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研究生： 周鴻鈞
Hung-Chyun Chou
論文名稱： 具延遲校正之創新分散式視覺刺激腦機介面架構於無線連網設備應用
A Novel Distributed Visual Stimuli BCI Architecture with Latency Calibration for Wireless Device Networking Applications
指導教授： 郭重顯
Chung-Hsien Kuo
口試委員: 黃漢邦
Han-Pang Huang
劉益宏
Yi-Hung Liu
林進燈
Chin-Teng Lin
徐國鎧
Kuo-Kai Shyu
鍾聖倫
Sheng-Luen Chung
宋開泰
Kai-Tai Song
蘇順豐
Shun-Feng Su
學位類別： 博士
Doctor
系所名稱： 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年： 2016
畢業學年度： 104
語文別： 英文
論文頁數： 125
中文關鍵詞： 腦機介面 、P300 、區間第二型模糊邏輯系統 、人工蜂群理論演算法 、穩態視覺誘發電位。
外文關鍵詞： brain computer interface, P300, interval type-2 fuzzy logic system, artificial bee colony algorithm, steady-state visual evoked potential
相關次數： 點閱：279  下載：0 
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    • 本論文提出具創新且實際應用之P300腦機介面，以SoD（Stimulus-on-Device）架構建立無線連網設備之腦機介面，不同於常見之SoP（Stimulus-on-Panel）架構，SoD架構提供更加直覺之腦機介面操作方法。然而，P300之特徵辨識依賴刺激時間與反應電位之同步性，由於無線連網之通訊時間延遲，對於SoD架構之腦機介面，其P300特徵之辨識將受到影響。同時，不同使用者P300反應時間之變異性也將造成P300特徵辨識之誤差，因此本論文提出一估測P300反應時間之模型，解決P300時間變異性之問題。本論文首先提出基於人工蜂群理論演算法（Artificial Bee Colony Algorithm ）訓練之區間第二型模糊邏輯系統（Interval Type-2 Fuzzy Logic System），藉由使用者之穩態視覺誘發電位（SSVEP）特徵，估測使用者P300反應之時間，並提出『註冊』之方法，以穩態視覺誘發電位之相位分析，評估無線連網之時間延遲，基於本系統之架構，本論文使用支撐向量機（Supporting Vector Machine）之分類器將擷取之腦波訊號分為觸發與未觸發之刺激。根據實驗結果，基於模糊邏輯系統估測之P300反應時間，七位受測者之資料傳輸率（Information Transfer Rate）平均提升5.03（ bits/ min），並於SoD架構中使用『註冊』之方法，平均提升資料傳輸率3.43 （ bits/ min）。

    This dissertation proposed a novel and practical P300-based BCI constructed of a novel stimulus-on-device (SoD) architecture for wireless networking applications. Instead of a stimulus-on-panel architecture (SoP), the proposed SoD architecture provides an intuitive control scheme. However, because P300 recognitions rely on the synchronization between stimuli and response potentials, the variation of latency between target stimuli and elicited P300 is a concern when applying a P300-based BCI to wireless applications. In addition, the subject’s dependent variation of elicited P300 affects the performance of the BCI. Thus, an estimation model that determines an appropriate interval for P300 feature extraction was discussed in this paper. An artificial bee colony (ABC)-based interval type-2 fuzzy logic system (IT2FLS) was used to find the latency of elicited P300 with a certain range by means of steady-state visual evoked potential (SSVEP) features first. Then, a “Registration” scheme was proposed. The phase lag analysis of SSVEP was conducted to estimate latency delays caused by wireless communications. Finally, a support vector machine (SVM) classifier was adopted to classify extracted epochs into target and non-target stimuli. Experimental results showed that from seven subjects, the information transfer rate (ITR) improved 5.03 bits/ min with the proposed ABC-based IT2FLS for the estimation of P300 latency. Based on the proposed SoD architecture, the average ITR improved 3.43 (bits/ min) after applying “Registration” scheme.

    Acknowledgements i Abstract ii Table of Contents iv List of Tables vii List of Figures viii Nomenclature xi 1 Introduction 1 1.1 Background and Motivation 1 1.2 Contribution 5 1.3 Dissertation Structure 5 2 Literature Review 7 2.1 P300-based BCI 7 2.2 SSVEP-based BCI 8 2.3 Hybrid BCI 9 2.3.1 Visual-based Hybrid BCI 9 2.3.2 Other Hybrid BCI 11 2.4 BCI Classifier 13 2.5 Fuzzy Logic System 13 2.6 ABC Optimization 14 2.7 Spatial Filter for ERP 14 2.8 Literature Review Conclusion 14 3 Brain Computer Interface 18 3.1 Investigation of SSVEP and P300 18 3.2 SoD-based BCI System Architecture 22 3.2.1 Pearson Correlation Coefficient 25 3.2.2 ABC-based IT2FLS for P300-centroid Estimation 26 3.2.3 KM Algorithm 28 3.2.4 ABC Algorithm 30 3.2.5 ABC-based IT2FLS Training 34 3.3 SoD-based BCI 35 3.3.1 SoD-based BCI Scenario 36 3.3.2 SSVEP Phase Lag Analysis 37 3.3.3 Registration Scheme 39 3.3.4 Operation Scheme 41 3.4 CCA-based Spatial Filter and SVM Classifier 42 3.4.1 CCA-based Spatial Filter 42 3.4.2 SVM classifier for P300-based BCI 43 4 Experimental Paradigms 45 4.1 EEG Acquisition 45 4.2 Experimental Paradigm of Developer Layer 45 4.2.1 Visual Stimuli 46 4.2.2 Experimental Protocol 47 4.2.3 ABC-based IT2FLS for P300 Latency Centroid Estimation 47 4.3 P300-based BCI Classification with/ without P300 Latency Estimations 48 4.3.1 Visual Stimuli 49 4.3.2 Experimental Protocol 49 4.3.3 P300-based BCI classifier Training 50 4.4 SoD-based P300 BCI 51 4.4.1 Experimental Setup 51 4.4.2 Visual Stimuli 52 4.4.3 Experimental Protocol 53 4.4.4 SoD-based P300 BCI Classifier Training 54 4.5 SoD-based P300 BCI for Online Application 54 5 Experiments 56 5.1 Experimental Results of Developer Layer 57 5.2 P300-based BCI with/ without P300 Latency Estimations 81 5.3 SoD-based P300 BCI 85 5.3.1 Phase Lag Analysis for tSoD estimation 85 5.3.2 SoD-based BCI Performance 89 5.3.3 SoD-based P300 BCI by Using High-power LED module 90 5.3.4 SoD-based BCI Improvement Strategy 92 5.4 Non-cerebral Electrical Signal Effects 97 6 Conclusion and Future Works 99 Reference 100 Resume 108

    [1] W.H.O. “Spinal cord injury. Internet: http://www.who.int/mediacentre/factsheets/fs384/en, Nov. 1, 2013 [Aug. 14, 2015].
    [2] L. Citi, R. Poli, C. Cinel and F. Sepulveda, “P300-based BCI mouse with genetically-optimized analogue control,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 16, no. 1, pp. 51-61, Feb. 2008.
    [3] Kaplan, S. Shishkin, I. Ganin, I. Basyul and A. Zhigalov, “Adapting the P300-based brain-computer interface for gaming: a review,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 5, no. 2, pp. 141-149, Jun. 2013.
    [4] J. Park and K.E. Kim, “A POMDP approach to optimizing P300 speller BCI paradigm,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, no. 4, pp. 584-594, Jul. 2012.
    [5] L. Bi, X.A. Fan, N. Luo, K. Jie, Y. Li and Y. Liu, “A head-up display-based P300 brain-computer interface for destination selection,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 4, pp. 1996-2001, Dec. 2013.
    [6] C. Postelnicu and D. Talaba, “P300-based brain-neuronal computer interaction for spelling applications,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 2, pp. 534-543, Jan. 2013.
    [7] J.D. Bayliss, “Use of the evoked potential P3 component for control in a virtual apartment,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 113-116, Jun. 2003.
    [8] N. Chumerin, N.V. Manyakov, M. van Vliet, A. Robben, A. Combaz and M. Van Hulle, “Steady-state visual evoked potential-based computer gaming on a consumer-grade EEG device,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 5, no. 2, pp. 100-110, Jun. 2013.
    [9] E. Yin, Z. Zhou, J. Jiang, Y. Yang, Y. Liu and D. Hu, “A dynamically optimized SSVEP brain-computer interface (BCI) speller,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 6, pp. 1447-1456, Apr. 2015.
    [10] M. Kaper, P. Meinicke, U. Grossekathoefer, T. Lingner and H. Ritter, “BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1073-1076, May. 2004.
    [11] E.W. Sellers, A. Kubler and E. Donchin, “Brain-computer interface research at the university of south Florida cognitive psychophysiology laboratory: the P300 speller,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 221-224, Jun. 2006.
    [12] S.M. Kamp, A.R. Murphy and E. Donchin, “The component structure of event-related potentials in the P300 speller paradigm,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 6, pp. 897-907, Nov. 2013.
    [13] C. Escolano, J.M. Antelis and J. Minguez, “A telepresence mobile robot controlled with a noninvasive brain-computer interface,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, pp. 793-804, Jun. 2012.
    [14] Y. Chae, J. Jeong and S. Jo, “Toward brain-actuated humanoid robots: asynchronous direct control using an EEG-based BCI,” IEEE Transactions on Robotics, vol. 28, 1131-1144, Oct. 2012.
    [15] V. Gandhi, G. Prasad, D. Coyle, L. Behera and T.M. McGinnity, “EEG-Based mobile robot control through an adaptive brain-robot interface,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 44, pp. 1278-1285, Sep. 2014.
    [16] J. Long, Y. Li, H. Wang, T. Yu, J. Pan and F. Li, “A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, no. 5, pp. 720-729, Sep. 2012.
    [17] R. Xu, N. Jiang, N. Mrachacz-Kersting, C. Lin, G. Asin Prieto, J. C. Moreno, J. L. Pons, K. Dremstrup and D. Farina, “A closed-loop brain-computer interface triggering an active ankle-foot orthosis for inducing cortical neural plasticity,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 2, pp. 2092-2101, Jul. 2014.
    [18] Frisoli, C. Loconsole, D. Leonardis, F. Banno, M. Barsotti, C. Chisari and M. Bergamasco, “A new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitation in real-world tasks,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 42, 1169-1179, Nov. 2012.
    [19] R. Ortner, B.Z. Allison, G. Korisek, H. Gaggl and G. Pfurtscheller, “An SSVEP BCI to control a hand orthosis for persons with tetraplegia,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19, no. 1, pp. 1-5, Feb. 2011.
    [20] G.R. Muller-Putz and G. Pfurtscheller, “Control of an electrical prosthesis with an SSVEP-based BCI,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 1, pp. 361-364, Jan. 2008.
    [21] E. Donchin, K.M. Spencer and R. Wijesinghe, “The mental prosthesis: assessing the speed of a P300-based brain-computer interface,” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 174-179, Jun. 2000.
    [22] N. Xu, X. Gao, B. Hong, X. Miao, S. Gao and F. Yang, “BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1067-1072, Jun. 2004.
    [23] H. Cecotti and A. Graser, “Convolutional neural networks for P300 detection with application to brain-computer interfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 3, pp. 433-445, Jun. 2011.
    [24] H. Serby, E. Yom-Tov and G.F. Inbar, “An improved P300-based brain-computer interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 1, pp. 89-98, Mar. 2005.
    [25] Y. Zhang, G. Zhou, J. Jin, M. Wang, X. Wang and A. Cichocki, “L1-regularized multiway canonical correlation analysis for SSVEP-based BCI,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 6, pp. 887-896, Nov. 2013.
    [26] J. Legeny, R. Viciana-Abad and A. Lecuyer, “Toward contextual SSVEP-based BCI controller: smart activation of stimuli and control weighting,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 5, no. 2, pp. 111-116, Jun. 2013.
    [27] C. Jia, X. Gao, B. Hong and S. Gao, “Frequency and phase mixed coding in SSVEP-based brain-computer interface,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 1, pp. 200-206, Jan. 2011.
    [28] Y. Kimura, T. Tanaka, H. Higashi and N. Morikawa, “SSVEP-based brain-computer interfaces using FSK-modulated visual stimuli,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 10, pp. 2831-2838, Oct. 2013.
    [29] H. Cecotti, “A self-paced and calibration-less SSVEP-based brain-computer interface speller,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, no. 2, pp. 127-133, Apr. 2010.
    [30] H. Gollee, I. Volosyak, A.J. McLachlan, K.J. Hunt and A. Gräser, “An SSVEP-based brain-computer interface for the control of functional electrical stimulation,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 8, pp. 1847-1855, Aug. 2010.
    [31] K.K. Shyu, P.L. Lee, M.H. Lee, M.H. Lin, R.J. Lai and Y.J. Chiu, “Development of a Low-Cost FPGA-Based SSVEP BCI Multimedia Control System,” IEEE Transactions on Biomedical Circuits and Systems, vol.4, no. 2, pp. 125-132, Apr. 2010.
    [32] P.L. Lee, H.C. Chang, T.Y. Hsieh, H.T. Deng and C.W. Sun, “A Brain-Wave-Actuated Small Robot Car Using Ensemble Empirical Mode Decomposition-Based Approach,” IEEE Transaction on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 42, no. 5, pp. 1053-1064, Sep. 2012.
    [33] H.Y. Wu, P.L. Lee, H.C. Chang and J.C. Hsieh, “Accounting for Phase Drifts in SSVEP-Based BCIs by Means of Biphasic Stimulation,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 5, pp. 1394-1402, May 2011.
    [34] P.L. Lee, C.L. Yeh, J.Y.S. Cheng, C.Y. Yang, G.Y. Lan, “An SSVEP-based BCI using high duty-cycle visual flicker,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 12, pp. 3350-3359, Dec. 2011.
    [35] Falzon, K. Camilleri and J. Muscat, “Complex-valued spatial filters for SSVEP-based BCIs with phase coding,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 9, pp. 2486-2495, Sep. 2012.
    [36] Y.K. Wang, S.A. Chen and C.T. Lin, “An EEG-based brain-computer interface for dual task driving detection,” Neurocomputing, vol. 129, pp. 85-93, Apr. 2014.
    [37] Lenhardt, M. Kaper and H.J. Ritter, “An adaptive P300-based online brain-computer interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 16, no. 2, pp. 121-130, Apr. 2008.
    [38] M. Salvaris, C. Cinel, L. Citi and R. Poli, “Novel protocols for P300-based brain-computer interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, no. 1, pp. 8-17, Jan. 2012.
    [39] R.C. Panicker, S. Puthusserypady and S. Ying, “Adaptation in P300 brain-computer interfaces: a two-classifier constraining approach,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 12, pp. 2927-2935, Dec. 2010.
    [40] S.T. Ahi, H. Kambara and Y. Koike, “A dictionary-driven P300 speller with a modified interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19, no. 1, pp. 6-14, Feb. 2011.
    [41] Z. Lin, C. Zhang, W. Wu and Xiaorong Gao, “Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, pp. 2610-2614, Dec. 2006.
    [42] L.J. Trejo, R. Rosipal and B. Matthews, “Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 225-229, Jan. 2006.
    [43] S.P. Kelly, E.C. Lalor, R.B. Reilly and J.J. Foxe, “Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 2, pp. 172-178, Jun. 2005.
    [44] Friman, I. Volosyak, A. Graser, “Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 4, pp. 742-750, Apr. 2007.
    [45] R. Kus, A. Duszyk, P. Milanowski, M. Labecki, M. Bierzynska, Z. Radzikowska, M. Michalska, J. Zygierewicz, P. Suffczynski, and P.J. Durka, “On the Quantification of SSVEP Frequency Responses in Human EEG in Realistic BCI Conditions,” PLoS ONE, vol. 8, no. 10, p.p. e77536, Oct. 2013.
    [46] D.P. McMullen, G. Hotson, K.D. Katyal, B.A.Wester, M.S. Fifer, T.G. McGee, A. Harris, M.S. Johannes, R.J. Vogelstein, A.D. Ravitz, W.S. Anderson, N.V. Thakor and N.E. Crone “Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, pp. 784-796, Jul. 2014.
    [47] M.E. Thurlings, J.B.F. Van Erp, A.M. Brouwer and P. Werkhoven, “Controlling a tactile ERP-BCI in a dual task,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 5, no. 2, pp. 129-140, Jun. 2013.
    [48] Y. Erwei, T. Zeyl, R. Saab, T. Chau, D. Hu and Z. Zhou, “A hybrid brain-computer interface based on the fusion of P300 and SSVEP scores,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 4, pp. 693-701, Jul. 2015.
    [49] R.C. Panicker, S. Puthusserypady and Y. Sun, “An asynchronous P300 BCI with SSVEP-based control state detection,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 6, pp. 1781-1788, Jun. 2011.
    [50] E. Yin, Z. Zhou, J. Jiang, F. Chen, Y. Liu and D. Hu, “A speedy hybrid BCI spelling approach combining P300 and SSVEP,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 2, pp. 473-482, Feb. 2014.
    [51] X. Fan, L. Bi, T. Teng, H. Ding and Y. Liu, “A brain-computer interface-based vehicle destination selection system using P300 and SSVEP signals,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 1, pp. 274-283, Feb. 2015.
    [52] Y. Li, J. Pan, F. Wang and Z. Yu, “A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 11, pp. 3156-3166, Nov. 2013.
    [53] B.Z. Allison, C. Brunner, V. Kaiser, G.R. Müller-Putz, C. Neuper and G. Pfurtscheller, “Toward a hybrid brain–computer interface based on imagined movement and visual attention,” Journal of Neural Engineering, vol. 7, no. 2, Mar. 2010.
    [54] G. Pfurtscheller, T. Solis-Escalante, R. Ortner, P. Linortner and G.R. Müller-Putz, “Self-paced operation of an SSVEP-based orthosis with and without an imagery-based “brain switch:” a feasibility study towards a hybrid BCI,” IEEE Transactions on Neural System and Rehabilitation Engineering, vol. 18, no. 4, Aug. 2010.
    [55] B. Rebsamen, E. Burdet, Q. Zeng, H. Zhang, M. Ang, C.L. Teo, C. Guan and C. Laugier, “Hybrid P300 and mu-beta brain computer interface to operate a brain controlled wheelchair,” in Proceedings of the 2nd International Convention on Rehabilitation Engineering and Assistive Technology, 2008, pp. 51-55.
    [56] S. Amiri, R. Fazel-Rezai and V. Asadpour, “A Review of Hybrid Brain-Computer Interface Systems,” Internet: http://www.hindawi.com/journals/ahci/2013/187024/, Jun. 5, 2013 [Aug. 14, 2015].
    [57] Rakotomamonjy, V. Guigue, “BCI competition III: dataset II- ensemble of SVMs for BCI P300 speller,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 3, pp. 1147-1154, Mar. 2008.
    [58] Y.H. Liu, J.T. Weng, Z.H. Kang, J.T. Teng and H.P. Huang, “An improved SVM-based real-time P300 speller for brain-computer interface” in Proceedings of IEEE International Conference on Systems Man and Cybernetics (SMC), 2010, pp. 1748-1754.
    [59] Y.H. Liu, C.A. Cheng and H.P. Huang, “Novel feature of the EEG based motor imagery BCI system: Degree of imagery,” in Proceedings of IEEE International Conference on System Science and Engineering (ICSSE), 2011, pp. 515-520.
    [60] Y.H. Liu, C.W. Huang and Y.T. Hsiao, “Comparison of methods for a motor imagery-based two-state self-paced brain-computer interface,” in Proceedings of International Conference on Advanced Robotics and Intelligent Systems (ARIS), 2013, pp. 174-178.
    [61] Y.H. Liu, C.T. Wu, Y.H. Kao and Y.T. Chen, “Single-trial EEG-based emotion recognition using kernel Eigen-emotion pattern and adaptive support vector machine,” in Proceedings of IEEE International Conference on Engineering in Medicine and Biology Society (EMBC), 2013, pp. 4306-4309.
    [62] Y. Zhang, G. Zhou, Q. Zhao, J. Jin, X. Wang, and A. Cichocki, “Spatial-temporal discriminant analysis for ERP-based brain-computer interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 2, pp. 233-243, Mar. 2013.
    [63] R. Xu, N. Jiang, C. Lin, N. Mrachacz-Kersting, K. Dremstrup, and D. Farina, “Enhanced low-latency detection of motor intention from EEG for closed-loop brain-computer interface applications,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 2, pp. 288-296, Feb. 2014.
    [64] A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, “Multiclass brain-computer interface classification by riemannian geometry,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, pp. 920-928, Apr. 2012.
    [65] C. Vidaurre, M. Kawanabe, P. von Bunau, B. Blankertz, and K.R. Muller, “Toward unsupervised adaptation of LDA for brain-computer interfaces,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 3, pp. 587-597, Mar. 2011.
    [66] B. Rivet, A. Souloumiac, V. Attina, and G. Gibert, “xDAWN algorithm to enhance evoked potentials: application to brain-computer interface,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 8, pp. 2035-2043, Aug. 2009.
    [67] X. Lei, P. Yang, and D. Yao, “An empirical Bayesian framework for brain-computer interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 17, no. 6, pp. 521-529, Jul. 2009.
    [68] M. Higger, M. Akcakaya, H. Nezamfar, G. LaMountain, U. Orhan, and D. Erdogmus, “A Bayesian framework for intent detection and stimulation selection in SSVEP BCIs,” IEEE Signal Processing Letter, vol. 22, no. 6, pp. 743-747, Jun. 2015.
    [69] H. Suk and S.W. Lee, “A novel Bayesian framework for discriminative feature extraction in brain-computer interfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 2, pp. 286-299, Apr. 2012.
    [70] Y.J. Chen, A.R.A. See, and S.C. Chen, “SSVEP-based BCI classification using power spectrum analysis,” Electrical Letter, vol. 50, no. 10, pp. 735-737, May. 2014.
    [71] C.T. Lin, M. Prasad and A. Saxena, “An improved polynomial neural network classifier using real-coded genetic algorithm,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. PP, no. 99, pp. 1-13, Mar. 2015.
    [72] R. Palaniappan, “Utilizing gamma band to improve mental task based brain-computer interface design,” IEEE Trans. Neural Syst. Rehab. Eng., vol. 14, no. 3, pp. 299-303, Sep. 2006.
    [73] G. Pfurtscheller and C. Neuper, “Motor imagery and direct brain-computer communication,” IEEE Proceeding, vol. 89, no. 7, pp. 1123-1134, Jul. 2001.
    [74] J.M. Cano-Izquierdo, J. Ibarrola, and M. Almonacid, “Improving motor imagery classification with a new BCI design using neuro-fuzzy S-dFasArt,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, no. 1, pp. 2-7, Jan. 2012.
    [75] H. Cecotti and A. Graser, “Convolutional neural networks for P300 detection with application to brain-computer interfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 3, pp. 433-445, Mar. 2011.
    [76] D. Coyle, G. Prasad, and T.M. McGinnity, “Faster self-organizing fuzzy neural network training and a hyperparameter analysis for a brain-computer interface,” IEEE Transactions on Systems, Man, and Cybernetics: Part B: Cybernetics, vol. 39, no. 6, pp. 1458-1471, Dec. 2009.
    [77] C.L. Lin, Y, M, Chang, C.C. Hung, C.D. Tu and C.Y. Chuang, “Position estimation and smooth tracking with a fuzzy-logic-based adaptive strong tracking Kalman filter for capacitive touch panels,” IEEE Transactions on Industrial Electronics, vol. 62, no. 8, pp. 5097-5108, Jun. 2015.
    [78] H. Chung, C. Hou and Y. Chen, “Indoor intelligent mobile robot localization using fuzzy compensation and Kalman filter to fuse the data of gyroscope and magnetometer,” IEEE Transactions on Industrial Electronics, vol. 62, no. 10, pp. 6436-6447, Mar. 2015.
    [79] S.C. Wang and Y.H. Liu, “A PSO-based fuzzy-controlled searching for the optimal charge pattern of li-ion batteries,” IEEE Transactions on Industrial Electronics, vol. 62, no. 5, pp. 2983-2993, Oct. 2015.
    [80] Z. Li, C.Y. Su, L. Wang, Z. Chen, T. Chai, “Nonlinear disturbance observer-based control design for a robotic exoskeleton incorporating fuzzy approximation,” IEEE Transactions on Industrial Electronics, vol. 62, no. 9, pp. 5763-5775, Jun. 2015.
    [81] E. Kayacan, E. Kayacan and M.A. Khanesar, “Identification of nonlinear dynamic systems using type-2 fuzzy neural networks—a novel learning algorithm and a comparative study,” IEEE Transactions on Industrial Electronics, vol. 62, no. 3, pp. 1716-1724, Aug. 2015.
    [82] C.F. Juang, Y.H. Chen and Y.H. Jhan, “Wall-following control of a hexapod robot using a data-driven fuzzy controller learned through differential evolution,” IEEE Transactions on Industrial Electronics, vol. 62, no. 1, pp. 611-619, Apr. 2014.
    [83] L.A. Zadeh, “The Concept of a Linguistic Variable and Its Application to Approximate Reasoning-1,” Information Sciences, vol. 8, pp.199-249, 1975.
    [84] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, Apr. 2007.
    [85] N.N. Karnik and J.M. Mendel, “Type-2 fuzzy logic systems: type-reduction,” in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 1998, vol. 2, pp. 2046-2051.
    [86] N.N. Karnik and J.M. Mendel, “Centroid of a type-2 fuzzy set,” Information Sciences, vol. 132, pp. 195-220, Feb. 2001.
    [87] M. Spüler, A. Walter, W. Rosenstiel and M. Bogdan, “Spatial filtering based on canonical correlation analysis for classification of evoked or event-related potentials in EEG data,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 6, pp. 1097-1103, Nov. 2014.
    [88] C.C. Chang and C.J. Lin, “LIBSVM,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, pp. 1-27, Apr. 2011.
    [89] K.K. Shyu, Y.J. Chiu, P.L. Lee, J.M. Liang and S.H. Peng, “Adaptive SSVEP-Based BCI System With Frequency and Pulse Duty-Cycle Stimuli Tuning Design,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 5, pp. 697-703, Sep. 2013.
    [90] J.J. Kim, T. Hwang, M. Kim, E. Oh, M. Hwangbo, M.K. Kim and S.P. Kim, “The effect of stimulus type and distance on neural control of a smart TV,” in Proceedings of IEEE/ EMBS International Conference on Neural Engineering, 2013, pp. 1343-1345.
    [91] J. Polich, “Updating P300: An integrative theory of P3a and P3b,” Clinical Neurophysiology, vol. 118, no. 10, pp. 2128-2148, Oct. 2007.

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