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1 Brain Computer Interface

2 i I II A.1 Linear Discriminant Analysis A

3 ii 31 32

4 iii 2.1 BCI Hz S S S

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6 Brain-Computer Interface BCI 1 [1] BCI 2 Amyotrophic Lateral Sclerosis: ALS [3, 4] [5] BCI functional Magnetic Resonance Imaging fmri Near Infrared Spectroscopy NIRS Electrocorticography ECoG Electroencephalography EEG BCI EEG EEG-based BCI [6, 7] EEG-based BCI EEG Pfurtscheller Graz-BCI [8] 8-13Hz Linear Discriminant Analysis LDA [9] BCI LDA Support Vector Machine SVM [10] 1 Brain-Machine Interface BMI [2]

7 2 1 BCI 1 BCI 1 EEG [8, 11, 12] EEG Event-Related Desynchronization ERD 3 Single Trial [13] 1.2 BCI Forward Error Correction FEC Automatic Repeat request ARQ Hybrid ARQ HARQ HARQ Reliability-Based Hybrid ARQ RB-HARQ [14] BCI BCI Dornhege [15] Hz

8 BCI 3 EEG BCI 4 BCI BCI BCI

9 4 2 2 EEG-based BCI EEG 2.1 BCI Quadratic Discriminant Analysis QDA Linear Discriminant Analysis LDA [10] k x k p f k (x) = f(x µ k, Σ k ) f k (x) x u k FEC Forward Error Correction ARQ Automatic Repeat Request Hybrid ARQ HARQ HARQ Basic Type-I HARQ Type-I HARQ Type-II HARQ Type-I Type-II [16] HARQ Reliability-Based Hybrid ARQ RB-HARQ [14] RB-HARQ BCI 1 A A.2

10 5 Intent Computer Neurons Distractions Sleepiness Classification Electrical Noise Feature Extraction Scalp EEG System Computer 2.1: BCI

11 : H E L O BCI HELLO {H E L O} {0 1} 2.1 BCI Constant ARQ Reliability-Based ARQ RB-ARQ RB-HARQ FEC Millán [17] unknown Basic Type-I HARQ Basic RB-ARQ Constant ARQ Basic ARQ RB-ARQ t x ut T X u = {x u1, x u2,..., x ut } K x ut u k K f k (x) π k k

12 7 4 P (k X u ) = t ˆπ ˆf k k (x ut ) l K t ˆπ l ˆf l (x ut ) (2.1) ˆ λ ˆk max P (k X u ) λ (2.2) k ˆk = arg max P (k X u ) (2.3) k Reliability-Based Automatic Repeat request RB-ARQ 1. k K f k (x) π k 1 X u = {x u1 } X u x ut x ut Constant ARQ Basic RB-ARQ T 2.3 X u = {x u1 x u1 R} K = {0, 1} k = 0 k = λ Basic RB-ARQ T 1/T x ut = ( 1 T t x ut x ut R) Constant ARQ 4 A.1 5 A.1

13 8 2 max k P (k x ut ) λ x ut c T c T = c 1 /T (2.4) RB-ARQ T Basic RB-ARQ Constant ARQ RB-ARQ Basic RB-ARQ Constant ARQ 2 K = {0, 1} µ 0 µ 1 σ 2 = σ 2 0 = σ d = 0.2 d = 1 Constant ARQ Basic RB-ARQ RB-ARQ 2.5 RB-ARQ 1 1 d = µ 0 µ 1 σ d 2.6 (2.5) µ 0 µ 1 2 s s 2 1 (2.6) s 2 k k

14 9 情報源送信機通信路受信機受信者メッセージ送信信号雑音源受信信号メッセージ 2.2:

15 10 2 Likelihood µ 0 µ L µ 1 x u1 2.3: µ L = (µ 0 + µ 1 )/ max k P(k x u1 ) µ 0 µ L µ 1 x u1 2.4: λ = 0.7

16 Accuracy [%] Time [sec] Constant ARQ Basic RB-ARQ RB-ARQ (a) d = Accuracy [%] Time [sec] Constant ARQ Basic RB-ARQ RB-ARQ (b) d = 1 2.5:

17 RB-ARQ EEG I [18] 3.1 Pz A2 1000Hz Polymate AP A B Hz Hz

18 3.1. I 13 [19] 5% [20] 3.1 p p (= 0.05/22) A A B 8-13Hz x k (n)(k {A, B}, n = 1, 2,..., 240) 2 n = 1, 2,..., n = 121, 122,..., 240 A 1. T = 1 n = 121, k = A 2. X u x ut x k (n) T = 1 n = n+1 X u = ϕ T = T + 1 n = n + 1 n < B k = B 10 Constant ARQ T Basic RB-ARQ RB-ARQ λ (a)(c)(e) 5 20 max k P (k x ut ) λ x ut 3.3(b)(d)(f) A C λ RB-ARQ 3.4 = 100 [%] (3.1)

19 14 3 Fp1 Fp2 F7 F3 Fz F4 F8 A1 A2 T3 C3 Cz C4 T4 T5 P3 Pz P4 T6 O1 O2 3.1: Thought A Thought B Boundary 25 Frequency Amplitude [uv] 3.2: 8-13Hz

20 3.1. I : A B 1 4.3E E E E E E E E E E E E E E E E E E E E E E-01

21 Constant ARQ Basic RB-ARQ RB-ARQ Time [sec] Amplitude [µv] Accuracy [%] 5 MA5 Threshold MA20 Boundary Data Number (a) Accuracy and Transmission Time (Subject A) (b) Moving Average and Threshold (Thought B at Subject A) Time [sec] Amplitude [µv] Constant ARQ 2 Basic RB-ARQ RB-ARQ Accuracy [%] 5 4 MA5 MA20 Threshold Boundary Data Number (c) Accuracy and Transmission Time (Subject B) (d) Moving Average and Threshold (Thought B at Subject B) Time [sec] Amplitude [µv] Constant ARQ 2 Basic RB-ARQ RB-ARQ Accuracy [%] 4 MA5 Threshold MA20 Boundary Data Number (e) Accuracy and Transmission Time (Subject C) (f) Moving Average and Threshold (Thought A at Subject C) 3.3:

22 3.1. I Theoretical Value Experimental Value 8 7 Time [sec] Accuracy [%] (a) Accuracy and Transmission Time (Subject A) Theoretical Value Experimental Value Time [sec] Accuracy [%] (b) Accuracy and Transmission Time (Subject C) 3.4:

23 (a) Basic RB-ARQ RB-ARQ Constant ARQ A B A RB-ARQ T < 5 RB-ARQ T > 5 Basic RB-ARQ λ 3.3(c) RB-ARQ Basic RB-ARQ Constant ARQ 3.3(d) 20 B Constant ARQ RB-ARQ B Basic RB-ARQ RB-ARQ B RB-ARQ EEG 2 [21, 22] 3.3(e) Constant ARQ Basic RB-ARQ RB-ARQ 50% 3.3(f) 90 Basic RB-ARQ RB-ARQ

24 3.2. II 19 λ λ A BCI C RB-ARQ (a) 2.4% 4.6% 3.4(b) 64.7% 44.2% 3.4(b) 2.5 d [23] II BCI Competition BCI EEG Millán data set V [17] 1 RB-ARQ K = {L, R, W } 15 4 C3 Cz C4 CP1 CP2 P3 Pz P Hz URL iii/

25 : S1 S2 S3 C3 Cz C4 CP1 CP2 C3 Cz C4 CP1 C3 Cz C4 CP1 CP2 Pz 62.5msec 16Hz [24] 2Hz 8Hz 30Hz sec LDA QDA A 2 RB-ARQ A 3.2 LDA 4 1 A A A A A RB-ARQ 0.5sec 1. T = 1 n = 1, k = L URL iii/results/martigny/irenesturm desc. txt

26 3.2. II : 5 Rank S1 S2 S3 AVG * X u x ut x k (n) 3. T T = T + 1 n = n T = 1 n = n+1 X u = ϕ T = T + 1 n = n + 1 n < T 8 0.5sec = msec 3 T %8 = 0 8 RB-ARQ 1 3 S Hz T 8 2Hz T %8 = 0 λ {λ i λ i = exp( i/2) + 1, i = 0, 1,..., 49} 16Hz 2Hz A A A A λ

27 Accuracy [%] Hz 16Hz Time [sec] 3.5: S1 16Hz 2Hz T = 8 16Hz T = 9 2Hz T 16 RB-ARQ I 1sec λ % I

28 3.2. II Accuracy [%] Hz 16Hz Time [sec] 3.6: S Accuracy [%] Hz 16Hz Time [sec] 3.7: S3

29 Brain-Computer Interface BCI ALS BCI RB-ARQ I II II 4.2 BCI 2 BCI

30 25 A.1 Linear Discriminant Analysis k K X k = {x k x k R p } x k A.1 p { 1 f k (x) = f(x µ k, Σ k ) = exp 1 } (2π) p/2 Σ k 1/2 2 (x µ k) Σ 1 k (x µ k) (A.1) k µ k Σ k π k ˆµ k = 1 X k ˆΣ k = 1 X k 1 ˆπ k = X k / X x k X k x k x k X k (x k ˆµ k )(x k ˆµ k ) (A.2) (A.3) (A.4) ˆ X = k K X k ˆΣ = ˆΣ k ˆΣ = 1 X K k K x k X k (x k ˆµ k )(x k ˆµ k ) x u k (A.5) P (k x u ) = ˆπ k ˆfk (x u ) l K ˆπ l ˆf l (x u ) (A.6) x u ˆk ˆk = arg max P (k x u ) k (A.7) Linear Discriminant Analysis LDA Quadratic Discriminant Analysis QDA

31 26 T x u1, x u2,..., x ut k t P (k x u1, x u2,..., x ut ) = ˆπ ˆf k k (x ut ) l K t ˆπ ˆf l l (x ut ) (A.8) T 1 k f k (x) = f(x µ k, Σ k /T ) x ut = 1 T P (k x u1, x u2,..., x ut ) = t x ut ˆπ k ˆ fk ( x ut ) l K ˆπ l ˆ fl ( x ut ) (A.9) A.2 A.1 LDA QDA K k K x k R p X k k µ k µ S w S b ˆµ k = 1 X k ˆµ = 1 X x k X k x k x k k K x k X k (A.10) (A.11) Ŝ w = (x k ˆµ k )(x k ˆµ k ) (A.12) k K x k X k Ŝ b = k K( ˆµ k ˆµ)( ˆµ k ˆµ) (A.13) ˆ X = k K X k A.14 w R p w Ŝ b w w Ŝ ww (A.14) Ŝ 1 w Ŝ= b P AP (A.15) A P A = (diag(d 1, d 2,..., d p ) d 1 d 2 d p ) P = (u 1 u 2 u p )

32 A A.14 d 1 u 1 w Tr(A) = p i d i (A.16) T 1 k ˆ S w = Ŝw T (A.17) Tr(Ā) = T Tr(A) (A.18)

33 28 [1] Birbaumer, N.: Breaking the silence: Brain-computer interfaces (BCI) for communication and motor control, Psychophysiology, Vol. 43, No. 6, pp (2006). [2] ALS ALS Retrieved Jan. 10th, 2009 from [3] Retrieved Jan. 3rd, 2009 from c060524a.html ( ). [4] Retrieved Jan. 3rd, 2009 from ( ). [5] OCZTechnology: Retrieved Jan. 11th, 2009 from products/ocz peripherals/nia-neural impulse actuator. [6] Tateoka, Y., Yoshikawa, T., Furuhashi, T. and Tanaka, K.: A basic study on electroencephalogram-based control, Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on advanced Intelligent Systems (SCIS & ISIS 2006), pp (2006). [7] BCI Vol. J92-D, No. 1, pp (2009). [8] Scherer, R., Muller, G., Neuper, C., Graimann, B. and Pfurtscheller, G.: An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate, IEEE Trans. on Biomedical Engineering, Vol. 51, No. 6, pp (2004). [9] Hastie, T., Tibshirani, R. and Friedman, J.: The Elements of Statistical Learning, Springer (2001).

34 29 [10] Blankertz, B., Müller, K. R., Krusienski, D. J., Schalk, G., Wolpaw, J. R., Schlogl, A., Pfurtscheller, G., Millán, J. R., Schroder, M. and Birbaumer, N.: The BCI competition III: validating alternative approaches to actual BCI problems, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 14, No. 2, pp (2006). [11] Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A. and Shenoy, K. V.: A highperformance brain-computer interface, nature, Vol. 442, pp (2006). [12] McFarland, D. J., Sarnacki, W. A. and Wolpaw, J. R.: Brain-computer interface (BCI) operation: optimizing information transfer rates, Biological Psychology, Vol. 63, pp (2003). [13] Kalcher, J., Flotzinger, D., Neuper, C., Gölly, S. and Pfurtscheller, G.: Graz braincomputer interface II: towards communication between humans and computers based on online classification of three different EEG patterns, Medical and Biological Engineering and Computing, Vol. 34, No. 5, pp (1996). [14] Shea, J. M.: Reliability-Based Hybrid ARQ, IEE Electronics Letters, Vol. 38, No. 13, pp (2002). [15] Dornhege, G.: Increasing Information Transfer Rates for Brain-Computer Interfacing, PhD Thesis, University of Potsdam (2006). [16] W-CDMA Hybrid ARQ Vol. 100, No. 343, pp (2000). [17] Millán, J.R.: On the need for on-line learning in brain-computer interfaces, Int. Joint Conf. Neural Networks, pp (2004). [18] Jasper, H. H.: The Ten Twenty Electrode System of the International Federation, Electroencephalography and Clinical Neurophysiology, Vol. 10, pp (1958). [19] (2003). [20] (1997). [21] Vidaurre, C., Schlogl, A., Cabeza, R., Scherer, R. and Pfurtscheller, G.: A fully on-line adaptive BCI, IEEE Trans. on Biomedical Engineering, Vol. 53, No. 6, pp (2006).

35 30 [22] Sykacek, P., Roberts, S. and Stokes, M.: Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation, IEEE Trans. on Biomedical Engineering, Vol. 51, No. 5, pp (2004). [23] Gasser, T., Bacher, P. and Mocks, J.: Transformations towards the normal distribution of broad band spectral parameters of the EEG, Electroencephalography and clinical neurophysiology, Vol. 53, pp (1982). [24] Ricker, D. W.: Echo Signal Processing, Springer (2003).

36 31 ( ) 1. BCI 2 D Vol.J92-D, No.1, pp , BCI Reliability-Based ARQ ( ) 1. H.Takahashi, T.Yoshikawa and T.Furuhashi: A Study on Application of Reliability Based Automatic Repeat Request to Brain Computer Interfaces, In Proc. the 15th Int. Conf. on Neural Information Processing of the Asia-Pacific Neural Network Assembly, pp , ( ) pp , Brain Computer Interface 2, vol. 107, no. 263, pp , BCI Reliability-Based ARQ 4, pp , BCI 2008, p.20, 2008.

37 32 COE

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