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1 NAIST-IS-MT EEG
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3 EEG,.., Positive, Negative.,,, Positive Negative Neutral.,,..,.,. (EEG: Electroencephalograph), International Affective Picture System, Self-Assessment Manikin.,.,,.,. EEG, Valence, Arousal, International Affective Picture System,, NAIST-IS-MT , i
4 EEG based emotion classification system considering individuality Tomoki Ariyoshi Abstract When we make an emotion classification system with physiological information, it is important to show appropriate stimulus to subject. In previous methods, the experimenter chooses stimulus set for all subjects based on previously investigated information. Subjects evaluate the stimulus and label an emotional class, such as positive or negative or neutral. However, as a result of subjective evaluation, subjects may have difficulty in perceiving stimulus and they classify most stimuli as the neutral class. Thus, we propose to make a subject-dependent stimulus set considering emotional individuality. When subjects watch this stimulus, we can get a correct emotional and physiological response. In this study, we propose a method of how to create a stimulus set considering individuality of each subject. We propose that subjects evaluate a large number of stimulus, and a stimulus set is made with this evaluation. Ambiguous stimuli, which may be labeled as different classes, we eliminated and the stimulus set is improved. We use EEG, Electroencephalograph, to measure physiological information, International Affective Picture System, as a stimulus set, and Self-Assessment manikin, as a subjective evaluation. We compare classifiers learned by a subject-dependent stimulus set in the proposed method with a classifier learned by a subject-independent stimulus set in the previous method. As a result, the accuracy of proposed classifier is higher than that of the previous method. Keywords: EEG, Valence, Arousal, International Affective Picture System, Support Vector Machine Master s Thesis, Department of Information Science, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-MT , March 13, ii
5 Contents SAM Valence, Arousal International Affective Picture System EEG Arousal SAM iii
6 SAM A. 42 A iv
7 List of Figures 1 SAM IAPS Neutral VS. Positive Neutral VS. Negative Low Arousal VS. High Arousal Valence, Arousal VS FDA NB SVM Neutral VS. Negative Low Arousal VS. High Arousal.. 34 v
8 List of Tables 1, 31 vi
9 ,, [1].,..,., [2], [3].,, [4]..,,., [5]..,, Negative Positive, [2, 6].,,,.,,
10 ,,.,,.,,,,..,,,,,. 1.3, 2.,,. 3,. 4.,,. 5,,. 6. 2
11 (Electroencephalogram: EEG),,,., EEG,. EEG, EEG., (Event-Related Potential: ERP),, basic emotions ( ) [7] 6 (,,,,, )., [8]. 2 dimensions of emotion ( ) [9],.,,., SAM Valence, Arousal Self-Assessment Manikin, SAM. Valence ( ), Arousal ( ), Dominance ( )., Valence Arousal, Semantic-Differencial [10]. Valence, Arousal, 1., 2.2 dimensions of emotion. 3
![Valence ( ), Arousal ( ), Dominance ( )., Valence Arousal, Semantic-Differencial [10].](/docs-images/42/22849277/images/page_11.jpg "Valence, Arousal, 1., 2.2 dimensions of emotion. 3")
12 Figure 1 SAM 2.4 International Affective Picture System International Affective Picture System ( IAPS) [11] [12]. 4, SAM, Valence, Arousal,. 2.5,.. D x = {x 1, x 2,, x D }. 4
13 2.5.1 x, c k p(c k x) P (c k x) = P (c k)p (x c k ) P (x) P (c k )P (x c k ) (1)., p(c k x i ). (MAP ). P (x c k ), P (c k ). x i, x j (i j), P (x c k ) = i P (x i c k ) (2). P (x i c k ) 1 P (x i c k ) = exp { (x } i µ ik ) 2 2πσik 2σ 2 ik (3). µ ik σ 2 ik c k i. P (c k x) c k c map, c kmap = arg max c k P (c k x) = arg max P (c k ) c k i P (x c k ) (4).,., { c kmap = arg max log P (c k x) = arg max c k c k log P (c k ) + i log P (x i c k ), c k. } (5) 2.5.2,. 5
14 ,. x, 6. w x y. y(x) = w T x. (6) c 1 m 1, c 2 m 2 7. m 1 = 1 x n, m 2 = 1 x n. (7) N 1 N n c 2 1 n c 2, w. m 2 m 1 = w T (m 2 m 1 ) (8) m 2 m 1 w,, w (m 2 m 1 ) (9). w,,,.,,. S W S B w. S B = (m 2 m 1 )(m 2 m 1 ) T (10) S W = (x n m 1 )(x n m 1 ) T + (x n m 2 )(x n m 2 ) T (11) n c 1 n c 2 J(w) = wt S B w w T S W w J(w) w, (12) w S w 1 (m 2 m 1 ) (13). w. 6
15 x, y(x) (, ), ϕ(x),, y(x) = w T ϕ(x) + b. (14). x w, b.,. y(x) = 0 x y(x) / w, arg max{ 1 w,b w min[t n(w T ϕ(x) + b)]} (15) n, w, b. w n, t n (w T ϕ(x) + b) = 1, arg min w,b 1 2 w 2 (16) subject to t n (w T ϕ(x n + b) 1 n = 1,, N (17) w, b..,,.,,. ξ n 0 (n = 1,, N),, ξ n = 0, ξ n = t n y(x n ). y(x) = 0 ξ n = 1, ξ n > 1. ξ n 7
16 17. t n (w T ϕ(x n + b) 1 ξ n n = 1,, N (18) ξ n 0. (19) N C ξ n w 2. (20) n=1 C > 0.,. C,. w, b. 2.6 EEG, [2]., Arousal, Arousal 2., IAPS,. IAPS Arousal, Arousal 50, 50,,.,. 3 [s],.,, 2 [s] 4 [s]. 6 [s],,. 8
![2.6 EEG, [2]., Arousal, Arousal 2.,. 2.6.1 2.4 IAPS,.](/docs-images/42/22849277/images/page_16.jpg "IAPS Arousal, Arousal 50, 50,,.,. 3 [s],.,, 2 [s] 4 [s].")
17 2.3 SAM. Arousal 0 4, 5. Arousal calm, exciting. 5, 2, 3. 2, 1 calm, 2 4 exciting. 3, 0 calm, 1, 2 neutral, 3, 4 exciting.. (EEG: Electroencephalogram).. P, Tp, O Hz Biosemi Active Two device [13] , 4-45 [Hz] Arousal, Arousal ,.., 2, 3., 2 70 %, 3 60 %., 9
18 , %, %,. 2.7., basic emotion ( ), dimensions of emotion ( ),,. SAM. dimensions of emotion Valence Arousal. IAPS., SAM,..,,. IAPS EEG. IAPS, High Arousal 50, Low Arousal IAPS. SAM,., 2 70 %, 3 60 %,. 10
19 [2]. 100, Arousal, Arousal.,,.,. 3.1 SAM ,., ,.,,, , IAPS [12] IAPS. 2, Positive Arousal 5.599, Valence 6, Negative Arousal 6.11, Valence 4, Neutral Arousal 4.081, Valence , ,. 300, SAM., 3 SAM. 1 SAM, Next 11
20 SAM.,. Valence SAM, 5, Arousal,. Arousal Z, 4. Positive Valence 6 Arousal Z 0, Negative Valence 4 Arousal Z 0, Neutral Valence 5 Arousal Z 0.,,.,. IAPS 300,. Figure 2 IAPS 12
21 Figure 3 Figure 4 13
22 Neutral., Arousal Neutral, Arousal Valence, Negative. Number of Images Positive Negative Neutral S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 Figure 5 14
23 IAPS Valence, Arousal, Neutral. IAPS,., , S2, S4, S9, S ,.. BrainAmp (Brain Products ) BP [Hz],, 4[Hz] - 22 [Hz] 1 [Hz]. NEC PX-50XM2. P7, P8, Tp9, Tp10, O1, O2, Fp1, Fp2. Fp1 Fp2, [14]. 15
24 Figure 6 32,. 7., ,,. 4.,,. 16
25 Figure 7 4 Valence, Arousal Z High Arousal Low Arousal. Arousal Z 0 High Arousal, 0 Low Arousal. High Arousal 16, Low Arousal 8.,, Arousal,, , %.. 1., [15]. 4 [Hz] 22 [Hz], 1 [Hz], 1 18, 1 18 [ ] 8 [ ] 144., -50 V -50 [ V], +50 [ V] +50 V [16]. 17
26 ,. [17],.,. 8,,. Figure 8, 3000 [ms], [ms] 1000 [ms], 100 [ms]. 1 21,,. 2.5,,, 18
27 . kernlab ksvm,, kernlab sigest. C C = 1,, C = ,, [ms], 500 [ms], 1000 [ms] 100 [ms] [ms] 1, 1 21.,,,.,.,,,.,, 20 %, 80 %.,,. NB, FDA, SVM., Neutral Positive, [ms]., 9.,.. 10,. Neutral Positive. Neutral Negative, [ms]., [ms] 5500 [ms] 3000 [ms]. 19
28 Low Arousal High Arousal, [ms]., 11.,, Low Arousal High Arousal,, [ms] 4500 [ms] 3000 [ms]. Accuracy FDA NB SVM Start Time [ms] Figure 9 Neutral VS. Positive 20
29 Accuracy Start Time [ms] FDA NB SVM Figure 10 Neutral VS. Negative Accuracy FDA NB SVM Start Time [ms] Figure 11 Low Arousal VS. High Arousal 21
30 High Arousal, Low Arousal,. 12. FDA, NB, SVM %,. Figure ,.,. 3.3.,.,, 22
31 .,., 100,,,,.,. 3.4 IAPS 300, SAM.,, Neutral.,. 23
32 4. 4.1,.,,.,.,,. IAPS,, IAPS [12] Valence, Arousal. Valence, Arousal Positive, Valence, Arousal Negative, Valence Arousal Neutral., SAM.,., 2,. 2,..,.,,. 24
33 Figure 13 25
34 ,,,.,,. Neutral. Arousal, Positive, Negative Arousal. Positive 15, Negative 65, Neutral 145, Positive 15, Negative Arousal 15. Neutral Arousal 15., 4.5,,. Valence Arousal. SAM Arousal 9, Arousal 1 9 3, Arousal., Arousal 1, 4, 9 3, 1 Low Arousal, 9 High Arousal, 4., 2., , , 26
35 ., 4.3 High Arousal Low Arousal SAM , 14., 5, Positive., 1,. Neutral 150, Positive 15, Negative 30, Neutral 15, Positive 15, Negative 15, 45.,, 40, 120. Valence, Arousal 15.. Valence Arousal, Valence Arousal. Number of Images S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 Figure 14 27
36 Valence Arousal Figure 15 Valence, Arousal 4.5.2,,.,,., ,,, 3. 28
37 4.6,,.,.,,., 4.5,. 29
38 , ,,.,, 3000 [ms], 500 [ms],,, , , [ms] 4500 [ms]. 4, High Arousal 16, Low Arousal 8, 24., High Arousal 16, Low Arousal 8., High Arousal Arousal, Low Arousal Arousal., 66.6 %. 16.,. 2,,. 16.,, 50 %. 17, 18, , 1.,,., 1,.. 30
39 Table 1, High Arousal 14 Low Arousal 6 1 High Arousal 2 Low Arousal 2 Figure 16 VS. 31
40 Figure 17 FDA Figure 18 NB 32
41 Figure 19 SVM ,.,,.,,. ( ) Neutral Negative [ms] 5500 [ms] 3000 [ms] Low Arousal High Arousal [ms] 4500 [ms] 3000 [ms]
42 Accuracy FDA NB SVM S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 Mean Figure 20 Neutral VS. Negative Accuracy FDA NB SVM S1 S2 S4 S5 S8 S9 S10 S11 Mean Figure 21 Low Arousal VS. High Arousal 34
43 5.2,,, 3.2.2, High Arousal Low Arousal 1500 [ms] 4500 [ms] Negative Neutral 2500 [ms] 5500 [ms].,,,.,, Negative Neutral, High Arousal Low Arousal. 5.3, IAPS. Arousal, Neutral, Impact.,,,,,. IAPS Valence, Arousal, 15, Valence, Arousal, Positive Neutral. IAPS,,,,,,., Valence, Arousal Negative. IAPS Negative, Positive. 35
44 1500 [ms] 4500 [ms] Low Arousal High Arousal, 2500 [ms] 5500 [ms] Neutral Negative., Negative., Negative Neutral., Low Arousal High Arousal,. Arousal. 36
45 6. 6.1,,. 2,,,. 3 2,,. 4. SAM,.. 5,.,,,.,,.,, 50 %, 70 %,. 6.2,.,. Highly Sensitive Person ( HSP) [18],,. HSP 37
46 ,,,. 38
47 ,,,.,..,..,,.,.. Sakriani Sakti,,.,.. Graham Neubig,,.....,.,,. 39
48 [1] Thomas Adelaar, Effects of media formats on emotions and impulse buying intent, Journal of Information Technology, Journal of Information Technology,Volume 18, Issue 4, [2] Guillaume Chanel, Emotion Assessment: Arousal Evaluation Using EEG s and Peripheral Physiological Signals, Multimedia Content Representation, Classification and Security Lecture Notes in Computer Science, Volume 4105, Pages , [3]. [4],,. IIC,. IIC,, Pages 51-54, [5] J.D. Morris, SAM: The Self-Assessment Manikin, An Efficient Cross- Cultural Measurement Of Emotional Response, Journal of Advertising Research, [6] M. Murugappan, An investigation on visual and audiovisual stimulus based emotion recognition using EEG, International Journal of Medical Engineering and Informatics, 1(3), [7] P Ekman, An argument for basic emotions, Cognition & Emotion, Taylor & Francis, [8] Alessandro Vinciarelli, Social Signal Processing: Survey of an Emerging Domain, Image and Vision Computing, Volume 27, Issue 12, Pages , [9] JA Russell, Evidence for a three-factor theory of emotions, Journal of research in Personality, [10] Margaret M. Bradley, Measuring emotion: The self-assessment manikin and the semantic differential, Journal of Behavior Therapy and Experimental Psychiatry, Volume 25, Issue 1, Pages 49-59,
49 [11] Lang, P.J., Bradley, M.M., International affective picture system (IAPS): Affective ratings of pictures and instruction manual, [12] International Affective Picture System (IAPS): Affective ratings of pictures and instruction manual, [13] Biosemi. [14] Steven K Sutton and Richard J Davidson, Prefrontal brain electrical asymmetry predicts the evaluation of affective stimuli, Neuropsychologia, Volume 38, Issue 13, Pages , [15] Danny Oude Bos, EEG-based Emotion Recognition, The Influence of Visual and Auditory Stimuli, [16],,, Pages 71, [17] Siamac Fazli, Enhanced performance by a hybrid NIRS? EEG brain computer interface, NeuroImage, Volume 59, Issue 1, 2, Pages , [18] Grant BenhamCorresponding, The Highly Sensitive Person: Stress and physical symptom reports, Personality and Individual Differences, Volume 40, Issue 7, Pages ,
50 A. A.1 1., EEG, 36 June
IPSJ SIG Technical Report Vol.2017-SLP-115 No /2/18 1,a) 1 1,2 Sakriani Sakti [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Scie
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THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, 464 8601 470 0393 101 464 8601 E-mail: matsunagah@murase.m.is.nagoya-u.ac.jp, {ide,murase,hirayama}@is.nagoya-u.ac.jp,
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