박사

Optimization of the features and training data for improving performance of motor imagery EEG-based BCI = 동작 상상 뇌파-기반 BCI의 성능 향상을 위한 특징과 훈련 데이터 최적화

이다빛 2018년
논문상세정보
' Optimization of the features and training data for improving performance of motor imagery EEG-based BCI = 동작 상상 뇌파-기반 BCI의 성능 향상을 위한 특징과 훈련 데이터 최적화' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 응용 물리
  • brain-computer-interface
  • common spatial pattern
  • motor imagery
  • wavelet trans- form
  • 미디어공학
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
4,713 0

0.0%

' Optimization of the features and training data for improving performance of motor imagery EEG-based BCI = 동작 상상 뇌파-기반 BCI의 성능 향상을 위한 특징과 훈련 데이터 최적화' 의 참고문헌

  • Z. Ma, F. Nie, Y. Yang, J. R. Uijlings, and N. Sebe, "Web image annotation via subspace-sparsity collaborated feature selection," IEEE Trans. Multimedia, vol. 14, no. 4, pp. 1021–1030, Aug. 2012.
  • Z. J. Koles, M. S. Lazar, and S. Z. Zhou, "Spatial patterns underlying population differences in the background EEG," Brain Topogr., vol. 2, no. 4, pp. 275–284, Jun. 1990.
  • Z. Fu, A. Robles-Kelly and J. Zhou, "Mixing linear SVMs for nonlinear classification," IEEE Trans. Neural Networks, vol. 21, no. 12, pp. 1963-1975, Nov. 2010.
  • Yu L, Liu H. "Feature selection for high-dimensional data: A fast correlation-based filter solution," Proceedings of the 20th International Conference on Machine Learning, 2003, pp. 856-863.
  • Y. Zhang, G. Zhou, J. Jin, X. Wang, and A. Cichocki, "Optimizing spatial patterns with sparse filter bands for motor-imagery-based brain–computer interface," J. Neurosci. Methods, vol. 255, pp. 85–91, Nov. 2015.
  • Y. Yang, I. Bloch, S. Chevallier, and J. Wiart, "Subject-Specific Channel Selection Using Time Information for Motor Imagery Brain-Computer Interfaces," Cognit. Comput., vol. 8, no. 3, pp. 505-518, Jun. 2016.
  • Y. Wu and Y. Liu, "Robust truncated hinge loss support vector machines," Journal of the American Statistical Association, vol. 102, no. 479, pp. 974-983, 2007.
  • Y. Liu, H. Zhang, M. Chen, and L. Zhang, "A boosting-based spatial spectral model for stroke patients EEG analysis in rehabilitation training," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 24, no. 1, pp. 169-179, Jan. 2016.
  • Y. Li, S. Pirk, H. Su, C.R. Qi, and L.J. Guibas,: "FPNN: Field probing neural networks for 3D data," Advances in Neural Information Processing Systems (NIPS), pp. 307-315, 2016.
  • Y. Li, C. Lin, J. Huang and W. Zhang, "A new method to construct reduced vector sets for simplifying support vector machines," in Engineering of Intelligent Systems, 2006 IEEE International Conference on, Apr. 2006, pp. 1-5.
  • X. Yong, R. K. Ward and G. E. Birch, "Generalized morphological component analysis for EEG source separation and artifact removal," in Neural Engineering, 2009. NER'09. 4th International IEEE/EMBS Conference on, Jun. 2009, pp. 343-346.
  • X. Song, V. Perera and S. Yoon, "A study of EEG features for multisubject brain-computer interface classification," in Signal Processing in Medicine and Biology Symposium (SPMB), 2014 IEEE, Jan. 2014, pp. 1-1.
  • W. Y. Hsu, "Embedded prediction in feature extraction: application to single-trial EEG discrimination," Clin EEG Neurosci., vol. 44, no. 1, pp. 31-38, Jan. 2013.
  • W. Tam, K. Tong, F. Meng, and S. Gao, "A minimal set of electrodes for motor imagery BCI to control an assistive device in chronic stroke subjects: a multi-session study," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 19, no. 6, pp. 617–627, Dec. 2011.
  • W. M. Campbell, D. E. Sturim and D. A. Reynolds, "Support vector machines using GMM supervectors for speaker verification," IEEE Signal Process. Lett., vol. 13, no. 5, pp. 308-311, Apr. 2006.
  • W. Hsu, "Embedded prediction in feature extraction: application to single-trial EEG discrimination," Clin EEG Neurosci., vol. 44, no. 1, pp. 31-38, Jan. 2013.
  • T. Kovacs and S. Shin, "Reliability of brain-computer interface language sample transcription procedures," J. Rehabil. Res. Dev., vol. 51, no. 4, pp. 579, Apr. 2014.
  • T. Kayikcioglu and O. Aydemir, "A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data," Pattern Recog. Lett., vol. 31, no. 11, pp. 1207-1215, Aug. 2010.
  • T. Al-ani, and D. Trad, Signal Processing and Classification Approaches for Brain-Computer Interface. In Intelligent and Biosensors; Somerset, V.S., Ed.; InTech: Rijeka, Croatia, 2010.
  • S. Wang, Z. Li, C. Liu, X. Zhang and H. Zhang, "Training data reduction to speed up SVM training," Appl. Intell., vol. 41, no. 2, pp. 405-420, Jan. 2014.
  • S. V. Eslahi and N. J. Dabanloo, "Fuzzy support vector machine analysis in EEG classification," International Research Journal of Applied and Basic Sciences, vol. 5, no. 2, pp. 161-165, 2013.
  • S. Siuly, Y. Li, and Y. Zhang, Cross-correlation aided logistic regression model for the identification of motor imagery EEG signals in BCI applications. EEG Signal Analysis and Classification: Techniques and Applications, Springer, 2016, pp. 153-172.
  • S. Park and S. Lee, "Small Sample Setting and Frequency Band Selection Problem Solving Using Subband Regularized Common Spatial Pattern," IEEE Sensors J., vol. 17, no. 10, pp. 2977–2983, May. 2017.
  • S. Lemm, C. Schafer and G. Curio, "BCI competition 2003-data set III: probabilistic modeling of sensorimotor mu rhythms for classification of imaginary hand movements," IEEE Trans Biomed Eng., vol. 51, no. 6, pp. 1077-1080, Jun. 2004.
  • S. Lemm, B. Blankertz, G. Curio, and K. Muller, "Spatio-spectral filters for improving the classification of single trial EEG," IEEE Trans. Biomed. Eng., vol. 52, no. 9, pp. 1541–1548, Sep. 2005.
  • S. Chen and H. Y. Zhu, "Wavelet transform for processing power quality disturbances," EURASIP Journal on Advances in Signal Processing, vol. 2007, (1), pp. 047695, Apr. 2007.
  • R. Zhang, P. Xu, T. Liu, Y. Zhang, L. Guo, P. Li, and D. Yao, "Local temporal correlation common spatial patterns for single trial EEG classification during motor imagery," Comput. Math. Methods. Med., 591216, Nov. 2013.
  • R. K. Sinha, Y. Aggarwal and B. N. Das, "Backpropagation artificial neural network classifier to detect changes in heart sound due to mitral valve regurgitation," J. Med. Syst., vol. 31, no. 3, pp. 205-209, Jun. 2007.
  • Q. Xu, H. Zhou, Y. Wang and J. Huang, "Fuzzy support vector machine for classification of EEG signals using wavelet-based features," Med. Eng. Phys., vol. 31, no. 7, pp. 858-865, Sep. 2009.
  • Q. Novi, C. Guan, T. H. Dat, and P. Xue, "Sub-band common spatial pattern (SBCSP) for brain-computer interface," In Proc. 3rd Int. Conf. Neural Eng. IEEE Eng. Med. Biol. Soc. (EMBS), May. 2007, pp. 204–207.
  • P. S. Hammon and V. R. de Sa, "Preprocessing and Meta-Classification for Brain-Computer Interfaces," in IEEE Transactions on Biomedical Engineering, vol. 54, no. 3, pp. 518-525, March 2007.
  • P. Li, P. Xu, R. Zhang, L. Guo, and D. Yao, "L1 norm based common spatial patterns decomposition for scalp EEG BCI," Biomed. Eng. Online, vol. 12, no, 1, pp. 77, Aug. 2013.
  • P. Herman, G. Prasad, T. M. McGinnity and D. Coyle, "Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification," IEEE Trans Neural Syst Rehabil Eng., vol. 16, no. 4, pp. 317-326, Jun. 2008.
  • P. Cipresso, L. Carelli, F. Solca, D. Meazzi, P. Meriggi, B. Poletti, D. Lul, A. C. Ludolph, V. Silani, and G. Riva, "The use of P300based BCIs in amyotrophic lateral sclerosis: from augmentative and alternative communication to cognitive assessment," Brain. Behav., vol. 2, no. 4, pp. 479-498, Jul. 2012.
  • Niedermeyer E, Lopes da Silva F. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincot Williams & Wilkins 2004.
  • N. Yamawaki, C. Wilke, Z. Liu and B. He, "An enhanced time-frequency-spatial approach for motor imagery classification," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 250-254, Jun. 2006.
  • N. F. Ince, A. H. Tewfik, and S. Arica, "Extraction subject-specific motor imagery time-frequency patterns for single trial EEG classification," Comput. Biol. Med., vol. 37, no. 4, pp. 499–508, Apr. 2007.
  • N. Brodu, F. Lotte and A. L cuyer, "Exploring two novel features for EEG-based brain–computer interfaces: Multifractal cumulants and predictive complexity," Neurocomputing, vol. 79, no. 1, pp. 87-94, Mar. 2012.
  • N. Brodu, F. Lotte and A. L cuyer, "Comparative study of band-power extraction techniques for motor imagery classification," in Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2011 IEEE Symposium on, Apr. 2011, pp. 1-6.
  • M. Tangermann, K. M ller, A. Aertsen, N. Birbaumer, C. Braun, C. Brunner, R. Leeb, C. Mehring, K. J. Miller. and G. Mueller-Putz, "Review of the BCI competition IV," Front. Neurosci, vol. 6, no. 55, 2012.
  • M. Murugappan, M. Rizon, R. Nagarajan, S. Yaacob, I. Zunaidi and D. Hazry, "EEG feature extraction for classifying emotions using FCM and FKM," International Journal of Computers and Communications, vol. 1, no. 2, pp. 21-25, 2007.
  • M. Krauledat, G. Dornhege, B. Blankertz, and K. Mller, "Robustifying EEG data analysis by removing outliers," Chaos and Complexity Letters, vol. 2, no, 3, pp. 259–274, 2007.
  • M. Grosse-Wentrup, C. Liefhold, K. Gramann, and M. Buss, "Beamforming in noninvasive brain–computer interfaces," IEEE Trans. Biomed. Eng., vol. 56, no. 4, pp. 1209–1219, Apr. 2009.
  • M. Besserve, J. Martinerie and L. Garnero, "Improving quantification of functional networks with eeg inverse problem: Evidence from a decoding point of view," Neuroimage, vol. 55, no. 4, pp. 1536-1547, Apr. 2011.
  • M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, "Mutual information-based optimization of sparse spatio-spectral filters in brain–computer interface," Neural Comput. Appl., vol. 25, no. 3–4, pp. 625–634, Sep. 2014.
  • M. Ahn, M. Lee, J. Choi, S. C. Jun, "A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users," Sensors, vol. 14, no. 8, pp. 14601-33, Aug. 2014.
  • L. Wang, X. Zhang, X. Zhong and Y. Zhang, "Analysis and classification of speech imagery EEG for BCI," Biomed. Signal Process. Control, vol. 8, no. 6, pp. 901-908, Nov. 2013.
  • L. Wang, S. Chen, and Y. Wang, "A unified algorithm for mixed   -minimizations and its application in feature selection," Comput. Optim. Appl., vol. 58, no. 2, pp. 409–421, Jun. 2014.
  • L. F. Nicolas-Alonso and J. Gomez-Gil, "Brain computer interfaces, a review," Sensors, vol. 12, no. 2, pp. 1211-1279, 2012.
  • K. M. Wilkinson, and W. J. McIlvane, "Perceptual factors influence visual search for meaningful symbols in individuals with intellectual disabilities and Down syndrome or autism spectrum disorders," Am. J. Intellect. Dev. Disabil., vol. 118, no. 5, pp. 353-364, Sep. 2013.
  • K. K. Ang, Z. Y. Chin, H. Zhang, and C. Guan, "Robust filter bank common spatial pattern (RFBCSP) in motor-imagery-based brain-computer interface," Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, pp. 578–581.
  • K. K. Ang, Z. Y. Chin, H. Zhang, and C. Guan, "Filter bank common spatial pattern (FBCSP) in brain-computer interface," In Proc. IEEE Int. Joint Conf. Neural Netw. (IJCNN), Jun. 2008, pp. 2390–2397.
  • K. K. Ang, Z. Y. Chin, C. Wang, C. Guan, and H. Zhang, "Filter bank common spatial pattern algorithm on BCI Competition IV Datasets 2a and 2b," Front. Neurosci, vol. 6, 2012..
  • J. S. Kirar and R. K. Agrawal, "Optimal Spatio-spectral Variable Size Subbands Filter for Motor Imagery Brain Computer Interface," Procedia Comput. Sci., vol. 84, pp. 14–21, Dec. 2016.
  • J. R. Wolpaw and D. J. McFarland, "Multichannel EEG-based brain-computer communication," Electroencephalogr. Clin. Neurophysiol., vol. 90, no. 6, pp. 444-449, Jun. 1994.
  • J. Mller-Gerking, G. Pfurtscheller, and H. Flyvbjerg, "Designing optimal spatial filters for single-trial EEG classification in a movement task," Clin. Neurophysiol., vol. 110, no. 5, pp. 787–798, May. 1999.
  • J. Katona and A. Kovari, "A Brain–Computer Interface Project Applied in Computer Engineering," IEEE Transactions on Education, vol. 59, no. 4, pp. 319-326, May. 2016.
  • J. Kalcher, D. Flotzinger, C. Neuper, S. G lly and G. Pfurtscheller, "Graz brain-computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns," Med Biol Eng Comput., vol. 34, no. 5, pp. 382-388, Sep. 1996.
  • J. J. Shih, D. J. Krusienski, and J. R. Wolpaw, "Brain-computer interfaces in medicine," Mayo Clin. Proc., vol. 87, no. 3, pp. 268-279, Mar. 2012.
  • I. Dokare and N. Kant, "Performance analysis of SVM, KNN and BPNN classifiers for motor imagery," Eng.Trends Technol., vol. 10, no. 1, pp. 19-23, Oct. 2014.
  • I. Belakhdar, W. Kaaniche, R. Djmel, and B. Ouni, "A comparison between ANN and SVM classifier for drowsiness detection based on single EEG channel," 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Monastir, Tunisia, 21-24 March 2016, pp. 443-446.
  • H. Wang and W. Zheng, "Local temporal common spatial patterns for robust single-trial EEG classification," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 16, no. 2, pp. 131–139, Apr. 2008.
  • H. Li, C. Li, C. Zhang, Z. Liu, and C. Liu, "Hyperspectral Image Classification with Spatial Filtering and ℓ 2, 1 Norm," Sensors, vol. 17, no. 2, pp. 314, Feb. 2017.
  • H. Li and O. Chutatape, "Automated feature extraction in color retinal images by a model based approach," IEEE Trans Biomed Eng., vol. 51, no. 2, pp. 246-254, Feb. 2004.
  • H. Kang, Y. Nam, and S. Choi, "Composite common spatial pattern for subject-to-subject transfer," IEEE Signal Process. Lett., vol. 16, no. 8, pp. 683 –686, Aug. 2009.
  • G. Santhanam, S. I. Ryu, M. Y. Byron, A. Afshar and K. V. Shenoy, "A high-performance brain–computer interface," Nature, vol. 442, pp. 195-198, Jul. 2006.
  • G. Pfurtscheller, C. Neuper, D. Flotzinger, and M. Pregenzer, "EEG-based discrimination between imagination of right and left hand movement," Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 6, pp. 642-651, Dec. 1997.
  • G. Pfurtscheller, "Functional brain imaging based on ERD/ERS," Vision Res., vol. 41, no. 10, pp. 1257–1260, May. 2001.
  • G. Pfurtscheller and F. L. Da Silva, "Event-related EEG/MEG synchronization and desynchronization: basic principles," Clin. Neurophysiol., vol. 110, no. 11, pp. 1842-1857, Nov. 1999.
  • G. Lan, C. Hou, and D. Yi, "Robust feature selection via simultaneous capped ℓ2-norm and ℓ2,1-norm minimization," In Proc. IEEE Int. Conf. Big Data Analysis (ICBDA), 2016, pp. 1–5, 2016.
  • G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, and K. Muller, "Combined optimization of spatial and temporal filters for improving brain-computer interfacing," IEEE Trans. Biomed. Eng., vol. 53, no. 11, pp. 2274–2281, Nov. 2006.
  • G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, and K. Mller, "Optimizing spatio-temporal filters for improving brain-computer interfacing," Adv. Neural Inf. Process. Syst., vol. 18, pp. 315-322, 2006.
  • G. Dornhege, B. Blankertz, G. Curio and K. Muller, "Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms," IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 993-1002, May. 2004.
  • G. Blanchard, and B. Blankertz, "BCI competition 2003-data set IIa: spatial patterns of self-controlled brain rhythm modulations," IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1062-1066, Jun. 2004.
  • G Pavlidis, Using Other Statistical Features. In Mixed Raster Content: Segmentation, Compression, Transmission; Springer: Singapore, 2016; p. 324.
  • F. Nie, H. Huang, X. Cai, and C. H. Ding, "Efficient and robust feature selection via joint ℓ2,1-norms minimization," Adv. Neural Inf. Process. Syst., pp. 1813–1821, 2010.
  • F. Lotte. A tutorial on EEG signal-processing techniques for mental-state recognition in Brain–Computer Interfaces. In Eduardo Reck Miranda and Julien Castet, editors, Guide to Brain-Computer Music Interfacing, pp. 133–161. Springer London, 2014.
  • F. Lotte, M. Congedo, A. L cuyer, F. Lamarche, and B. Arnaldi, "A review of classification algorithms for EEG-based brain-computer interfaces," J. Neural Eng., vol. 4, no. 2, pp. R1-R13, Jun. 2007.
  • F. Lotte and C. Guan, "Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms," IEEE Trans. Biomed. Eng., vol. 58, no. 2, pp. 355-362, Feb. 2011.
  • E. B. Goldstein, Sensation and Perception. Thompson Wadsworth 2007
  • D. Meyer, F. Leisch and K. Hornik, "The support vector machine under test," Neurocomputing, vol. 55, no. 1, pp. 169-186, Sep. 2003.
  • D. Lee and S. G. Lee, "The Study on Movement Imagery EEG Classification using GMM and SVM," Journal of KIIT, vol. 11, no. 7, pp. 67-75, Jul. 2013.
  • D. E. Thompson, S. Blain-Moraes, and J. E. Huggins, "Performance assessment in brain-computer interface-based augmentative and alternative communication," Biomed. Eng. Online, vol. 12, no. 1, pp. 43, May. 2013.
  • D. Coyle, G. Prasad and T. M. McGinnity, "A time-series prediction approach for feature extraction in a brain-computer interface," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 13, no. 4, pp. 461-467, 2005.
  • D. Chinarro, System Engineering Applied to Fuenmayor Karst Aquifer (San Juli n De Banzo, Huesca) and Collins Glacier (King George Island, Antarctica). Springer, 2014.
  • D Reynolds, Gaussian Mixture Models. In Encyclopedia of Biometrics; Springer: Boston, MA, USA, 2009; pp. 827–832.
  • C. Ren, D. Dai, and H. Yan, "ℓ2; 1-norm-based regression for classification," In: First Asian Conf. Pattern Recognit. (ACPR), IEEE, Nov. 2011, pp. 485–489.
  • C. Ren, D. Dai, and H. Yan, "Robust classification using ℓ2,1-norm-based regression model," Pattern Recognit., vol. 45, no. 7, pp. 2708–2718, Jul. 2012.
  • C. Jing and J. Hou, "SVM and PCA based fault classification approaches for complicated industrial process," Neurocomputing, vol. 167, no. 1, pp. 636-642, Nov. 2015.
  • C. Hsu, C. Chang and C. Lin, "A practical guide to support vector classification," Available online: https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf (accessed on 6 October 2017).
  • C. G. Pinheiro, E. L. Naves, P. Pino, E. Losson, A. O. Andrade, and G. Bourhis, "Alternative communication systems for people with severe motor disabilities: a survey," Biomed. Eng. Online, vol. 10, no. 1, pp. 31, Apr. 2011.
  • C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learning, vol. 20, no. 3, pp. 273-297, 1995.
  • C. Chang and C. Lin, "LIBSVM: a library for support vector machines," ACM Trans. Intell. Syst. Technol (TIST)., vol. 2, no.3, pp. 27, Apr. 2011.
  • C Valens, A really Friendly Guide to Wavelets. Available online: http://www.cs.unm.edu/~williams/cs530/arfgtw.pdf (accessed on 22 August 2017).
  • B. Peters, G. Bieker, S. M. Heckman, J. E. Huggins, C. Wolf, D. Zeitlin, and M. Fried-Oken, "Brain-computer interface users speak up: the Virtual Users Forum at the 2013 International Brain-Computer Interface meeting," Arch. Phys. Med. Rehabil., vol. 96, no. 3, pp. S33-S37, Mar. 2015.
  • B. Perseh and A. R. Sharafat, "An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection," J. Med. Signals Sens., vol. 2, no. 3, pp. 128-143, Jul. 2012.
  • B. Nasihatkon, R. Boostani, and M. Z. Jahromi, "An efficient hybrid linear and kernel CSP approach for EEG feature extraction," Neurocomputing, vol. 73, no. 1, pp. 432–437, Dec. 2009.
  • B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. Muller, "Optimizing spatial filters for robust EEG single-trial analysis," IEEE Signal Process. Mag., vol. 25, no. 1, pp. 41–56, 2008.
  • B. Blankertz, K. Muller, G. Curio, T. M. Vaughan, G. Schalk, J. R. Wolpaw, A. Schlogl, C. Neuper, G. Pfurtscheller and T. Hinterberger, "The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials," IEEE Trans Biomed Eng., vol. 51, no. 6, pp. 1044-1051, Jum. 2004.
  • B. Blankertz, K. Muller, D. J. Krusienski, G. Schalk, J. R. Wolpaw, A. Schlogl, G. Pfurtscheller, J. R. Millan, M. Schroder, and N. Birbaumer, "The BCI Competition III: Validating alternative approaches to actual BCI problems," IEEE. Trans. Neural. Syst. Rehabil. Eng., vol. 14, no. 2, pp. 153–159, Jun. 2006
  • A. Subasi, "EEG signal classification using wavelet feature extraction and a mixture of expert model," Expert Syst. Appl., vol. 32, no. 4, pp. 1084-1093, May. 2007.
  • A. Subasi, "Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction," Comput. Biol. Med., vol. 37, no. 2, pp. 227-244, Feb. 2007.
  • A. S. Sankar, S. S. Nair, V. S. Dharan and P. Sankaran, "Wavelet sub band entropy based feature extraction method for BCI," Procedia Comput. Sci., vol. 46, pp. 1476-1482, 2015.
  • A. P. Dempster, N. M. Laird and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc. Series B Stat. Methodol., vol. 39, no. 1, pp. 1-38, 1977.
  • A. Najmi and J. Sadowsky, "The continuous wavelet transform and variable resolution time-frequency analysis," Johns Hopkins APL Tech. Dig., vol. 18, no. 1, pp. 134-140, 1997.
  • A. K. Das, S. Suresh, N. Sundararajan, and K. Subramanian, "A subject-specific frequency band selection for efficient BCI-an interval type-2 fuzzy inference system approach," In: Proc. IEEE Int. Conf. Fuzzy Syst. (FUZZ-IEEE), Aug. 2015, pp. 1-8.
  • A. Hashemi, H. Arabalibiek and K. Agin, "Classification of wheeze sounds using wavelets and neural networks," Stud Health Technol Inform., vol. 173, pp. 161-165, 2012.
  • A. Bashashati, M. Fatourechi, R. K. Ward and G. E. Birch, "A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals," J. Neural Eng., vol. 4, no. 2, pp. R35–57, 2007.
  • A. Argyriou, T. Evgeniou and M. Pontil, "Multi-task feature learning," Adv. Neural Inf. Process. Syst., pp. 41–48, 2007.