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1 2014 3

2 i VEP VEP VEP VEP VEP VEP VEP

3 ii VEP VEP

4 iii VEP F 2F VEP

5 ,,,,,.,..,, ,,,,,,, [1].,,,,,, 25% [2]. (Neuron) (Glia), 1000 [3][4]. 1,,,.,, [3][4]. Fig. 1.1., (Retina), (Lateral geniculate nucleus: LGN), (Visual cortex)., µm [5]. 3 3, Helmholtz 3,,, 3 (Young-Helmholtz ) [5]. 3, 3 (Cone), (Rod) 4 [5][6][7][8] , 530, 430 nm, L, M, S, [5][6][7][8].,, 500 nm [6]. 4

6 1 2 Fig. 1.1: Visual information process, 5, [6]., 0.35 S [5][6][9]., S 6%, L M 1:1-17:1 [6]., 1.25, 20 [6]., Retinal, [5]. Fig (Bipolar cell), (Retinal ganglion cell) [5][6][7][8]. 1, 1 [8]., [2][5][10]. 80% Midget, 10% Parasol Bistratified, [2]. Parasol, L M,, [5][10][11].,,, [5][10][11]. Midget L M,, L ( ), M ( ) [5][11]. Parasol,,, [5][11]. Bistratified, 3, S

7 1 3 ( ), L M ( ) [2][8][12]., Midget [2][12]., ( : Optic nerve) (LGN), LGN (Primary visual cortex: V1) [2][5][10][11]., ( ), ( ). [5][7][8]. LGN 6, 4 (Magnocellular layer), 2 (Parvocellular layer), (Koniocellular layer) (Fig. 1.1)[8]. Parasol LGN V1 4Cα, Midget LGN V1 4Cβ, Bistratified LGN V1 2/3 blob [2][5]., V1,, 3 [13], [14]. V1 2 (V2). V2 (Thick stripe), (Interstripe), (Thin stripe) 3 (Fig.1.1) [5]. V1 4Cα, 4B V2 5 /MT (Dorsal pathway)., V1 2/3 blob V2, V1 2/3 interblob V2 4 (V4) (Ventral pathway) [5][10][11]., [5][10][11]. Fig. 1.2: Diagram of visual information processing

8 ,,.,, [3][4]., Fig. 1.3,.,,,,. (Electroencephalogram: EEG) [3][4][15].,, [4]. Fig. 1.3, (Cerebrospinal fluid),,, (Meninges), (Skull) (Scalp) [1]. 4, ( )[16]. 80 1, mv µv. Fig. 1.3: Generation and conduction of electroencephalogram, [5].,.,, [5]

9 1 5.,,, [13][14][17][18][19][20][21]., [5][7][22].,, (Magnetoencephalogram: MEG), (Electroretinogram: ERG)., (MRI), (PET), (SPECT), MRI (fmri), (NIRS). fmri PET, NIRS, [23][24][25][26][27],,. MEG,,, [15][22].,,,,, (Visual evoked potential: VEP), [2],.,,, (time-locked) (event related) [28]. VEP, (Somatosensory evoked potentials: SEP), (Auditory evoked potentials: AEP) [28]. Berger 1929, Dawson [16]. Dawson, [16]. VEP 1964 Cigarek Gastaut [11][16], 1970 Halliday VEP [11][16],.

10 VEP,., VEP, VEP ,,,., / [16]., Fig [30]. VEP, Halliday (Queen Square ) [4][11][16][31], [32]. 2. 2, 1 (Reference derivation), 1 (Bipolar derivation).,,,., (Fig. 1.4 A1, A2),,, [16]..,,,, ERG, [4].,.,, δ (0.5-4 Hz), θ (4-8 Hz), α (8-13 Hz), β (13 Hz -) [4][16]., 25-65, 25, 65 [16]., VEP, VEP. µv, µv - µv [29]. VEP, , [4][16].,

11 1 7 Fig. 1.4: International system, Dawson, [4][16][33].,,. Fig. 1.5, (Segment 1, Segment 2,...) (Trigger) [33][34].,,., ,,,., LGN,

12 1 8 Fig. 1.5: Averaging method [11]. VEP, checkerboard grading.,,,,, [11][29].,.,, [11]. VEP, ( ) transient VEP steady-state VEP 2 (Fig. 1.6)[2][11]. transient VEP, VEP, 1 Hz (Fig. 1.6 (a)). VEP, ( ) 75 ms (N75), 100 ms (P100), 145 ms (N145) [2][11]., transient VEP [2][11]., N75 V1

13 1 9 4C, P100 V1 2/3, N145 V1 - V3 [2][11]. 3.5 Hz, 1,,. VEP steady-state VEP (Fig. 1.6 (b)). steady-state VEP,, (First harmonic: 1F) 2 (Second harmonic: 2F) (Fig. 1.6 (c)), [2][11]., [35][36][37]. Complex demodulation Wavelet [38][39][40]., steady-state VEP, transient VEP [2][11]., transient VEP steady-state VEP, [2], transient VEP steady-state VEP [2]. Fig. 1.6: transient VEP and steady-state VEP

14 VEP.,., VEP, VEP VEP,,,, [16][29]., VEP [41][42]., [43]. VEP P100, P100. VEP,, [16][29], 3 [16][29]., P100, 60 [29]., P100, [29]. Fig. 1.7 ERG VEP [2]. ERG (Flash ERG) (Retinal dysfunction), ERG (Pattern ERG) (Optic Neuropathy or Maculopathy). VEP (Full-field VEP), (Prechiasmatic Lesion). CT MRI, VEP [2]. VEP (Hemi-field VEP), (Retrochiasmatic Lesion),. VEP, VEP (Multimodality VEP) ,,.,, VEP, VEP [44].

15 1 11 Fig. 1.7: Algorithm of sequential steps which process visual function[2], checkerboard VEP, [42]. ( ) VEP,., Regan Celesia [39][46][48][45].[49][50]. Regan, steady-state VEP, 10 Hz, 16 Hz, Hz 3 [39][46][48]. 3 steady-state VEP,,.,, 1F 10 Hz, 2F 5 Hz VEP, [45]. Celesia, steady-state VEP (critical frequency of photic driving: CFPD),,,, [49][50].,, VEP [48][51][52],, VEP ERG, VEP VEP [51].,,,,,, / / VEP [53][54].,, ( / ) ( /, / ) [55],,.

16 , VEP., VEP,., VEP, VEP,., VEP,., VEP,, VEP, ( ),, VEP.,,, α.,,.,, VEP., VEP,.,,,., VEP, VEP, VEP 1,.,,, VEP, (Dominant rhythm) 10 Hz α, [4]., VEP [4].,, [61], [62][63][64], VEP [65][66][67]. VEP, VEP,. VEP,,,

17 1 13,., VEP,.,,,., VEP,,, VEP,., VEP, VEP VEP, VEP,, VEP,., VEP,,. 1.5, VEP,,, VEP, VEP., Fig. 1.8, VEP, VEP, VEP, 3. VEP, VEP,.,,, [68][69][70][71][72][73][74][75], VEP,. 2, [76]. VEP, VEP., [77], [78][79],,,

18 , [80], VEP,., [81][82]. 3,,, [83]. 4, 3, [84]. VEP, VEP,. 5, 6, VEP,.,, VEP. (simusoidal modulated light) [45][46][48][56][57], [53][54].,,., [19], ERG [58]. VEP, [59][60], 10 Hz,. 5,, VEP [85]. 6,, VEP, VEP [86]. 7, VEP,,,.

19 1 15 Fig. 1.8: Structure of thesis

20 (visual evoked potential: VEP),, [2]. VEP,,, [43]. VEP,, ( ),,. VEP,,. VEP,, VEP [11].,,,.,,. VEP,,, VEP.,,, [87], [68], [88][69][89], [70][71][72], [90][73][91][92].,, [74], [75], [68][69][70][71][72][73][74][75].,, VEP [93],, [94][95]., VEP,,., VEP,

21 2 17, [96]., VEP,, VEP. VEP,, VEP.,, α.,,,., VEP 10,. 2.2 VEP VEP VEP,, Fig VEP (conventional VEP recording), (measurement equipment) (subject) (visual stimuli), (EEG)., (stimulus genera-tor), (biological amplifier), (signal analyzer).,, LED,.,, VEP. VEP (recorded EEG and VEP), (medical doctor or medical technologist)., VEP, VEP, (instruction)., VEP,,. (real-time evaluation system), Fig. 2.1., (EEG data and recording condition), VEP,.,, VEP., VEP,.,,. VEP, VEP (state of VEP appearance) (state of subjects). VEP, VEP (averaged waveform and power spectrum), VEP (characteristic parameters)

22 2 18 Fig. 2.1: The concept of conventional VEP recording (broken line) and proposed structure of VEP recording including real-time evaluation system..,, (comparison with data base)., VEP, (blink artifacts) (EMG artifacts), α (posterior alpha wave) 3,., VEP, VEP VEP,,. VEP, [85],., VEP., VEP FFT.,, VEP., (1F ), 2, 3, 4 (2F, 3F, 4F ),, S(f, 1), S(f, 2), S(f, 3), S(f, 4), S(f, a), (= S(f, 1) + S(f, 2) + S(f, 3) + S(f, 4))., f [Hz]., 1F, 2F, 1F 4F VEP, A(f, 1) = 4 S(f, 1), A(f, 2) = 4 S(f, 2), A(f, a) = 4 S(f, a) [85]. VEP, 1F 2F VEP [%]

23 2 19, R(f, 1) = S(f, 1)/S(f, a) 100 [%], R(f, 2) = S(f, 2)/S(f, a) 100 [%]., VEP.,,., VEP, ,.,. VEP,, VEP. [95], VEP.., x, 5 Hz 2. Fig. 2.2.,, t f, t p, t b. 3, A f B(x, t p ), A b B(x, t p ) [µv], D f B(x, t p ), D b B(x, t p ) [s]., (A f B(x, t p ))/(D f B(x, t p )). A f B(x, t p ) 30 [µv] (2.1) A b B(x, t p ) 40 [µv] (2.2) D f B(x, t p ) + D b B(x, t p ) 0.52 [s] (2.3) (A f B(x, t p ))/(D f B(x, t p )) 200 [µv/s] (2.4), (2.1) - (2.4), t p.

24 2 20 Fig. 2.2: Parameters on blink artifacts detection.,. 1 T [s], N B, R B = N B /T (2.5). (2.5), θ B R B θ B (2.6),. α, α ( ). VEP,, α,.,,,.,,. α, complex demodulation (CD) [97]. CD,,,. CD, f 0 [Hz], f c [Hz], f 0 ± f c [Hz]. α 8-13 [Hz], 8 [Hz] 13 [Hz] 10.5 [Hz] f 0, α

25 [Hz] f c., CD, [98], 2., CD x t α, A α (x, t) [µv]. α,., 7 [µv] 3 α [4],, A α (x, t) 0.3 [s] 10 [µv], α. α, α. 1 T [s], α T α [s],, α R α = T α /T 100 [%] (2.7)., α R α θ α, α. R α θ α (2.8),., VEP (2F, 3F, 4F ),. CD,., VEP, [Hz]., CD 40 [Hz], 10 [Hz]. α, x t A E (x, t) [µv].,, A E (x, t) 0.05 [s] 10 [µv],. α, x 1 T [s] T E (x) [s],. R E (x) θ E R E (x) = T E (x)/t 100 [%] (2.9) R E (x) θ E (2.10),.

26 , [85] VEP., 2, 6-18 [Hz] 14, 50 [s] [30] 10 (Oz, Pz, Cz, Fz, O1, O2, P3, P4, T5, T6),, 200 [Hz], 0.53 [Hz], 60 [Hz], VEP. 10 ( A - J, 21-24, ), 1, 90.,,,., Fz, α Oz,., θ B = 0.2, α θ α = 30 [%], θ E = 30 [%].,, α,,,., (dynabook satellite T31 186C/5W, ) AD (CBI-3133B, Interface), C (Visual C++, Microsoft). VEP, (VSGThree, Cambridge Research System), (HM903D B1, iiyama), (Neurofax EEG-4524, ).,, AD 200 [Hz]. Fig. 2.3., 2. VEP ( :, : ),,

27 2 23.,, α,,.,, α,.,,,. Fig. 2.3: An example of screenshot of real-time evaluation system VEP Fig. 2.4, 1 ( J, 21 ) VEP,. Fig. 2.4 (a) VEP, (b) 1F, (c) 2F.,,,, 9., VEP,. Fig. 2.4,,, 1,

28 2 24., 3 ( VEP, 1F, 2F ) 1., VEP,. Fig. 2.4: Characteristic parameters of VEP vs. temporal frequency. Amplitudes of VEP for (a) whole component, (b) 1F component and (c) 2F component , 1, Fig Fig. 2.5, I (24 ),, 6-18 [Hz]. Fig. 2.5 (a),, α, 3, (2.5), (2.7), (2.9).,,

29 2 25., 1 1 ( 50 [s]),.,,. Fig. 2.5 (a), 6, α 33.1 [%], ( ). 7, ( ).,, 8.8,. Fig. 2.5 (b), (c), (d),, α,. Fig. 2.5 (b), (c) 6 7, Fig. 2.5 (d) 4., 1 50 [s],,, α,., Fig. 2.5 (a),,., Fig. 2.5 (b) α, Fig. 2.5 (c). Fig. 2.5 (d),, (b), (c) VEP, VEP,. Fig. 2.6 (a), G (21 ) 12 [Hz],. Fig. 2.6 (a-1),, VEP (1F, 2F ),, α,. Fig. 2.6 (a-2) VEP, (a-3), 2., α 33 [%],,., (, 12 [Hz]), Fig. 2.6 (b). Fig. Fig. 2.6 (a).fig. 2.6 (b-1),, VEP 1F 4.9 [µv], 1.7 [µv]. 2F 0.45 [µv] 0.33 [µv], Fig. 2.6 (a-3) (b-3), 2F. Fig. 2.6 (b-2), VEP, (b-3),.

30 2 26,, VEP. 1,, VEP VEP,,.,. VEP,,,.,.,,,., VEP,.,. VEP,,.,,. 1,, VEP.,, VEP.,. Fig. 2.5, VEP,.,,,. Fig. 2.6,, VEP,.,, VEP,., VEP., VEP,., VEP,,

31 2 27,,.,, VEP, , Bradnam [93], VEP,. de Beer [94],,.,., VEP,, α, 3, , 3,.,,,.,,,., VEP,, α, 3,,,,,,.,,., 3, ,, AD,.,, USB,. Bradnam [93],,, VEP 1 PC VEP.,,,.

32 , [85] ( 4 [Hz] steady state VEP), VEP. VEP, 1-2 [Hz] pattern reversal VEP flash VEP. VEP transient VEP,, N75, P100, N145., VEP, VEP.,,,,. α,.,.,,,,., VEP,,.,, VEP. 2.5 VEP,., VEP.,,, α VEP, VEP.,, VEP.

33 2 29 Fig. 2.5: Real-time evaluation of state of subject during VEP recording (Subject I, 24 y. o., Male). (a) shows parameters for evaluating the state of subject. (b), (c) and (d) correspond the detail results of detected segments for each evaluation item.

34 2 30 Fig. 2.6: Comparison of VEP responses between (a) before taking rest and (b) after taking rest. (a-1) parameters, (a-2) averaged waveform of VEP and (a-3) power spectrum for the data before taking rest. (b-1), (b-2) and (b-3) correspond same ones for the data after taking rest.

35 (visual evoked potential: VEP), [2],, [2][33]. VEP,,.,,., ( ). [61], [62] VEP [65]., VEP [16]., VEP,,., VEP,., jitter [99], [77]. [77],,. VEP, [100], [78], [79].,., [80], VEP [101][102]., [101][102],,.

36 VEP [33] VEP Fig. 3.1., (Fig. 3.1(a)), (Fig. 3.1(b) ), (Fig. 3.1(c) ). 3.5 [Hz] steady-state VEP, f s [Hz] 1F, 2 2F, (Fig. 3.1(c) ) VEP [2]. Fig. 3.1: Outline of conventional analysis method for estimating VEP amplitude. [33],, VEP., flash VEP [11],, ( ).,

37 3 33 VEP., VEP,., (Fig. 3.1(b) ), (Fig. 3.1(d)). [103],, VEP., (f s [Hz] 2f s [Hz]), VEP, VEP,.,,, VEP , Fig. 3.1(d), ( )[80] VEP, ( ),,.,,.,., VEP., 1, 1,. g (f) = K { 0.25 t ( σ ξ ) 2 k k=1 1 + (γ k ) 2 2γ k cos 2π t(f m k ) ) t ( σ ξ } k 1 + (γ k ) 2 2γ k cos 2π t(f + m k ) + t(σ v ) 2 (3.1).g(f) f [Hz], m k, γ k 1, σ ξ k, σv, t., m k, Fig Fig. 3.2 (a) f s [Hz] VEP

38 3 34., K (m k, γ k, σ ξ k σv ). f s [Hz] VEP (Fig. 3.2 (b)), (Fig. 3.2 (c)) (Fig. 3.2(d)), VEP. Fig. 3.2: Estimation of VEP and dominant rhythm component by EEG model , m k, γ k, σ ξ k, σv ( ) K., Fletcher-Powell [104], [80]. K AIC (Akaike information criterion) [105][106],.,,., k

39 3 35 f p k H k, S k 3. VEP 1, VEP 1 (k = 1), 2 (k = 2). VEP, f p 1 f s [Hz], H 1. S 1, f s ± 0.5 [Hz]. waxing-and-wanning[4], 1 [Hz].,,.,, f p 2, α (8-13 [Hz]) f p 2 = f h 2 / f j=f l 2 P (j f)j f f / f f h (3.2) 2 / f P (j f) f j=f2 l/ f. f, P (j f), f l, f h, 8-13 [Hz]., f s ± 0.5 [Hz] VEP,. H 2 (3.2) f p 2 [Hz], S [Hz], S 1., (m k, γ k, σ ξ k, σv ) [80]. m k = f p k (3.3) γ k = H k S k t (3.4) H k + S k t σ ξ k = 4S k Hk t (3.5) H k + S k t σ v = 1 (3.6), Fletcher-Powell. VEP,, 1., 3 (k = 3), f p 3, H 3, S 3., 2 f p 2 ± 1.0 [Hz],.,,. α 8-13 [Hz],. S 3. (3.2), f p 3

40 3 36 H 3.,. S 2, S 3. 2 (k = 2, 3), 1 (k = 2) AIC, AIC (k = 4), ,., VEP,. VEP, VEP,, 2 2 = 4., α,. VEP, 1,., i yi V (t) = a V i sin{2πf V i (t i 1 f s )} ( i 1 f s 0 (t < i 1 f s t i 1 f s + 1 f V i, i 1 f s + 1 f V i ) < t), VEP (3.7) I E(t) = yi V (t) (3.8) i=1. a V i ā V, σ a, f V i, f s, σ f. ( ), [80]. x α (n t) = a α (n t) sin(2πm α n t) + v(n t) (3.9) a α ((n + 1) t) = γ α a α (n t) + ξ α (n t) (3.10), (3.9) a α (n t), (3.10) 1. n, m α, t, γ α 1, v(n t) ξ α (n t) ( 0 σ ξ α σv ).,. VEP f s 8, 9, 10, 11, 12 [Hz] 5, ā V = 4 [µv]. σ a σ f

41 3 37, σ a = σ f = 0, σ a = 2.0 [µv], σ f = 1.0 [Hz]., m α = 10.0 [Hz], γ α = 0.98, σ ξ α = 22.9, σ v = 10.0.,,.,, (3.9) 1,. t = 5 [ms], [s] 10., Fig. 3.1(c) (d), Fig. 3.1(c), Fig. 3.1(d) 3 VEP,., VEP S, 4 S [80]., VEP f s ± 0.5 [Hz], α VEP., (3.7) VEP,, VEP. (3.10), (3.9),., A τ Â, ε = Â A τ A τ 100 [%] (3.11)., 10, Fig VEP (estimated VEP amplitude), (estimated amplitude of posterior dominant rhythm). 2 (high amplitude background activity), 1 (C1) VEP (VEP without variability), 2 (C2) VEP (VEP with variability). 2 (C3), (C4), (low amplitude background activity), VEP 2., ( ) ( ).,, (true value), (proposed), (stimulus locked averaging),

42 3 38 (power spectrum averaging).,,,,.,,., 2 100, 3 100, 4,. VEP,, VEP (C2) 2.8 ± 4.7 [%]. (C2) 10 [Hz],,., VEP., VEP (C3), 1.2 ± 4.4 [%], VEP (C4) 12 ± 3.6 [%],. (C2) 10 [Hz], (C1) VEP., VEP,, VEP.,, (C1), (C2), 10 [Hz]. (C4), 2.8 ± 4.1 [%], VEP., (C1), (C2), 3.9 ± 1.8 [%] 4.1 ± 1.9 [%]., VEP., VEP,,. 10,.,. 3.5,, VEP. VEP, VEP.

43 3 39 Fig. 3.3: Estimated amplitudes and error rate of VEP component (upper part) and posterior dominant rhythm (lower part) for simulation data (21, ) 1, 57 [cm] 30. 8, 9, 10, 11, 12 [Hz] 5, [30], Oz, 200 [Hz], 0.53 [Hz], 60 [Hz].,,., Fig. 3.4 VEP. Fig. 3.3, VEP,, (closed eye state), (open eye state). 3,. VEP,,,, VEP., 10, 11, 12 [Hz] 3,. 3,,, VEP

44 3 40.,,., VEP.,,.,,,. Fig. 3.4: Estimated amplitudes of VEP component (upper part) and posterior dominant rhythm (lower part) for actual data ,, steady-state VEP.,, VEP, 2., [62], [65]. [16], VEP,.

45 , α.,, θ β., [4] θ, δ, β.,., VEP,,.,.,., jitter[99].,, jitter VEP,. 3.7 VEP.,,., VEP.

46 (Visual evoked potentials: VEPs),, [2]. VEP [33]., VEP,.,, ( )., (α ),,., [63], VEP [65]. VEP,,. 3, [80], VEP [83].,. 9, VEP (21-25, ), 57 [cm] 30. 8, 9, 10, 11, 12 [Hz] [30], Oz, 200 [Hz], 0.53 [Hz], 60 [Hz].,,., [s] 10.,

47 4 43 (FFT),,. Fig.4.1 (a), 10 [Hz].. Fig.4.1 (a). Fig.4.1 (b). (10 [Hz]) 1F, 2 (20 [Hz]) 2F. Fig. 4.1: Recorded raw VEP and its power spectrum VEP [83], Fig.4.1(b), ( )[80] VEP,,,.,,.,., 1, 1,.

48 4 44 g (f) = K { 0.25 t ( σ ξ ) 2 k k=1 1 + (γ k ) 2 2γ k cos 2π t(f m k ) ) t ( σ ξ } k 1 + (γ k ) 2 2γ k cos 2π t(f + m k ) + t(σ v ) 2 (4.1). g(f) f [Hz], m k, γ k 1, σ ξ k, σv, t. Fig. 4.2: Estimation of VEP and dominant rhythm component by EEG model. m k Fig.4.2 Fig.4.2 (a) f s [Hz] VEP

49 4 45 K (m k γ k σ ξ k σv ) f s [Hz] VEP (Fig.4.2 (b)) (Fig.4.2 (c)) (Fig. 4.2(d)) VEP, (m k γ k σ ξ k σ v ) K., Fletcher-Powell [104], [80]. K AIC (Akaike Information Criterion)[106],. [83] k f p k H k S k 3 VEP 1 VEP 1 (k = 1) 2 (k = 2) VEP f p 1 f s [Hz] H 1 S 1 f s ± 0.5 [Hz] f p 2 α (8-13 [Hz]) f p 2 = f h 2 / f j=f l 2 P (j f)j f f / f f h (4.2) 2 / f P (j f) f j=f2 l/ f f P (j f) f l f h 8-13 [Hz] f s ± 0.5 [Hz] VEP H 2 (4.2) f p 2 [Hz] S [Hz] S 1 (m k γ k σ ξ k σv ) [80] m k = f p k (4.3) γ k = H k S k t (4.4) H k + S k t σ ξ k = 4S k Hk t (4.5) H k + S k t σ v = 1 (4.6)

50 4 46 Fletcher-Powell AIC, AIC [83]., k = 1 VEP, VEP,., VEP S, 4 S [80]., VEP f s ± 0.5 [Hz], α VEP. 4.3 Fig ( D, 21, ), VEP. (i), (power spectrum), (ii) VEP (separated VEP component), (iii) (posterior dominant ryhthm). Fig.4.3 (a) 8 Hz, (proposed), (power spectrum averaging)., VEP, VEP,. Fig.4.3 (b) 9 Hz. (b-ii) VEP,., (b-iii),. Fig.4.4 9, VEP. (a) VEP (Amplitude of VEP component), (b) (Amplitude of posterior dominant rhythm). (temporal frequency), (amplitude), (power spectrum averaging), (proposed). Fig.4.4 (a) VEP, 9 Hz., 9, 10 Hz, t (p 0.05),., Fig.4.4 (b), 9 Hz,,,, (p 0.1).

51 4 47 Fig. 4.3: Comparison between proposed method and conventional method ,., Fig.4.3 (b) VEP, VEP,.,,. Fig.4.4, VEP,,,

52 4 48 Fig. 4.4: Estimation of VEP and dominant rhythm component by EEG model.., [83],,.,, VEP α., θ β.,.,. 4.5 VEP, VEP., VEP VEP [83]., VEP.

53 (visual evoked potential: VEP),, [2]. VEP, 1 Hz (transient VEP), 3.5 Hz (steady-state VEP) [47][49]. VEP,,, [2]. transient VEP (N75), (P100), (N175), [2], [107]. steady-state VEP, (first harmonic: 1F), 2 (second harmonic: 2F),, [2]., 1F [45][48][54], 2F [108]., [54]., ( ), ( ),.,,, 2, VEP,. 9 VEP,.,,.

54 (, ),,, VEP.,, VSG Three (Cambridge Research System, U. K.), 12, 87.7 cd/m 2, 1.19 cd/m 2, 57 cm. 2, 1., 6, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 11.5, 12, 15, 18 Hz s, Oz 200 Hz, 0.3 s (0.53 Hz), 60 Hz., ( 0.2 ) , VEP s (1024 ) 1, 50 s, 3 1, 30., 1 VEP. VEP (FFT),. VEP,,, 1F 4F VEP., 3F, 4F 1F, 2F,,., (sub harmonic),,. 1F 4F, 1F - 4F VEP, VEP,.,,,., VEP, ±0.5 Hz, 1F 4F S(p, f, h) [µv]

55 p, s ( ), r ( ). f, h 1F - 4F VEP (a ). f [Hz] VEP, 1F 4F, S(p, f, a) = S(p, f, 1) + S(p, f, 2) + S(p, f, 3) + S(p, f, 4) [µv 2 ] (5.1)., f Hz 1F, 2F, VEP A(p, f, h) = 4 S(p, f, h) (h = 1, 2, a) [µv] (5.2)., VEP 1F 2F. R(p, f, h) = S(p, f, h) 100 (h = 1, 2) [%] (5.3) S(p, f, a) 5.2.4, D A (f, h) =., A(s, f, h) A(r, f, h) 100 [%] (5.4) max A(s, f, h), A(r, f, h) D R (f, h) = R(s, f, h) R(r, f, h) 100 [%] (5.5) max R(s, f, h), R(r, f, h). D A (f, h), D R (f, h), %,, ,,,

56 ,., n, Ã(p, f, h) Ã(p, f, h) = A(p, f, h) 1 A(p, f, h) (5.6) n f., VEP 1 ( E, 21 ) Fig Hz, 10 Hz, 12 Hz, 1 s, 0 30 Hz.,.,. 1F 2F., 6 Hz, 1F, 2F. 10 Hz, 1F, 2F. 12 Hz, 1F, 2F Fig.5.2,,.,,,., ( 1F, 2F, VEP ), ( 1F, 2F, VEP )., 1F, 2F, VEP. Fig.5.2 (a-1 ) (a-2 ) VEP, 11 Hz., Fig.5.2 (a-1) (a-2) 1F, 11 Hz, 2F, 6 Hz. Fig.5.2 (a-1) (a-1 ), VEP 1F, 7 Hz 11 Hz., Fig.5.2 (a-2) (a-2 ),. Fig.5.2 (a-1), 1F 2F, 1F., Fig.5.2 (a-2), 6 Hz 2F, 7 Hz, 1F. 1F 2F.

57 5 2 53, Fig.5.2 (b-1), 9 Hz 1F, 9-11 Hz,. 2F. Fig.5.2 (b-2), 6-15 Hz 1F, 2F, 9 Hz 11 Hz 2F Fig.5.3 (5.4), (5.5). 1F, 2F, VEP,,.,,., Fig.5.3 (a-i) 1F, 6, 10.5, 11, 18 Hz,. Fig.5.3 (a-ii) 2F,. Fig.5.3 (a-iii) VEP, 6 Hz (a-i) 1F., Fig.5.3 (b-i) 1F, Hz, 11 Hz. Fig.5.3 (b -ii) 2F, 12, 15 Hz. 1F 2F, 11 Hz , VEP,., 1F 4F, 0.5 Hz.,,,., A(p, f, h) R(p, f, h) 2., [54][108]., VEP., VEP 1F, 2F, [108],. 1F,, VEP.

58 , 5.4, 5.5 D A (f, h), D R (f, h). [54],,. Fig.5.3, 2, VEP Regan[45], 1F, 10 Hz.,, 1F, Yamasaki [54] 9 Hz, Pieh [108] 12.5 Hz., Fig.5.2 (a-1) (a-2) 1F 11 Hz,., Fig.5.2 (b-1), 6-8 Hz ( ) Hz ( ), 8-12 Hz ( ) 1F. Fig.5.3 (a), 1F, 2F, VEP,,., VEP,, Fig.5.2 (a-1 ), (a-2 ),. Fig.5.3 (a),.,,.,,.,.,,,, F 2F 1F 2F Pieh [108],, 7.5 Hz 2F. 2F,

59 F, 2F., Fig.5.2 (a-2) 6 Hz 2F., Fig.5.2 (a-1) 1F,. Fig.5.2 (b-2), 2F,., 2F VEP, α VEP Pigeau [65], VEP, α, α., flash VEP, Lazarev [66], VEP, Birca [67], VEP, α., α VEP, Fig.5.2 (a-1) (a-2), 1F.,,. α VEP, α. Fig.5.2 (a-1) (b-1), 1F 9-12 Hz., Fig.5.3 (a-i) (a-iii), α. VEP Hz,,, α,. 5.5,.,.,., 2F, 2F,.

60 Fig. 5.1: VEP waveform (left hand side) and their periodograms (right hand side) for temporal frequency of 6 Hz (upper part), 10 Hz (middle part) and 12 Hz (lower part).

61 Fig. 5.2: Relationship between temporal frequency and the characteristics of (a) VEP amplitude (left hand side) and (b) ratio (right hand side) for sinusoidal pattern (upper part) and square pattern (lower part) stimuli.

62 Fig. 5.3: Differences of response between sinusoidal pattern and square pattern.

63 (visual evoked potential: VEP),, ( ) [2]. 3.5 Hz steady-state VEP, (1F) 2 (2F), [2]. VEP,, [2]. VEP, VEP [48][51][52], VEP [45][53][54][109][110]., / 2F 5 Hz [109][110], /, / 1F 10 Hz, 2F 5 Hz [45].,, (V1) ( / ) ( /, / ) [53][54][55]. [45][53][54][109][110], [53][54] [45][109][110]., VEP [85], VEP. V1,,, 3 [13]., [13],, VEP [45][53][54][109][110]., ( / ), ( / ), 2, 4 VEP,. 11 VEP

64 6 2 60,,,, 3, (, ),,, VEP.,, VSG Three (Cambridge Research System, U. K.), 12, 87.7 cd/m 2, CIE x = 0.620, y = ( ), x = 0.166, y = ( ), x = 0.290, y = ( ), 57 cm., ( 0.2 )., / (R/B), / (R/G) 2,., (Sin.) (Sq.) 2., / (R/B-Sin.), / (R/B-Sq.), / (R/G-Sin.), / (R/G-Sq.) 4. 4, 5, 6, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 11.5, 12, 15, 18 Hz 16, 50 s, Oz 200 Hz, 0.53 Hz, 60 Hz steady-state VEP, (1F ), (2F, 3F, 4F ), VEP., 1F, 2F, 1F 4F VEP 3. VEP, [85] VEP, 2, 2, 2 2 = 4. ( 1, 2),. f [Hz] 1 VEP A 1 (f) [µv], 2

65 A 2 (f) [µv]. (4 18 Hz) A 1 (f) A 2 (f) A max, 2 D(f) = A 1(f) A 2 (f) A max 100 [%] (6.1). (6.1), 1, 2,. A max,, VEP Fig. 6.1 (a)., R/B-Sin., R/G-Sin., R/B-Sq., R/G-Sq., VEP (Whole), 1F (1F), 2F (2F), (Temporal frequency), (µv). VEP, R/B-Sin. 9 Hz, R/G-Sin. 9.5 Hz, R/B- Sq. R/G-Sq. 4 Hz. 1F, R/B-Sin. 10 Hz, R/B-Sq Hz, R/G-Sin. R/G-Sq. 9.5 Hz, R/B R/G. 2F, R/B-Sin. R/G-Sin. 5 Hz, R/B-Sq. R/G-Sq. 4 Hz., Sin. Sq. VEP 1F 7-10 Hz, Sq. VEP 2F 4-6 Hz.,, R/G R/B, VEP 1F Hz Fig. 6.1 (b),, (6.1). R/B Sin. Sq. (R/B-Sin. vs. R/B-Sq.), R/G (R/G-Sin. vs. R/G-Sq.). Fig. 6.1 (a), (6.1). (6.1), 10 %., VEP 2F, 4, 5 Hz Sq.. R/B VEP, 9, 9.5 Hz, 1F, Hz Sin.

66 , t (p < 0.1) Fig. 6.1 (c),,, Sin. R/B R/G (R/B-Sin. vs. R/G-Sin.), Sq. (R/B-Sq. vs. R/G-Sq.). Sin. VEP 1F, Hz R/B. Sq. VEP 11.5, 12Hz, 1F Hz R/B., Sq. 1F, Hz R/G., t Fig. 6.1 (a), 1F, Sin. Sq Hz. [85],, Sin. Sq.. Sin.,, 7 10 Hz., Sq.,,. Fig. 6.1 (a), Sin. 2F, 5 Hz. Regan[45] /, Fiorentini [109] Porciatti [110] /,. Fig. 6.1 (b), 2F, 4, 5 Hz Sq.. [85], 2F Sq. Sin.. Pieh [108],, 7.5 Hz 2F 1F, 2F, Sq. 2F. steady-state VEP,, [111]., 2F., 2F,, 5 Hz.

67 F, Fig. 6.1 (c) 12 Hz R/B, Fig. 6.1 (a) R/B R/G. Gouras[51], VEP, VEP,, V1. Crewther [52], P100., 2 ( ), V1, R/G R/B., R/B R/G, V1, R/G. [53] Yamasaki [54], 24 cd/m 2 Sq.,, Hz, R/B 9 Hz, R/G 12 Hz.,.,,., [85], Fig. 6.1 (b), R/B VEP 1F, 9 Hz Sin., R/G Sin. Sq..,., Sq. Sin. [58], Sin. Sq., V1. [85], 10 Hz Sin.., Sin.,. R/B, Sin., V1,. R/G, Sin. Sq.. [54], R/G, Sin., Sq.. Fig. 6.1 (c), Sq. 1F, Hz R/G, Sin. R/B R/G., S, L, M [9],

68 R/B R/G. VEP [53], ( 1 4 5) V1 ( 6 3 1), 1 1, V1,., Sin., V1., Sin. R/B R/G, Sq. R/G R/B , 2.,,. /,.

69 Fig. 6.1: Relationship between temporal frequency and characteristics of VEPs for (a) amplitude, (b) difference between stimulus patterns, and (c) difference between color combinations.

70 66 7,., VEP,,, VEP, VEP.. 1,,, VEP,. 2, VEP, [76]., VEP.,,, α 3,,. 10 VEP,, VEP,. 3,, VEP [83],, VEP,.. 4, 3, VEP, 9 VEP [84].,, VEP, 3., VEP. 5, [85].. 9,. 6, 2, 5 2

71 7 67 [86]. 11,,., 5 6,,,,,,. VEP,,, VEP., ,.,.,, 1, VEP. 6, ,, 1 30,., VEP., VEP.,, VEP,,. 3, 4,., VEP, VEP, VEP. steady-state VEP 1., steady-state VEP,. 5, 6 VEP, 1,.,,.,, 1,, 1..,

72 7 68. VEP,.,,.,,.,, steady-state VEP, VEP transient. VEP,. VEP,.,, VEP,,. VEP,. VEP,,., VEP,., VEP,,., VEP,. VEP, VEP, VEP,.,,., VEP,.

73 69,,,,.,,.,.,..,,,..,,.

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83 79 A. ( ) [1],,,,,, : ERP, 37 6, 432/ [2],,,,, : 2, 39 1, 10/17, 2011 [3],,,,,,, :, 50 5, 433/442, 2012 [4],,,,,,, :, 50 6, 599/606, 2012 [5],,,,,,, :, 41 1, 1/6, 2013 B. [1] K Goto, T Sugi, T Maekawa, K Ogata, Y Goto, S Tobimatsu and M Nakamura: Quantitative Analysis between Visual Mismatch Negativity and Psychopathology Scale for Schizophrenia, ICROS-SICE International Joint Conference 2009, 1B16-6, 119, (Fukuoka) August 2009 [2] K Goto, T Sugi, Y Goto, T Yamasaki, S Tobimatsu and M Nakamura: Quantitative analysis of VEP on difference between sinusoidal pattern and rectangular pattern, 29th International Congress of Clinical Neurophysiology, P1-4, S101, (Kobe) October 2010

84 80 [3] K Goto, T Sugi, S Goto, Y Goto, T Yamasaki and S Tobimatsu: Topography Estimation of Visual Evoked Potential by Combinational Use of Mathematical Models, the 2011 IEEE/ICME International Conference on Complex Medical Engineering, MP1-3(1), 205/210, (Harbin) May 2011 [4] K Goto, T Sugi, Y Matsuda, S Goto, H Fukuda, Y Goto, T Yamasaki and S Tobimatsu: Real-time Evaluation System for Accurate VEP Recording and Analysis, the 2012 ICME International Conference on Complex Medical Engineering, TP1-4, 429/434, (Kobe) June 2012 [5] K Goto, T Sugi, Y Matsuda, S Goto, H Fukuda, Y Goto, T Yamasaki and S Tobimatsu: Analysis of Visual Evoked Potentials for Different Stimuli: Effects of Color Combination and Patterns. 35th Annual International Conference of the IEEE Engineering in Medical and Biology Society, Short Papers No. 3012, (Osaka) July 2013 [6] Y Sueyoshi, K Goto, T Sugi, Y Matsuda, S Goto, H Fukuda, Y Goto, T Yamasaki and S Tobimatsu: Physiological State Evaluation of VEP Recording by Combination of Image and EEG. 35th Annual International Conference of the IEEE Engineering in Medical and Biology Society, Short Papers No. 3078, (Osaka) July 2013 [7] K Goto, T Sugi, Y Matsuda, S Goto, H Fukuda, Y Goto, T Yamasaki and S Tobimatsu: Chracteristic Analysis of Visual Evoked Potentials and Posterior Dominant Rhythm by Use of EEG Model th International Conference on Control, Automation and Systems, MB-02, 233/236, (Gwangju) October 2013 C. (Book chapter) [1] T Sugi, K Goto, S Goto, Y Goto, T Yamasaki and S Tobimatsu: Topography estimation of visual evoked potentials using a combination of mathematical models. In Jinglong Wu: Biomedical Engineering and Cognitive Neuroscience for Healthcare: Interdisciplinary Applications. Medical Science, 129/141, 2013 D. [1],,,,,, :, 20 2C4, 59, ( )

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86 82 [13],,,,,,, :, 24, 1A1, 23, ( ) [14],,,,,,,,, :, 24, 1A2, 24, ( ) [15],,,,,,, :,, Vol. 112, No. 417, MBE , 1-6, ) [16],,,,,,, :, 2013, , ( ) [17],,,,,,, : :, 25, P2-90, ( ), [18],,,,,, :, 25, P2-51, ( ), [19],,,,,,, :,, vol. 113, no. 409, MBE , pp ( ) [20],,, : STN-DBS, 53, O5-1. ( ) [21],,,,,,,, :, 26, 2C2. ( ), [22],,,,,,,, :, 26, 2C3. ( ),

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