fmri BCI(Brain Computer Interface) IAPS fmri(fnuctional Magnetic Resonace Imaging) Valence( ) Arousal( ) IAPS Valence ( ) 2 BCI 48 fmri SD(Semantic Di
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1 26 fmri Decoding of Human Emotion induced by Visual Stimuli using fmri Brain Signal
2 fmri BCI(Brain Computer Interface) IAPS fmri(fnuctional Magnetic Resonace Imaging) Valence( ) Arousal( ) IAPS Valence ( ) 2 BCI 48 fmri SD(Semantic Differential) SPM(Statistical Parametric Mapping) p t 2 SVM(Support Vector Machine) 5 80 i
3 BCI,, fmri(fuctional Magnetic Resonace Imaging), SVM(Support Vector Machine) ii
4 Abstract Decoding of Human Emotion induced by Visual Stimuli using fmri Brain Signal Hirokazu Takahashi Estimation of human emotion from brain activities has been studied. These studies aim at applications to BCI(Brain Computer Interface). A previous study shows that the relation between presented image of IAPS(International Affective Picture System) to human subjects and their emotion. The study uses fmri(functional Magnetic Resonance Imaging) to measure brain activities. It uses the regression analysis to estimate the emotion from the measurement data output from the fmri and indicators of valence and arousal of IAPS. The result shows that low scored value data of the brain activation level have no correlation to the emotion, however, high scored value data of the brain activation level have the possibility of correlation to the emotion. In this thesis, these high scored measurement value data are focused. In the experiment, human subjects are presented the IAPS image whose indicators of valence is high. The output brain activation data is expected as high. This study verifies comfort-discomfort images can be identifiable. Previous researches of human emotion show that some brain area plays an important role in human emotion. However, in the application to BCI, we do not need to care of the brain area of emotion because the only accurate classification is required. Therefore, in this research, the all brain area is used to perform machine learning. In the experiment, 48 comfort-discomfort images are presented at random to human subjects, we measure the subjects brain activity by fmri. After the experiment, iii
5 we do a questionnarie to the subjects using SD(Semantic Differential) method to select the subjective comfort-discomfort images of each subject to be used for analysis. The voxel values and their location in the brain are analyzed using t-test(significant level p < 0.001) to find the significant difference at the activation. The voxel values are learned by a machine learning, and are evaluated by cross-validation, and finally the accuracy of the recognition is calculated. The result shows that the subjective accuracy of comfort-discomfort images of 5 subjects has 80 in average. From the result, the comfort-discomfort feelings that induced from the visual stimuli can be estimated from the brain information. The result of this paper shows the possibility of the BCI which translates the human emotion. key words Brain infromation decording, Comfort, Discomfort, Human emotion, functional-magnetic-resonance-imaging, Support-Vector-Machine iv
6 fmri SPM SVM A B v
7 4.1.3 C D E A : 30 B 32 C 40 vi
8 (1 ) A B B C C D D E E A C.1 A p C.2 A p C.3 B vii
9 C.4 B C.5 C p C.6 C p C.7 C p C.8 C p C.9 C p C.10 C p C.11 C p C.12 D C.13 D p C.14 D p C.15 D p C.16 E C.17 E p C.18 E p viii
10 B.1 IAPS B.2 IAPS B.3 A B.4 B B.5 C B.6 D B.7 E ix
11 1 [1] [2] [3] [4] [1] BCI(Brain Computer Interface) fmri(functional Magnetic Resonance Imaging) [5] IAPS[6] fmri Valence( ) Arousal( ) IAPS Valence ( ) Valence ( ) Valence 2 SVM(Support 1
![Imaging) [5] IAPS[6] fmri Valence( ) Arousal(](/docs-images/41/22591940/images/page_11.jpg ") IAPS Valence ( ) Valence ( ) Valence 2")
12 Vector Machine) SVM 3 fmri SPM(Statistical Parametoric Mapping) SVM( ) 4 5 2
13 2 2.1 NIRS PET fmri NIRS (oxy-hb) NIRS [7] oxy-hb oxy-hb NIRS [8] 3
14 2.1 PET PET [9] fmri (MR ) fmri [10] BCI fmri NIRS PET 4
![) fmri [10]](/docs-images/41/22591940/images/page_14.jpg "BCI fmri NIRS")
15 [11] SVM(Support Vector Machine) SVM 1995 AT&T V.Vapnik SVM SVM 2 2 SVM 5
16 3 3.1 fmri fmri IAPS[6] fmri A ( 3 2 ) fmri MRI 6
17 3.1 fmri SIEMENS fmri MAGNETOM Verio3T 3T 1 45mT/m 1 200mT/m/ms (FoV) 50cm Neurobehavioral Systems Presentation[13] (International Affective Picture System: IAPS)[6] IAPS Valence Arousal Arousal Valence : Valence 7.0 Arousal 5.0 : Valence 3.0 Arousal 5.0 7
![4 Neurobehavioral Systems Presentation[13] (International Affective](/docs-images/41/22591940/images/page_17.jpg "Picture System: IAPS)[6] IAPS 700 1 9 Valence Arousal Arousal")
18 3.1 fmri Valence Arousal [14] B 3.1 8
19 3.1 fmri (5 ) 6 15 (5 ) 9 (3 ) 1 8 BOLD [5] (1 ) 9
![9 (3 ) 1 8 BOLD [5]](/docs-images/41/22591940/images/page_19.jpg "1 165 4 12 3.")
20 3.1 fmri IAPS [14] SD(Semantic Differential)
21 3.2 SPM 3.2 SPM SPM(Statistical Parametric Mapping)[15] SPM8 SPM fmri DICOM SPM Analyze MRIConvert[16] Analyze SPM8 Realignment Normalisation Smoothing Realignment fmri Normaliseation Realignment Smoothing fmri 6 (2 ) SPM5 2 (GLM:General Linear Model) GLM 2 (contrast) p t 2 11
22 3.3 SVM p MNI Talairach Daemon[17] SPM5 p t MNI Talairach Daemon 3.3 SVM 2 ROI SVM( ) BDTB1.2.2(Brain Decorder Toolbox)[18] libsvm-3.16[19] SVM(Support Vector Machine) 12
23 3.3 SVM SVM( )
24 4 4.1 SVM( ) IAPS SD B B ROI C D C IAPS 4.1 C E
25 4.1 SD IAPS IAPS
26 ( ) ( ) ( ) A 71(10/14) 83(15/18) 78(25/32) B 50(6/12) 85(17/20) 71(23/32) C 80(8/10) 78(11/14) 79(19/24) D 70(7/10) 92(13/14) 83(20/24) E 76(10/13) 90(10/11) 83(20/24) 4.2 ( ) ( ) ( ) A 62(15/24) 62(15/24) 62(30/48) B 54(13/24) 50(12/24) 52(25/48) C 75(18/24) 78(18/24) 75(36/48) D 70(17/24) 75(18/24) 72(35/48) E 58(14/24) 54(13/24) 56(27/48) ROI ROI t 16
27 A A p SPM 2 ROI 4.2 ROI 2 1. MNI (-12, 52, -8) 200 t MNI (-20, -66, -50), 29 t mm 4.2 A 17
28 B B p SPM ROI 3 ROI MNI (-42, 22, 52) 22 t ROI MNI (36, -58, 68) 48 t mm MNI (-4, -70, -30) 13 t 3.44 ( ) 4.3 B 4.4 B 18
29 C C p SPM ROI 2 ROI MNI (-18, -90, -44) 200 t mm ROI MNI (38, 32, -22) 96 t C 4.6 C 19
30 D D p SPM ROI 3 ROI MNI (-18, -58, 20) 24 t ROI MNI ( ) 200 t MNI ( ) 200 t D 4.8 D 20
31 E E p SPM ROI 4 ROI MNI ( ) 75 t 3.81 ( ) ROI MNI (-56, -12, 42) 200 t MNI ( ) 200 t 4.85 ( ) 45 MNI ( ) 200 t E 4.10 E 21
32 p SPM MNI (14, -34, 20) [20] 4.12 MNI (30, -34, 14) [21]
33 MNI (24, -12, -6) ( ) (24, 2, -18)
34 5 [5] SD IAPS 80 BCI 24
35 2 fmri SPM BDTB ITNews
36
37 3 5 27
38 [1],,,,,,, Mayer -,, Vol.25 (2004) No.1 P41-49, [2],,, Vol.25 (2005) No.3 P [3],,,, -, Vol.124 (2004) No.1 P [4],, Vol.58 (2010) No.4 P [5], fmri, 25, 2014 [6] Lang, Peter J., Margaret M. Bradley, Bruce N. Cuth-bert, International affective picture system (IAPS):Technical manual and affective ratings, [7],, 46, [8],,,,,, [9],, Vol.25 (2005) No.2 P , [10], : fmri, 28(1), 17-27,
39 [11], fmri, 24, 2013 [12],, [13] Presentation, neurobehavioralsystems, [14],,, ( ), [15] Statistical Parametric Mapping, [16] MRIConvert files LCNI, [17] Talairach Daemon,Research Imaging Institute of the University of Texas Health Science Center San Antonio, [18] Brain Decoder Toolbox, ATR-DNi, [19] Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:127:27,2011. Software available at cjlin/libsvm [20],,, 25 03,, [21],,,,,,, Vol.37 (2006) No.1 JANUARY P9-14,
40 A : A SPM p SVM( ) A.1 D
41 A.1 31
42 B B IAPS 48 IAPS Valence Arousal B.1 B.2 B.3 B.7 32
43 B.1 IAPS Slide No. Valence Arousal Slide No. Valence Arousal
44 B.2 IAPS Slide No. Valence Arousal Slide No. Valence Arousal
45 B.3 A Slide No. Valence Arousal Slide No. Valence Arousal
46 B.4 B Slide No. Valence Arousal Slide No. Valence Arousal
47 B.5 C Slide No. Valence Arousal Slide No. Valence Arousal
48 B.6 D Slide No. Valence Arousal Slide No. Valence Arousal
49 B.7 E Slide No. Valence Arousal Slide No. Valence Arousal
50 C C SPM5 p t Statistics 40
51 C.1 A p1 41
52 C.2 A p2 42
53 C.3 B 43
54 C.4 B 44
55 C.5 C p1 45
56 C.6 C p2 46
57 C.7 C p3 47
58 C.8 C p1 48
59 C.9 C p2 49
60 C.10 C p3 50
61 C.11 C p4 51
62 C.12 D 52
63 C.13 D p1 53
64 C.14 D p2 54
65 C.15 D p3 55
66 C.16 E 56
67 C.17 E p1 57
68 C.18 E p2 58
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