i

Transcription

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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,,,,.,,.,.,..,,,..,,.

74 70 [1], :,,, [2] S. Tobimatsu, G. G. Celesia: Studies of human visual pathophysiology with visual evoked potentials. Clin Neurophysiol. 117, 1414/1433, [3] : :, 33(4), 221/230, [4] : 5.,, [5] : I,,, [6] :,,, [7] :,,, [8] :,,, 2010 [9] C. A. Curcio, A. A. Kimberly, K. R. Sloan, L. L. Connie, J. B. Hurley, I. B. Klock, A. H. Milam: Distribution and morphology of human cone photoreceptors stained with anti-blue opsin. J Comp Neurol 312, 610/624, [10] M. S. Livingstone, D. Hubel: Segregation of form, color, movement, and depth: anatomy, physiology, and perception. Science 240, 740/749, 1988 [11], : (VEPs):, 36(4), 257/266, 2008 [12] S. H. Hendry, R. C. Reid: The koniocellular pathway in primate vision. Ann Rev Neurosci 23, 127/153, 2000 [13] E. N. Johnson, M. J. Hawken, R. Shapley: The spatial transformation of color in the primary visual cortex of the macaque monkey, Nature Neurosci 4(4), 409/416, [14] A. Hanazawa, H. Komatsu, I. Murakami: Neural selectivity for hue and saturation of colour in the primary visual cortex of the monkey. European Journal of Neuroscience 12, 1753/1763, 2000

75 71 [15],,, :. 40(1), 8/18, [16], :.,, 2008 [17] A. M. Derrington, J Krauskopf, P Lennie: Chromatic mechanisms in lateral geniculate nucleus of macaque. J. Physiol 357, 241/265, [18] P. Lennie, J. Krauskopf, G. Sclar: Chromatic mechanisms in striate cortex of macaque. Journal of Neuroscience 10(2), 649/669, [19] J. Kremers, B. B. Lee, J. Polorny, V. C. Smith: Responses of macaque ganglion cells and human observers to compound periodic waveforms. Vision Res 33(14), 1997/2011, [20] R. L. De Valois, R. T. Marrocco: Single cell analysis of saturation discrimination in the macaque. Vision Res 13, 701/711, 1973 [21] H. Komatsu, Y. Ideura, S. Kaji, S. Yamane: Color selectivity of neurons in the inferior temporal cortex of the awake macaque monkey. J Neurosci 12, 408/424, [22], 36(3), 114/121, 2008 [23] K. T. Mullen, B. Thompson, R. F. Hess: Responses of the human visual cortex and LGN to achromatic and chromatic temporal modulations: An fmri Study, J Vision 10(13), 1/19, [24] J. Liu, B. A. Wandell: Specialization for chromatic and temporal signals in human visual cortex. J Neurosci 25(13), 2459/2468, [25] T. Yamasaki, Y. Goto, Y. Ohyagi, A. Monji, S. Munetsuna, M. Minohara, K. Minohara, J. Kira, S. Kanda and S. Tobimatsu: Selective impairment of optic flow perception in amnestic mild cognitive impairment: Evidence from event-related potentials, Journal of Alzheimer s Disease 28, 695/708, [26] M. A. Pastor, M. Valencia, J. Artieda, M. Alegre, J. C. Masdeu: Human cerebral activation during steady-state visual-evoked responses. The Journal of Neuroscience 23(37), 11621/11627, [27] S. P. Koch, J. Steinbrink, A. Villringer, H. Obrig: Synchronization between background activity and visually evoked potential is not mirrored by focal hyperoxygenation: Implications for the interpretation of vascular brain imaging. The Journal of Neuroscience 26(18), 4940/4948, 2006.

76 72 [28] : (1), 47(9), 573/583, [29] : (2), 47(10), 638/648, [30] G. H. Klem, H. O. Luders, H. H. Jasper, C. Elger: The ten-twenty electrode system of the International Federation. In: Deuschl G and Eisen A eds. Recommendations for the Practice of Clinical Neurophysiology: Guidelines of the International Federation of Clinical Neurophysiology, Elsevier, Amsterdam, 3/6, [31], ;.,, [32] G. F. A. Harding, J. V. Odom, W. Spileers, H. Spekreijse: Standard for visual evoked potentials Vision Res 36(21), 3567/3572, [33] G. D. Dawson: A summation technique for the detection of small evoked potentials. Electroencephalogr Clin Neurophysiol 6, 65/84, 1954 [34], : Averaging (1), 4(2), 233/237, [35] Y. Goto, T. Taniwaki, K. Yamashita, N. Kinukawa, S. Tobimatsu: Interhemispheric functional desynchronization in the human vibratory, Brain Research 980, 249/254, 2003 [36] Y. Goto, T. Taniwaki, N. Kinukawa, S. Tobimatsu: Interhemispheric function synchronization at the first step of visual information processing in humans. Clinical Neurophysioligy 115, 1409/1416, [37],, :, 33(1), 13/20, 2005 [38] M. M. Muller, S. Hillyard: Concurrent recording of steady-state and transient eventrelated potentials as indices of visual-spatial selective attention. Clinical Neurophysioligy 111, 1544/1552, [39] D. Regan: Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine, New York, Elsevier, [40] M. detommaso, S. Stramaglia, J. M. Schoffelen, G. Marco, G. Libro, L. Losito, V. Sciruicchio, M. Sardarp, M. Pellicoro, F. M. Puca: Steady-state visual evoked potentials in the low frequency range in migraine: a study of habituation and variability phenomena. International Journal of Phychophysiology 49, 165/174, 2003.

77 73 [41],,,,, :, 51(12), 737/746, [42],, :, 51(12), 713/720, [43] : (VEP) :. 36(4), 267/277, [44] : (9), 572/583, [45] D. Regan: Chromatic adaptation and steady-state evoked potentials, Vision Res 8, 149/158, [46] D. Regan: A high frequency mechanism which underlies visual evoked potentials. Electroenceph Clin Neurophysiol 25, 231/237, [47] D. Regan: Steady-state evoked potentials. J. Opt. Soc. Am. (A) 67, 1475/1489, [48] D. Regan: Human visual evoked potentials. In Picton T. W. ed, Human event-related potentials. Hand-book of Electroencephalography and clinical neurophysiology, revised series, Vol. 3, Elsevier, Amsterdom, 159/243, [49] G. G. Celesia: Visual evoked responses. In: Owen JH, Davis H, editors, Evoked potential testing. Grune and Stratton, Orlando, FL, 1/54, [50] G. G. Celesia: Steady-state and transient visual evoked potentials in clinical practice. Ann N. Y. Acad Sci 388, 290/305, [51] P. Gouras: Progress in Human Visual Evoked Responses, J Clin Neurophysiol 1(1), 77/82, [52] D. P. Crewther, S. G. Crewther: Different temporal structure for form versus surface cortical color systems - Evidence from chromatic non-linear VEP. PLOS one, vol. 5 no. 12, e15266, [53],,, :,, , 41/44, [54] T. Yamasaki, Y. Goto, N. Kinukawa, S. Tibimatsu: Neural basis of photo/chromatic sensitivity in adolescence. Epilepsia, 49(9), 1611/1618, 2008.

78 74 [55] M. S. Livingstone, D. H. Hubel: Anatomy and physiology of a color system in the primate visual cortex, J Neurosci 4 309/356, [56] D. G. Green: Sinusoidal flicker characteristics of the color-sensitive mechanisms of the eye. Vision Res 9, 591/601, [57] C. E. Sternheim, C. R. Cavonius: Sensitivity of the human ERG and VECP to sinusoidally modulated light. Vision Res 12, 1685/1695, [58] M. Kondo, P. A. Sieving: Post-photoreceptoral activity dominates primate photopic 32-Hz ERG for sine-, square- and pulsed stimuli. IOVS 43(7), 2500/2507, [59] F. Teng, Y. Chen, A. M. Choong, S. Gustafson, C. Reichley, P. Lawhead, D. Waddell: Square or sine: Finding a waveform with high success rate of eliciting SSVEP. Computational Intelligence and Neuroscience 2011, , [60] D. S. Nicol, R. Hamilton, U. Shahani, D. L. McCulloch: Monocular and binocular steady-state flicker VEPs: frequency-response functions to sinusoidal and squarewave luminance modulation. Doc Ophthalmol 122, 63/70, [61] W. G. Walter: The convergence and interaction of visual, auditory, and tactile responses in human nonspecific cortex. Ann N.Y. Acd Sci 112, 320/361, [62],, : α,. J88-A(4), 480/489, [63] T. Kawaguchi, H. Jijiwa, S. Watanabe: The dynamics of phase relationship of alpha waves during photic driving, Electroenceph Clin Neurophysiol 87, 88/96, [64] T. Fukami, K. Hayashi, T. Shimada, T. Akatsuka, Y. Saito: Influence of visual stimulus on amplitude and phase of alpha wave as measured by multi-channel EEG. IEICE Trans Inf and Syst E86-D(11), 2444/2451, [65] R. A. Pigeau, A. M. Frame: Steady-state visual evoked responses in high and low alpha subject. Electroencephalogr Clin Neurophysiol, 84(2), 101/109, [66] V. V. Lazarev, D. M. Simpson, B. M, Schubsky, L. C. deazevedo: Photic driving in the electroencephalogram of children and adolescents: harmonic structure and relation to the resting state. Braz J Med Biol Res 34, 1573/1584, 2001 [67] A. Birca, L. Carmant, A. Lortie, M. Lassonde: Interaction between the flash evoked SSVEPs and the spontaneous EEG activity in children and adults, Clin Neurophysiol 117, 279/288, 2006

79 75 [68] R. Agarwal, J. Gotman, D. Flanagan, B. Rosenblatt: Automatic EEG analysis during long-term monitoring in the ICU. Electroencephalogr Clin Neurophysiol 107(1), 44/58, [69] H. Kuwahara, H. Higashi, Y. Mizuki, S. Matsunari, M. Tanaka, K. Inanaga: Automatic real-time analysis of human sleep stages by an interval histogram method. Electroencephalogr Clin Neurophysiol. 70, 220/229, 1988 [70] G. W. Harding: An automated seizure monitoring system for patients with indwelling recording electrodes. Electroencephalogr Clin Neurophysiol. 86, 428/437, [71] I. Osorio, M. G. Frei, S. B. Wilkinson: Real-time automated detection and quantitative analysis of seizures and short-term predication of clinical onset. Epilepsia 39(6), 615/627, [72] M. E. Saab, J. Gotman: A system to detect the onset of epileptic seizures in scalp EEG. Clin Neurophysiol. 116, 427/442, [73] M. A. Black, R. D. Jones, G. J. Carroll, A. A. Dingle, I. M. Donaldson, P. J. Parkin: Real-time detection of epileptiform activity in the EEG: a blinded clinical trial. Clin Electroencephalogr. 31(3), 122/130, [74] M. Nakamura, Q. Chen, T. Sugi, A. Ikeda, H. Shibasaki: Technical quality evaluation of EEG recording based on electroencephalographers knowledge. Med Eng Phys 27, 93/100, [75] P. J. Durka, H. Klekowicz, K. J. Blinowska, W. Szelenberger, S. Niemcewicz: A simple system for detection of EEG artifacts in polysomnographic recoedings. IEEE Trans Biomed Eng. 50(4), 526/528, [76],,,,,,, :, 50(5), 433/442, 2012 [77],, : 35(7), 130/137, [78] T. P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, T. J. Sejnowaki: Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clin Neurophysiol. 111, 1745/1758, [79] D. Iyer, G. Zouridakis: Single-trial evoked potential estimation; Comparison between independent component analysis and wavelet denoising. Clin Neurophysiol. 118, 495/504, 2007.

80 76 [80],, :. 24(1), 8/14, [81],,,,, :. 35(4), 392/399, [82],,,, :, 43(3), 447/455, [83],,,,,,, :, 50(6), 599/606, [84] 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) Oct [85],,,,, : 2. 39(1), 10/17, [86],,,,,,, :. 41(1), 1/6, 2013 [87] M. Nakamura, T. Sugi, A. Ikeda, R. Kakigi, H. Shibasaki: Clinical application of automatic integrative interpretation of awake background EEG: quantitative interpretation, report making, and detection of artifacts and reduced vigilance level. Electroencephalogr Clin Neurophysiol 98, 103/112, [88] J. R. Smith, I. Karacan, M. Yang: Automated analysis of the human sleep EEG. Waking Sleeping 2, 75/82, [89] S. Charbonnier, L. Zoubek, S. Lesecq, F. Chapotot: Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging. Comput Biol Med 41, 380/389, [90] S. B. Wilson, C. A. Turner, R. G. Emerson, M. L. Scheuer: Spike detection II: automatic, perception-based detection and clustering. Clin Neurophysiol 110, 404/411, 1999.

81 77 [91] T. Sugi, M. Nakamura, A. Ikeda, H. Shibasaki: Adaptive EEG spike detection: determination of threshold values based on conditional probability. Front Med Biol Eng 11(4), 261/277, [92] Z. Ji, T. Sugi, S. Goto, X. Wang, A. Ikeda, T. Nagamine, H. Shibasaki, M. Nakamura: An automatic spike detection system based on elimination of false positives using the large-area context in the scalp EEG. IEEE Trans Biomed Eng. 58(9), 2478/2488, [93] M. S. Bradnam, A. L. Evans, D. M. I. Montgomery, D. Keating, B. E. Damato, A. Cluckie, D. Allan: A personal computer-based visual evoked potential stimulus and recording system. Doc Opthalmol. 86, 81/93, [94] N. A. de Beer, M. von de Velde, P. J. Cluitmans: Clinical evaluation of a method for automatic detection and removal of artifacts in auditory evoked potential monitoring. J Clin Monit. 11(6), 381/391, [95],,,, :. 38(1), 33/41, [96],,,,, :. 23 (CD-ROM).,, [97] P. Bloomefield: Fourier Analysis of Time Series: An Introduction. Wiley, New York, 118/150, [98] S. Noachtar, C. Binnie, J. Ebersole, F. Mauguiere,A. Sakamoto, B Westmoreland: A glossary of terms most commonly used by clinical electroencephalographers and proposal for the report form for the EEG findings. In: Deuschl G and Eisen A eds. Recommendations for the Practice of Clinical Neurophysiology: Guidelines of the International Federation of Clinical Neurophysiology, Elsevier, Amsterdam, 21/41, [99] O. Rompelman, H. H. Ros: Coherent averaging technique: a tutorial review part 2: trigger jitter, overlapping responses and non-periodic stimulation. J Biomed Eng. 8, 30/35, [100] P. Comon: Independent component analysis, - a new concept?. Signal Proc. 36, 287/314, [101] T. Sugi, S. Tsuchiya, S. Nishida, Y. Goto, S. Tobimatsu, N. Nakamura: Decomposition of VEP and dominant rhythm components during photic stimulation by

82 78 use of EEG model, 2009 IEEE/ICME International Conference on Complex Medical Engineering (CD-ROM). ICME. Arizona, [102],,,,,, :. 21.,, 21, [103], :.,, 106/121, [104] R. Fletcher, M. J. D. Powell: A rapidly convergent descent method for minimization, The Computer Journal 6, 163/168, [105],, : A-5-4,,, 127/142, [106] H. Akaike: A new look at the statistical model identification, IEEE Trans Automat Contr 19, 716/723, [107] S. Tobimatsu, G. G. Celesia, S. B. Cone: Effects of pupil diameter and luminance changes on pattern electroretinograms and visual evoked potentials, Clin Vision Sci 2, 293/302, [108] C. Pieh, D. L. McCulloch, U. Shahani, H. Mactier, M. Bach: Maturation of steadystate flicker VEPs in infants: fundamental and harmonic temporal response frequencies. Doc Ophthalmol 118(2), 109/119, [109] A. Fiorentini, V. Porciatti, M. C. Morrone: Temporal characteristics of colour vision: VEP and psychophysical measurements. In: Valberg A, Lee BB eds, From pigments to perception: advances in understanding visual processes, New York, Plenum Press, 139/149, [110] V. Porciatti, F. Sartucci: Retinal and cortical evoked responses to chromatic contrast stimuli, Brain 119, 723/740, [111] P. Turner: Critical flicker fusion frequency and its modification by a conditioning stimulus of flickering light. J Physiol, vol. 171, 6/8, 1964.

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. ( ),