 N n N __n Fig 7 a Fig 7 b
28. ICA 4 ICA ICA ICA 4 2 x y z A y2 y 2 2 ICA e 3 O S E l 3 Yala ya u x 9 e 7
29. gt 5 4 wrapper approach 5 4 1 2 K 3 wrapper approach 2 base line grid search Spect 6 Ek 3 Table 3 2 Wisconsin Ionosophere SDect 98 20 0 81 97 12 2 43 92 83 1 57 86 03 5 4 2 2 17 Spect wrapper approach SVM RBF 8 g 3
30. Tagged Image File Format 82 10 50 100 3 3 Fig 4 a Fig 4 b Fig 4 a 100 100 Fig 4 b 100 40 i20 120 m 80 E 10 80 E 10 gE gE tie 60 50 it 60 50 B 40 _ 7 100 40 p 100 20 20 Yy Y a ap ss 0 BEBE eee prec ee Be Ea Bee u 0 PRETEN SEM a b Fig 4 4
31. RRE T 80 1 T Weiss E Hansen R Rost L Beyer F Merten C Nichelmann and C Zippel Mental Practice of Motor Skills Used in Poststroke Rehabilitation has Own Effects on Central Nervous Activation International Journal of Neuroscience Vol 78 No 3 4 pp 157 166 1994 2 G Pfurtscheller and C Neuper Future prospects of ERD ERS in the context of brain computer interface BCI developments In Event Related Dynamics of Brain Oscilla tions Vol 159 of Progress in Brain Research pp 433 437 2006 3 A Ubeda J M Azorin N Garcia J M Sabater and C Perez Brain machine interface based on EEG mapping to control an assistive robotic arm In Biomedical Robotics and Biomechatronics BioRob 2012 4th IEEE RAS EMBS International Conference on pp 1311 1315 2012 4 N Al Moubayed B A S Hasan J Q Gan A Petrovski and J McCall Continuous presentation for multi objective channel selection in Brain Computer Interfaces In Evolutionary Computation CEC 2012 IEEE Cong
32. 6 1 pp 100 109 March 2007 2 Takeo Tsujii and Shigeru Watanabe Neural correlates of dual task effect on belief bias syllogistic reasoning A near infrared spectroscopy study Brain research Vol 1287 pp 118 125 1 September 2009 3 Jiro Okuda Toshikatsu Fujii Takashi Tsukiura Kazuyo Tanji Kyoko Suzuki Ryuta Kawashima Hiroshi Fukuda Masatoshi Itoh and Atsushi Yamadori Thinking of the future and past the roles of the frontal pole and the medial temporal lobes NeurolImage Vol 19 pp 1369 1380 August 2003 4 Satoshi Tsujimoto Aldo Genovesio and Steven P Wise Evaluating self generated de cisions in frontal pole cortex of monkeys Nature Neuroscience Vol 13 pp 120 126 6 December 2009 5 KARZ Mediz pp 17 21 2002 35 2014 06 28
33. GP 59 wrapper approach GP ft CR AIAA filter approach at 2 2 SVM
34. BA 2 2 5 2 5 2 L 1 Class1 class 2 penalty transformed feature value Eig 5 ifli lt k weight k 1 i ifli gt k weight i k 1 5 2 6
35. 1 L 3 PRAT FEV RVR SZ 9
36. 2 fNIRS functional near infrared spectroscopy 2 2 2 SAL 1 2 n Tap Tap n 2 SE Synchronization Error 1 Fig SE n Tapa n Taps 7 1 3 3 1
37. Fig 5 PEO OU CRM UC S fNIRS Z score Fig 6 4 B E C Fig 7
38. fe 2 1 2 2 3 BO KIL 13 30Hz 13 16Hz low 8 9 8 13 16Hz 13 30Hz 8 13 16Hz 13 30Hz
39. 2 1 18 ITIA n r Tapan Y y Tap n 1 Subject A I 1 ti SE n SE n 1 S T T 1 e V aps n 1 time ITls n Fig 1 Fig 2 CH 110Tap D
40. Blue KU Red 1 Vol 220 No 31 1998 2 JRH AT AEM AT 4 ABE Vol 22 No 3 pp 399 410 2007 3 Peter Blattner Peter Oelhafen Thomas Gotz Christian Cajochen Sarah L Chellappa Roland Steiner Non visual effects of light on melatonin alertness and cognitive perfor mance can blue enriched light keep us alert PLoS ONE Vol 6 No 1 2011 4 Narcisse P Bichot and Jeffrey D Schall Priming in macaque frontal cortex during popout visual search Feature based facilitation and location based inhibition of return The Journal of Neuroscience Vol 22 No 11 pp 4675 4685 2002 5 Linda Sommerlade Jens Timmer Bjorn Schelter Malenka Mader Wolfgang Mader Block bootstrapping for noisy data Journal of Neuroscience Methods Vol 219 No 2 pp 285 291 2013 26 35 2014 06 H 28 H fNIRS
41. D 8 IEC 73 TA Fig 1 Fig 2 9 10 dD 2 HA 2
42. 10 Lag 100 100 4 2 1 0 7 CH 4 2 3 4 2 1 0 7 4 2 2 Lag 100 100 CH Fig 5 CH Group 4 CH CH CH Lag 0 T CH Lag 100 Lag 100 Group A CH 60dB White noise 80dB CH UML Fig 5 c Group A 7 CH Lag
43. 6 1 1 T flee Ey Fig 1 R
44. 1 2 1 9 7 23 ik BST ORRE xi RE A B 22 5 22 9 C 52 56 fNIRS ETG 7100 fNIRS 10 Hz CHS 10 20 3 x 5 1 2 Fig 2
45. Medium High Low 7 5 Low 2 5 52 2 0 2 0 High mMidium OLow BHigh mMidium OLow 15 _ 15 amp amp T 1 0 1 0 8 8 0 5 0 5 0 0 h 0 0 A B C D E F G A B D F G subject subject a b e tT a y HE N Eig 4 20 2 0 r High 0 552 r High 0 712 High High 1 5 1 5 A Medium Medium Low ig _ Low 3 10 3 1 0 r Medium 0 624 pert Liner High _ gt lt Liner High 7 a gt Liner Medium gt Q Liner Medium 95 05 r Low 0 446 Liner Low Liner Low r Low 0 193 0 0 0 0 0 0 5 1 1 5 2 response time a u error a u a b Eig 5
46. CH CH Fig 7 500 400 300 200 100 0 A CH 3 14 CH 8 14 CH 11 18 Fig 7 CH 3 14 CH 8 14 CH 11 18 THETIC Fig 6 CH3 CH 8 13 19 CH8 CH 3 13 CH CH 18 19 CH11 CH 10 12 19 CH18 CHI4 14 19
47. functional Near Infrared Spectroscopy fNIRS fNIRS 2 fNIRS functional Near Infrared Spectroscopy 2 1 fNIRS 700 900nm MRI 30mm fNIRS
48. GP 1 A 2
49. 4 CET Lnew Zola MiN maxz min 4 x min maz 5 5 ICA ICA ICA PCA fNIRS 3 Wiener NIRS y n 5 y n a n w n 5 a n fNIRS w n fNIRS amp Wiener fNIRS
50. 4 FNIRS 1 2 2 3 T ARIE TORE ZIRIS b
51. 6 1 GO NOGO GO NOGO NOGO 3 fNIRS Oxy Hb 7 5 Medium Low
52. 4 Wiener 6 ASNR signal to noise ratios 2 1CA 2 1 ICA ICA Fig 1 ICA c t s 4 1 x t As t 1 4 ICA lL x t 4 s t ICA e
53. 60 sec 10 sec ER 50 sec MEL 1min T Fig 2 14 12 10 1 eR Fig 2 3 2 B E H K MaPea 22CH 2 DEY O CHI1 2 CH 1 3 CHI21 22
54. 2 HE ICA Independent Component Analysis WS fNIRS ICA LAL ICA 5 fNIRS HO fNIRS ICA INIRS
55. T BGM T 2 DH DHE SSD 2 2 1
56. GPMFC GP ZL GPMFC Step 1 Initialization Step 2 Evaluation oe 1 input data Y f X X Sen EE ee Transformed data Tel 31 24 51 23 23 11 15 _ i 23 a mI 33 a m B Y 0 Evaluate the number 100 of overlapping area Fig 1 Step 3 Selection
57. 1 SD 3 5 3 1 Fig 3 a Fig 3 b 1
58. fNIRS FNIRS ICA 1 ICA 0 PCA Principal Component Analysis 2 ICA FastICA ICA fNIRS 3 fNIRS fNIRS ICA NIRS ICA 100
59. C3 C4 FCI FC2 FC5 FC6 COP1 CP2 4 LE 15 4 4 Fig 8 10 Fig 9 Fig 10 80
60. Wiener Hig 2 Wiener 6 P w Pela Pale i P w fNIRS rln Py w wn CH 1 CH LA G w HSW X e Y X Eig 2 Wiener 4 ASNR6 ASNR ASNR SNR SNR 7 7 2 SNR 10 logio z 8 O 9 2 NIRS ge e n ow No OJ e Q N NO Se e n x n 2 n 10 a n fNIRS x n
61. T FR 1 Vol 108 No 52 pp 19 24 2008 2 SRC AUT Vol 105 No 304 pp 43 46 2005 3 Vol 3 No 1 pp 14 15 2001 4 2012 5 Vol 103 No 135 pp 31 36 2003 6
62. k BO EX
63. 7 30 White noise 3 30 1 4 4 1 Fig 2 1 30 2 3 HE Group A Group B 2 4 2 2 2 BOLD Oxy Hb fNIRS A Oxy Hb
64. COR 512 1 2 2 3 99 2 1 5 7 6 a i e 3 1 8 13 16Hz 13 30Hz 4 4 1 22 24 1 19 20 DYE 4 2 Polymate AP1532
65. Wiener CH CH Wiener NIRS fNIRS ICA 2 3 CH11 2 CH11 KY T Ex as 9 FE 30 5 8 s s a b Fig 7 INIRS Table 1 ASNR CH11 ASNR amp CH ASNR 7 24 3 84 13 53 12 30
66. DLPFC DLPFC DLPFC DIZ DLPFC 6 RST DLPEC DLPEO LC NIRS 7 9
67. Fig 4 4 4 13 16Hz 13 30Hz u WORE 8 12Hz SVM SVM A 2 4 Fig 3 Fig 4 Sele loge 13 0 1 2 3 4 5 6 7 8 9 10 11 12 13 s Fig 5 2 1 8 pg 1 4 28 28Co 378 FFT 4 fold 5
68. fNIRS functional near infrared spectroscopy 2 5 2 1 HE Fig 1 a 2 2
69. VAS Blue Red Red Blue Blue VAS 4 2 Fig 7 DLPEC dorsolateral prefrontal cortex IFC inferior fro
70. 2 2 2 c 1 Fig 6 1 88 bee 2 a rT 4 HEYS 2 Eig 7 2 5 Mani aaa ia 2
71. KiS8 18 3 KiS8 18 POMS Profile of Mood States 3 2 Presentation 500 Hz sin 100 ms ma Cla 30 150 RELA 50
72. 13 14 3 3 White noise 3 EV 60dB 2 60dB 2 80dB fNIRS 48dB dB 20l0og 10 X 2 28 30 ay est 8 4 435 18275 30s 150s 330s 30s EF 3s 1s 7s White noise a b Fig 1 3 2 fNIRS ETG 7100 Hitachi medical Co Japan 22OH 24CH EL
73. 1 2 1 2 7 Class 1 class2 fitness 2 fitness 2 OO 000000 1 gt range of class1 range of class2 Fig 7 2 3 3 2 2 2 A 1 2 8 Class1 Class 2 a Po 0 5 rot Q transformed feature value transformed feature value evaluate only first class evaluate only second class feature2 featurel Identify transformed feature of two dimensional Fig 8 2 4 wrapper approach G
74. N N 2 2 CUT 6 1 Trig 7 b Table 2 GA parameter in track bar and select individual Parameter Value Population size 8 Crossover method BLX a a value 0 33 Selection method Roulette Crossover rate 1 0 Mutation rate 0 33 71 Table 3 115 SD 99 0 14 0 6 0 4 0 2 The average value in total of three parameters The average value in total of three parameters O ea 0 0 0 0 0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90 Generation Generation a b Fig 7 6
75. 12 ws a wj Cane dave 11 3 3 1 GNG GP 200 Fig 3 400 x 300pixel 2 GP 100 x 100pixel 12 13 3 L 9 GNG amp GP GCE Global Consistency Error 4 GCE 7 0 Vp
76. 1 3 Oxy Hb Oxy Hb 2 Oxy HDb 0 Oxy Hb 3 Oxy Hb 4 Oxy Hb 1 Eig 1 1 Go 1 0 sec Go 1 0 1 9 sec Nogo rest 30 sec rest 30 sec Fig 2 response error error response time ms the number of angles Fig 3 4 4 1 Fig 3
77. Table 1 1 OS debian7 5 64bit Kernel 3 2 0 4 amd64 CPU Quad Core AMD Opteron tm Processor 2356 1 2GHz Core 4 Memory 8GB 81 Fila Upload Galery Ext Search MISLArchive Uplaod Select file Pigs 3 1 1 Exif Exif Exif JPEG HE CHS DICOM Exif TIFF Image File Directory IFD IED Fig 3 Directory Count Directories Offset to Data Offset to Next IFD Fig 3 IFD 1 IFD 1 IFD
78. functional Magnetic Resonance Imaging fMRI L REPRO 2 FER fMRI 2 2 1 RO
79. MaPea Multiple analogy Parts extracting algorithm SVM Suport Vector Machine B 2 MaPea Multiple analogy Parts extracting algorithm MaPea 2 MaPea fNIRS 1 oecY7 7 vecQ cos 0 1 1 vec Y xXvec Qj 1 vec 2 h vec Qj 1 2 cosh j S i 1 j 1 cos i cos0 gt a S i 1 j cos0 1 1 co0s
80. 60 2 10 2 50 3 4 fNIRS Oxy Hb 1 0 Hz 5 0 s 4 4 1 A Fig 3 1 A
81. Vol 11 pp 1 64 2003 Vol 19 pp 137 162 1990 tre C Vol 77 No 783 pp 3962 3967 2011 35 35 2014 06 28 H RF Ayame MASAZUMI Aik 3 8 1
82. GP Gini 3 1 GPMFC 1 2 2 4 2 Class1 class2 eo ee e 00000 class overlapping region class overlapping region distribution with low identification distribution with high identification Fig
83. 2 K 2 1 distractor target E
84. gw 28 A pp 2D1 3 2014 D Martin C Fowlkes D Tal and J Malik A database of human segmented nat ural images and its application to evaluating segmentation algorithms and measuring ecological statistics EEE International Conference on Computer Vision Vol 2 pp 416 423 2001
85. 10 20 EK 1 002 10 s 23 6 1 87 C 58 4 3 9 A WIRA 4 4 22 6 IWE 0 5 3 3 Fig 1 a Fig 1 b 1 30 16 B 2 FX 8 3 1
86. 2 e e ICA E gt z Fig 1 ICA 2 2 TCA fNIRS ICA fNIRS CH 7 ICA ICA ICA fNIRS
87. a haar web OpenCV WP web Fig 2 1 1 A 1 web 1 AOR 4 LAN Table 1 web OpenCV
88. 1 1 C3 C4 FC1 FC2 FC5 FC6 CP1 CP2 4 Fig 1 2 1 FFT FFT COR FFT 512 FFT 1 FFT 2 4 99 FFT Fig 2 T A B
89. 2011 NIRS 2009 etg 7100 ver 3 05 nirs Vol 2 No 2 pp 59 72 2010 OGAWA S Brain magnetic resonance imaging with contrast dependent on blood oxygenation Proc Natl Acad Sci USA Vol 87 pp 9868 9872 1990 2011 NTS inc 2007 OGRE ERKE Vol 2007 No 15 pp 1 6 2007
90. 15 3 1 65 0 50 0 0 0 55 0 5 N CN Q 0 60 2 2 10 0 0 65 D J 1 5 0 70 U3 5 2 5 2 0 1 5 1 0 0 5 0 0 2 5 2 0 1 5 1 0 0 5 0 0 feature1 feature1 Fig 13 Ionosophere Fig 14 Ionosopehre feature2 feature2 MD 20 5 0 0 0 5 1 0 To 0 8 0 6 0 4 0 2 0 0 feature1 feature1 Eig 15 Spect Fig 16 Spect 16 Wisconsin Breast Cancer Ionosophere 11 13 3 1 12 14
91. 2 2 fNIRS 10 Hz 1 0 Hz Oxy Hb 3 2 2 CH A Fig 6 A a NIRS 0 30 1 0 20 Oxy Hb a u H 0 00 p b Fig 6 fNIRS j A bere 1 J i Boot model a DLPFC b IFC Fig 7 ROI 25 4 4 1
92. B Elk C 5 C 3 J 2000 3 5 fa rf E 5 8 gt 8 0 20 40 60 s Fig 6 ai Y ais DS Fmt gt C mm Er Fig B E tk
93. AMRIT fNIRS 2 RST RST
94. 0 001 3 A High Middle Neutral Fig 2 Fig 4 Fig 2 A High OAM RICHES FL Ode Fig 3 A Middle Table 1 Axial GE EPI T2 FOV mm 192 TR ms 150 TE ms 40 mm 5 RCI 20 lStatistical Parametric Mapping Matlab 56 ma a ed gt 4 Bae OF B F lt amp ciate L R a L TF ARCB el qr Fig 2 High A Fig 3 Middle A B F Cai k OT R BER F Fig
95. 1 SD Fig 2 2 2
96. Ayumi OHMURA MRI 4 1 Brain Machine Interface BMI 2
97. D KO Ek 9 NIRS 39 4 2 F 2 8 4 24 p gt 05 F 2 8 2 82 p gt 05 F 2 8 9 31 p lt 05 F 2 8 22 95 p 05 B Fig 8 CH7 10 CH5 6 10 F
98. Sub 4 5 2Group 3 Group A Group B Group A Oxy Hb A Oxy Hb A Oxy Hb 2 Lag 0 7 Lag 10 10 2 CH Grounp j 60dB 80dB 80dB 60dB 60dB 60dB 80dB Table 1 33 Sub 1 Sub 2 Sub 3 Sub 4 N AOxy Hb mmol mm ooo
99. 17 18 19 2 A Oxy Hb 2 2 3 A Oxy Hb HA A Oxy Hb 21 A Oxy Hb 4 2 1 A Oxy Hb 70CH A Oxy Hb Fig 3 CH CH 0 9 CH
100. lt 1 K TSUBOTA and S HATO Corneal Disease and Regenerative Medicine TRENDS IN THE SCIENCES Vol 15 No 7 pp 8 13 2010 2 N KOIZUMI Sakamoto Y Okumura N Tsuchiya H Torii R Cooper LJ Ban Y Tanioka H Kinoshita Cultivated Corneal Endothelial Transplantation in a Primate Possible Future Clinical Application in Corneal Endothelial Regenerative Medicine Cornea Vol 8 No 27 pp 48 55 2008 3 T Hiroyasu K Uehori U Yamamoto M Tanaka Construction of an Interactive System Aims to Extract Expert Knowledge about the Condition Cultured Corneal Endothelial Cells Proceeding of IEEE international conference on systems man and cybernetics pp 1805 1810 2013 4 J CHOOBINEH R J VOKURKA and L VADI A prototype expert system for the evalua tion and selection of potential suppliers International Journal of Operations and Production Management Vol 16 pp 106 127 1980 5 H S KIM and S B CHO Application of interactive genetic algorithm to fashion design Engineering Applications of Artificial Intelligence Vol 13 No 6 pp 635 644 2000 78 6 S B CHO and J Y LEE Emotional image retrieval with int
101. 3 Medium High Low 12 7 1 7 12 3 6 5 2 High High 53
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103. PRK Fua INOUE White noise 3 White noise Boot Strap A Oxy Hb 1 D
104. Fig 7 2 20 1 0 sx 0 5 E A MA Pe NO KR ee oO a NO 2 E E SF oF lt QS QS Fig 6 E A E N y A 9O R NS NS rs NS S AS Q Q Qe Q lt Qe Q Qe Qe Fig 7 5 5 1 SE Fig 3 leader follower Hig 5 3 9 leader leader 5 2 2
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106. GO NOGO Task Reaction Time RT 2 2 1 D 2 2 GO NOGO Task GO NOGO Task Fig 1 2 GO NOGO GO NOGO 2 GO NOGO NN a NN Time Time RT Fig 1 GO NOGO Task 2 3 Reaction Time RT RT
107. 9 8 NIRS functional Near Infrared Spectroscopy ETG 7100 8 5 5 2 2 3
108. CH 5 AD 3 fNIRS
109. 30 mm 10 30 mml CHS NIRS fNIRS 3 3 1 White noise 3
110. Fig 1 b AO 3 3 1 o 4 3 o INIRS 1 a e a b Fig 1 10 s 30 s 60 s 50 s Fig 2 3 2 4 1 22 23 mW fNIRS ETG 7100 10 20 19 5 24 4 C 28 41 3 3 Fig 2 30 A
111. T GP 2 1 M Inaba Contact lens wear and corneal endothelial cell loss Journal of the eye Vol 26 No 2 pp 187 192 2009 2 N Koizumi K Nishida S Amano and S Kinoshita Progress in the development of tissue engineering of the cornea in japan Journal of Japanese Ophthalmological Society Vol 111 No 7 pp 493 503 2007 nn 2010 4 M D Abramoff P J Magalh es and S J Ram Image processing with imagej Biophotonics international Vol 11 No 7 pp 36 42 2004 5 A E Carpenter T R Jones M R Lamprecht C Clarke I H Kang O Friman D A Guertin C H Joo L A Robert J Moffat P Golland and D M Sabatini Cellprofiler image analysis software for identifying and quantifying cell phenotypes Genome Biology Vo
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114. GP Growing Neural Gas GNG GNG GP 2 Neural Gas NG Fig 1 NG LAL NG
115. GP GP filter approach wrapper approach 2 filter approach wrapper approach baseline 1 293 Neural Network NN WX Decision Tree DT SVM Support Vecter Machine SVM 5 6 NN SVM SVM 7
116. ee FU J Fig l 3 3 1 RST NIRS RST LBL fNIRS functional Near infrared Spectroscopy 3 2 RST
117. 38 mn 8 0 silence ir z 0 6 pinknoise gy 7 5 _ Whitenoise th X 7 0 E 0 3 2 ir 6 5 T 00 T G a a 0 silence Dinknoise whitenoise 0 100 200 300 400 s Fig 6 8 0 AL e silence E 0 6 pinknoise g 7 5 whitenoise M 7 0 03 MI T 00 3 6 0 0 3 oOo 100 200 300 silence DInknoise whitenoise s Fig 8 Fig 6 Fig 7 4 4 1 3 1 KO O 9
118. Fig 4 3 3 2 10 Hz 1 0 s MAXa MINa MAXv MINy pi 05 pi 05 4 Fig 7 rIFG right Inferior Frontal Gyrus aA Ol Eig 5 High VisuaLmain Auditory main Low 4 High RTa RTv
119. mapa a s saa AJJ Fig 1 OpenCV OpenCV Intel API 9 3 3 1
120. 4 3 Fig 5 X High 5 Bs 5 1 2
121. 2 E 2 1 2 19 pp 115 120 2007 2 43 3 KiSS 18 2007 A POMS 2006 5 N Konoike Y Kotozaki S Miyachi C M Miyauchi Y Yomogida Y Akimoto K Kuraoka M Sugiura R Kawashima K Nakamura Rhythm information represented in the fronto parieto cerebe
122. R T pi R O p 12 GCE GCE 13 1 0 GCE R T pi RCO p R T Di 86 E T O pi 12 ta SS H ST Xo rae 2 4 r y SA Fig 3 GCE T 0 mw1 YO BoA XD B T pi 13 GNG Table 1 Table 1 GNG 12500 5 25 0 4 2 0 10 3 2 Fig 3 Image1 Fig 4 Image2 Eig 5 Image1 9 4 GP
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124. 4 fNIRS MaPea CORR R CH 3 14 CH 8 14 CH 11 18 8 8 0 1 fNIRS Vol 16 No 1 pp 34 46 2011 2 Tms HIP Vol 113 No
125. Yuji NISHIMURA 1 PERO Msi CHADS 2 3 I
126. 2 2 1 Fig 1 FEL 4 functional Near Infrared Spectoroscopy fNIRS 2
127. Lag 3 Group A CH 23 24 kot Group B CH Group A CH White noise 80dB CH 3 Group B 60dB W
128. 10 VAS OUR 10 10cm X X b Blue c Red Fig 1 Table 1 K lux White 4535 32 1 639 11 4 Blue Rest Visual Search Task Rest 7056 20 9 971 26 6 ne Red 3053 31 4 305 1 7 Fic 2 BRB x x iR Fig 3 VAS 23 2 2 3 fast10 Fig 4 p gt 05 L fast10 B A F 2 11 3
129. 5 ee Se 42 3 3 20 NIRS NIRS ETG 7100 3 72 21 3 24 5 C 47 52 CHS 3 4 NIRS NIRS RST 4 4 NIRS DLPFC DLPFC
130. BOLD Blood Oxygenation Level Dependent Hb 3 3 1 3 2 11 23 5 0 5 AAS GO NOGO GO NOGO 2 Fig 2 30 l 600 s 1000 Hz GO GO 1000 Hz NOGO OI NOGO 1100 Hz 30 sec 600 secl 30 secl lt gt a gt lt gt Rest Visual and Auditory GO NOGO Task Rest gt Time GO NOGO 7 gee Y 1000
131. Z Fig 5 Fig 6 Fig 5 Fig 6 voxel Z voxel A High MRETI O AIRIS D Z 0 D 0 High Middle High Neutral Z 4 High BBE RICHI LK EB ZBI 4 3 High 57 Z High Z H
132. The Japanese Journal of Experimental Social Psychology Vol 32 No 1 pp 27 33 1992 7 C Vol 131 No 1 pp 70 75 2011 8 Vol 75 No 653 pp 561 568 2010 35 2014 06 28 H Nozomi MASHIMA RST 2
133. White noise 60dB 80dB 2Group 3 Group A Group B Group A Oxy Hb A Oxy Hb Lag 0 7 Lag 10 10 2 CH ij Group Group A CH 60dB 80dB Group B CH 80dB
134. DICOM Exif Exchangeable Image File JPEG 2 Fig 1 1 2 3 4 5 a FA DEHN T NN
135. Fig 2 30 90 distractor a L target IT L 40 distractor target distractor L 0 90 180 270 target OD T14 90 270 90 m 270 n Presentation 3 Fig 3 VAS Visual Analog Scale 2 2 2 e 3 fast10
136. GNG 6 GNG 84 o ee aN NAM 4 why PC 2 19 Fig 2 ES 2 20x ee K means 1 2 GNG wi 2 EK Oi GNG
137. Oxy Hb Deoxy Hb Hb Oxy Hb Deoxy Hb Hb dh y NH 50 2 2 GO NOGO GO NOGO GO NOGO GO NOGO GO NOGO 800 ms NOGO GO GO NOGO 3 GO NOGO
138. en Yes No Yes gt 1 2 3 4 5 6 7 8 fie 2 2 3 10 10 23 1 A 1 CHS 22 3 25 6 C 49 57 11 17 2 4 ASI Audio Technologies EMCPU1 0G 10 20 NIRS Fig 1 1 30 8 3 7 2
139. g tec g GAMMAbox 1kHz AD 16 10 20 10 A1 A2 AFz 28 Fig 3 4 3 1 Fig 5 5 1 25 1 10 1 20 1 130 CHS EK 5 100 70cm
140. UCI Machine Learanig Repogitory 4 3 DOF 10 NNN filter approach 2 wrapper approach a c 2 ana filter approach SVM wrapper approach SVM RBE KO y 2 GP filter approach GP GP filter approach GPMFC GP
141. Vol 56 pp 97 102 2003 35 2014 06 28 fNIRS ACE Yuka NAKAMURA fNIRS FNIRS ICA Wiener fNIRS 1 fNIRS functional Near Infrared Spectroscopy fNIRS
142. 10 50 oxy Hb 5 2 Fig 3 NIRS Fig 4 45 51 4 6 4 NIRS Fie 5 NIRS Fig 5 1 5 CH22 CH20 CH21 2 5 2 55 2 55 0 0 0 0 0 0 2 5 2 5 2 5 CH18 30 90 140 CH17 30 90 140 30 90 140 2 5 2 5 1 2 5 2 5 0 0 0 0 j 0 0 j 2 5 1 2 5 2 5 30 90 140 CH13 30 90 est CH12 CH1 1 2 5 2 55 2 5 0 0 a 0 0 2 5 2 5 2 5 30 90 1 30 90 30 90 140 CH6 30 90 140 CH5 2 5 2 5 0 0 0 0 oO N w A I w oO aJ u w w J Mn on j 4 30 90 Ho 30 9 90 3
143. 30 150 50 l Neurobehavioral Systems http www neurobs com 19 SE ms eS 2 NUU DX o oF QA OF oF 9d lt QF QF QM QM QM Q Q Fig 3 SE 50000 40000 30000 im 20000 10000 Fig 4 SE 80 60 E leader O o 40 H follower 20 0 YA es gd GH gat Fig 5 KiSS 18 4 4 1 SE Fig 3 1 SE B leader SE RRE A D leader 3 4 5 9 A leader B follower S71 2 6 7 8 B leader WIRA A A follower
144. 8 75 3 3 Fig 4 b 2 2 PP FT
145. 2 GPMFC 1 2 1 2 wrapper approach SVM RBF 2 GP 1 Celia C Bojarczuk and Heitor S Lopes and Alex A Freitas and Edson L Michalkiewicz A constrained syntax genetic programming system for discovering classification rules application to medical data
146. 2014 6 28 H Ideya SUGITA GO NOGO FNIRS 7 5 IFC Medium FP Low 1 Lax 1 5 X 5
147. 3 ul gt Qo CH AN sak ee JON RECS Qa gt Qe H H z H 2 Be L Ze Fe 0 Hs ADA Qa 10 10 1 Lag gt Qa Lag Group gt Lag 1 J 1 7 1 10 10 CH FF gt Hs 4 CH H Qa z H 32 4 2 4 4 2 3 A Oxy Hb CH Fig 5 CH Table 1
148. b fast10 30 white 235 f sblue 20 Bred a 19 l s 6 A 6 D Subject 1000 10 1000 10 T 800 8 500 I 600 6 600 400 4 ir 400 S gt Dy 200 200 2 ed id 0 0 0 white blue white white blue red white blue red Fig 5 VAS fast10 24 3 1 9 3 2 3 2 1 fNIRS fNIRS ETG 7100 14 16 4 23 22CH 10 20 white 4564 78 K BREE 578 16 lux
149. 8 12Hz FIR 11 1 BMI 1 D 1 3 3 1 EEG FFT
150. Tim c 6 Celt gt 6 c g 7 10 aS 6 welt w a t w t 1 7 N t 1 N t 4 1 8 C t 1 CA 9 C t 1 S 10 Step 6 t lt Tyme 1 Step 2 GNG 1 5 2 dave eu 11 dilation erosion
151. 0 8 0 7 Fig 0 9 Group A Group B CH 0 7 CH PMS NM 30 Sub 1 Sub2 Sub3 Sub4 2400 2400 2400 2400 1800 1800 1800 1800 me ES im EX 1200 EN 1200 by 1200 by 1200 600 600 600 600 0 0 0 0 206 114 22 70 162 206 114 22 70 162 206 114 22 70 162 206 114 22 70 162 60dB 80dB a Group A Sub 1 Sub2 Sub3 Sub4 2400 2400 2400 2400 1800 1800 1800 1800 m Fa 5 B 1200 amp 1200 1200 1200 600 600 600 600 0 0 206 114 22 70 162 206 114 22 70 162 206 114 22 70 162 206 114 22 70 162 Lag Lag Lag Lag 60dB 80dB b Group B Fig 4 Lag 4 2 2 Lag 2 2 5 6 22 T A Oxy Hb Lag k Lag Fig 4 Lag 100 100 0 1 fNIRS 10 10
152. IE 1 4 3 5fold crossvalidation FF filter approach SVM wrapper approach SVM RBF 64 5 2 1 GP CONF X Y ls filter approach wrapper approach Table 1 GP IT A 5 3 filter approach 5 3 1 2 2 SVM 3 Ionosophere 10 1onosophere 5
153. 2 5 5 1 RST 5 NIRS 5 1 5 RST Fig 2 Reading Read and remember word Answer Reading ll ox 1 lt Rest Task Rest 60 S 220 280 s 60 S Fel LA 60 BAD 1 5 ARO SAN Sa
154. 5 1 Fig 6 8 13 16Hz Fig 6 20 12 Fig 7 8 13 30Hz Fig 7 20 15 Fig 8 Fig 8 20 9 8 13 16Hz 30 s s e Bt ov a R s 40 A B C D E F G H I J K L M N P Q R Fig 6
155. 19 Oxy Hb fNIRS BOLD Oxy Hb Oxy Hb 5 6 COE 2 3 fNIRS fNIRS 1 EX fMRI functional Magnetic Resonance Imaging 12 fNIRS 700 900nm
156. Wisconsin Breast Cancer 0 9 Spect EK 2 WBC Spect EK 10 Table 2 2 Wisconsin Ionosophere Spect 92 3 1 81 77 08 8 92 79 46 2 32 19 24 5 3 2 2 11 13 15 3 Wisconsin Breast Cancer Ionosophere Spect Heart 2 SVM 12 14 16 3 2 CN CN p ap ap Y 0 6 Q q o 0 8 id a 0 004 0 02 0 00 0 02 0 04 z230 25 20 i5 10 5 0 0 feature1 feature1 Fig 11 Breast Cancer Fig 12 Breast Cancer Spect Heart
157. fNIRS ASNR 5 fNIRS 5 1 1 22 NIRS ETG 7100 TSND121 ATR Promotions 10 20 3x5 22CH fNIRS 10 Hz 24 8 71 9 EV 830 KZ 60
158. filter approach b classl 0 7 class2 feature2 NJ Wu P un 240 0 1 0 2 0 4 0 5 0 3 featurel Fig 17 Spect wrapper approach 6 GP 2 filter approach wrapper approach 2 3 filter approach 2 wrapper approach filter approach D 2
159. Step 4 Crossover 2 RR 2 Step 5 Mutation 60 3 Step 6 Terminal Criterion Step 2 Step 5 GPMFC 0 Pi AP2 O hast n a O O _ O C C T T rc y I L J L cross pont 2 g Q i aioe A C1 ae mutate point lt O m O J 0 E EE E oO gt 0O O Fig 2 Fig 3 3 filter approach GP filter approach filter approach
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163. A Oxy Hb Sub2 Sub4 A Oxy Hb CH 23 A Oxy Hb Sub1 Sub3 A Oxy Hb NIRS MRI Magnetic Resonance Imaging 30 NIRS Group B Sub4 Sub1 Sub2 Sub3 Oxy Hb A Oxy Hb Group B CH 25 26 Sub 1 60dB Oxy Hb Sub2 Sub3 A Oxy Hb White noise Sub 4 Sub 40 A Oxy Hb
164. een ww Neutral image Fig 1 Objects 4 Landscapes 5 People 2 4 7 Fig 1 30 6 12 3 1 10 1 1 FRAC 2 2 5 MRI ECHELON Vega 1 5 T 23 C Table 1 2 6 SPM8 Realign Slice timing Coresister Normalize MNI Smoothing C FWHM 8 mm
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168. 32768 APP1 TIFF 32768 IED Undifined 3 2 DICOM DICOM Exif JPEG JPEG 2816 x 2112 B DICOM
169. Deoxy Hb SSS Oxy Hb A Oxy Hb CH 2 1 29 Sub Sub2 Sub3 Sub4 30 30 30 30 0 aa z 20 gi 20 10 o o o 0 0 0 0 12345678 12345678 12345678 12345678 60dB 80dB a Group A Sub Sub2 Sub3 Sub4 30 30 30 30 20 z 20 z 20 z 20 10 10 10 8 10 0 0 0 0 1234567 8 12345678 1234567 8 12345678 60dB 80dB b Group B Fig 2 Group A Group B fe ay 200 200 Pore tira 150 150 ey B 10c B 10 100 Bs 100 posses ICH1 22 50 50 A 1 M 0 0 La EEN 09 O8 07 09 08 07 eal CH1 24 Yo E 6OdB E 80dB a CH b CH Eig 3 Oxy Hb
170. FIR Finite Impulse Response 5 1 BMI 1 FFT Fast Fourier Transform FFT 97 97 FFT 1
171. FULT 1 2 SVM RBF 2 10 A B A i featureB j A i fitness mean of acc A i B Fie 10 5 filter approach wrapper approach 5 1 Wisconsin Breast Cancer 24 420 2 1 1 Ionosophere 350 2 1 2 Spect Heeart 256 2
172. ON Oxy Hb mmol mm o ooo O N Ww AOxy Hb mmol mml 6 oo onarNnNORN BE AOxy Hb mmoFmm NON NND 0 50 100 150 200 0 50 100 150 200 0 50 100 150 200 0 50 100 150 200 s s s s 60dB 80dB 80dB a Group A Sub 1 Sub 2 Sub 3 Sub 4 0 8 0 6 0 4 0 2 AOxy Hb mmol mm MhNONAD AOxy Hb mmoFmm o AOxy Hb mmol mm oo N O NWHE AOxy Hb mmol mm 0 2 _ 05 4 3 2 1 5 0 0 1 2 0 50 100 150 200 50 100 150 200 0 50 100 150 200 ices 50 100 150 200 s s s s 60dB 60dB 60dB EMITE 80dB b Group B Fig 6 Boot Strap Group A CH 60dB 80dB Group B CH 80dB Group A CH Group B C
173. ee 2 7 ICA Wiener fNIRS D 1 NIRS 2009 2 near infrared spectroscopy Journal of Rehabilitation Vol 8 pp 21 24 2008 3 Vol 12 No 2 pp
174. wrapper approach 9 20 21 22 filter approach wrapper approach GPMFC Genetic Programing Multiple Feature construction GP filter approach GP GPMFC
175. 2011 PUR Vol 103 No 135 pp 31 36 2003 Vol 198 No 4 pp 285 290 2001 HERC RUE SIRNA TP ABR Vol 26 pp 95 102 2012 Haak Vol 47 No 1 pp 29 37 2007 3 MBE2004 15 pp 9 16 2004 C Vol 131 No 1 pp 70 75 2011 Tsukasa Funane Jit tli2 gt Development and application of noncontact near infrared spectroscopy system for measuring biological tissue 2012
176. 22 3 LOL FORBP AT Mozart White noise 3 T
177. 4 30 _ 30 QOQ OU A W N As is NNN i 3 Fig 2 VAS Visual Analogue Scale 10cm 0 10 2 5 NIRS et 50 10 50 1 6 7 10 50 37 m 10 Sa Pst Aik SE n 10 E zo l PN th x 1 6 5 Ty silence pinknoise whitenoise Fig 3 10 m BILZ 8 om 10
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181. Fig 5 ea CH A Oxy Hb BootStrap 28 29 A Oxy Hb Fig 6 BootStrap os 4 A Oxy Hb 2 2 BOLD A Oxy Hb 2 DED CH A Oxy Hb Oxy Hb BootStrap A Oxy Hb CH 4 2 A Oxy Hb A Oxy Hb oe A Oxy Hb Group Group A A LL Subl Sub3
182. WNAI fmri functional magnetic resonance imaging 1 2011 nirs a Vol 30 No 4 pp 197 203 2006 zl Vol 33 No 1 pp 103 105 2013 HER nirs Vol 55 pp 226 231 2003 SP Vol 100 No 725 pp 15 20 2001 Vol 21 pp 143 154 2008 EEF HREH EEF FAR EHEJ SIEM att
183. 16 2011 2 NIRS WE MEDIX 39 4 10 2003 3 RITE BER B OJA Sternberg 47 1 64 69 2009 4 M Daneman and P A Carpenter Individual difference in working memory and read ing Journal of Verbal Learning and Verbal Brheavior 19 4 450 466 1980 5 ERMET EREI The Japanese Journal of Psychology 65 339 345 1995 6 A Baddeley The episodic buffer a new component of working memory Trends in Cognitive Sciences 11 4 417 423 2000 7 2008 8 FRET 2000 9 N Osaka M Osaka H Kondo and M Morishita The neural basis of executive function in working memory an fMRI study based on individual differences Neurolmage 21 623 631 2004 10 A Villringer J Planck C Hock and L Schleinkofer Near infrared spectroscopy NIRS a new tool to stydy hem
184. 4 Neutral A 19 0 4 m High m High 0 3 m Middle 0 8 oe 02 N 0 4 01 0 2 0 0 A B i D A B C D Fig 5 MWIKO Z 1E Fig 6 Z Fig 4 A Neutral RA A High 4 3 Middle 4 2 Neutral A REFAIRE COo REA 4 3
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186. Hel 1100 He pad y Y Y NUNEN Le gt Time RT Fig 2 0 5 s 1 0 3 0 60 dB 3 3 3 3 1 RT RTyv RT RTa 10 sample Fig 3 RT MAXa MAXy RT MINa MINy Fig 4 4 C RT a iii MAX a MAX RBS Mk 3 E S 0 0 a a MIN ay MINw Time 2 Time Fig 3 3 SE n 11 3 SE n 11 E E e 0 gt 0 p MAXa MIN a MAX v MINw Ave 693 4 ms Ave 443 3 ms Ave 565 6 ms Ave 445 9 ms a b
187. SE Fig 4 2 4 2 KiSS 18 KiSS 18 Hig 5 SE BT leader follower 4 3 2 2 CH5 14 19 CH2 7 12 CH9 18 22 CH3 7 11 CH 2 r OR y B Fig 6 Fig 7 Fig6
188. sets Artificial Intelligence in Medicine vol 30 2004 pp 27 48 Antonio Moreda Pieiro Andrew Fisher Steve J Hill The classification of tea according to region of origin using pattern recognition techniques and trace metal data Journal of Food Composition and Analysis Elseveir April 2003 pp 195 211 Buntine Wray Learning classification trees Statistics and Computing vol 2 June 1992 pp 63 73 4 G E Hinton and R R Salakhutdinov Reducing the Dimensionality of Data with Neural Networks Sci ence Vol 313 No 5786 pp 504 507 2006 5 V N Vapnik Statistical Learning Theory Wiley 1998 2 na Ww 67 6 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 F Otero M Silva A Freitas and J Nievola Genetic programming for attribute construction in data mining in Genetic Programming Lecture Notes in Computer Science vol 2610 Berlin Germany Springer 2003 pp 101 121 Frauke Friedrichs and Christian Evolutionary tuning of multiple SVM parameters Trends in Neuro computing 12th European Symposium on Artificial Neural Networks 2004 Vol 1 me 64 March 2005 Pages 107 117 J Koza Genetic programming on the programming of computers by means of natural selection MIT Press 1992 O Smart H Firpi and G Vachtsevanos Genetic programming of conventional features to detect seizure precursors Eng Applicat Artif Intel
189. 0 lt a cos0 1 gt a S i j S i j 1 cos0 1 1 co0s0 lt a cos0 1 gt a 2 2 1 7 1 1 otherwise 0 if S i j lt 0 3 T 69 3 1 10 1 22 23 fNIRS BTG 7100 10 20 21 1 23 9 C 28 46 30 sec 3 60 sec 50 sec b iO l P 2 sec lt 10 sec Fig 1 Fig 1 30 sec 9
190. 128 pp 67 72 2013 3 MARAE 2 fNIRS MPS Vol 2014 No 18 pp 1 6 2014 gt Vol 46 No 5 pp 225 230 2001 72 35 2014 06 28 H Hideyuki MATSUURA
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192. 49 30 90 140 CH2 30 90 140 CH1 30 90 140 s Oxy Hb DAD mM mml Fig 3 fNIRS 8 LS O 4 bX os mm a E 0 Fig 4 O 50 s 100 fNIRS s Fig 5 fNIRS C s Fig 6 ICA ICA Fig 6 INIRS Fig 7 a Fig 7 b ASNR Table 1 Fig 7 b Table 1 6 Fig 7 b Table 1 Wiener
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198. ML DICOM Digital Imaging and Communications in Medicine DICOM
199. P ORMEA HA RAC K A kal HAG wrapper approach SVM 4 1 3 SVM SVM GP 63 SVM SVM 9 SVM Y ms Y _ ot rr A r ve Fig 9 4 2 wrapper approach 2
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201. Vol 4 No 3 28 June 2014 Monthly Lecture Meeting HB 35 BABS EAL AEE a RR Published by the Medical Information System Laboratory of Doshisha University Kyotanabe J apan Medical Information System Laboratory The Monthly Lecture Meeting Contents Abs I tS AG amp FAL RIRI PE D AREE BUT S RAE EO RR fNIRS Ha fNIRS lt gt ae FNIRS WARS ES sat X SE KAA
202. e Visual main RTa RTv e Auditory main RTv RTa e Low RTa RTv High Visual main Auditory main Low RT RT High Low RT CORR Low RT SE n 11 la u Low Auditory Visual High main main Fig 6 IFG au Ne Fig 7 Low 5
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204. fNIRS 3 1 7 1 6 23 3 0 5 RR Chore 21 9 25 2 24 3 3 2 Fig 2 50 mm 5 mm 0 05 P VAL 1 0 GO GO NOGO 4 7 10 GO NOGO 1 0 1 9 GO NOGO GO NOGO 9 2 30 45 3 GO Enter 3 3
205. hite noise CH 25 26 60dB White noise NIRS 80dB CH E 31 PE ese BSB SB SB SBT SB SBE SESE ee eS SE Ss HSB SBT HSB SBE HSE SE eB eet eet ee SS a eo ee eee Se Ss 100 ee e see BSB SB SBT BSB mm SS SF Wy e eeeaeeeeeeeeeeeeese ees b 60dB c 80dB NN bE EE EE EE EN EN se wee eee ee eee eee EN Fig 5 gH ZH EE DS a oe White noise Qe Group 2 Fel 3 CH 2 RF Te ADNA Fe yH
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