[1] kelly SP, Lalor EC, Reilly RB, Foxe JJ. Visual spatial attention tracking using high-density SSVEP data independent brain-computer communication. IEEE Trans. Neural Syst. Rehab Eng. 2005; 13 (2): 172-178.
[2] Li Y, Bin G, Gao X, Hong B, Gao S. Analysis of phase coding SSVEP based on canonical correlation analysis (CCA). In: Int. IEEE/EMBS Conf. on Neural Engineering; Cancun, Mexico; 2011. pp. 368-371
[3] Allison BZ, Wolpaw EW, Wolpaw JR. Brain-computer interface systems: progress and prospects. Expert Rev Med Devices 2007; 4 (4): 463-474.
[4] Friman O, Volosyak I, Gräser A. Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces, IEEE Trans. Neural Syst. Rehab Eng. 2007; 54 (4): 742-750.
[5] Hwang HJ, Hwan Kim D, Han CH, Im CH. A new dual frequency stimulation method to increase the number of visual stimuli for multiclass SSVEP based Brain computer interface (BCI). Brain Res. 2013; 1515: 66-77.
[6] Ting W, Guo-zheng Y, Bang-hua Y. Hong S. EEG feature extraction based on wavelet packet decomposition for brain computer interface. Measurement 2008; 41: 618–625.
[7] Ahirwal MK, Kumar A, Singh GK. Suri JS. Sub-band classification of decomposed single event-related potential co-variants for multi-class brain–computer interface: a qualitative and quantitative approach. IET Science, Measurement & Technology 2016; 10 (4): 355-363.
[8] Varsta M, Heikkonen J, Millan JDR. Evaluating the performance of three feature sets for brain–computer interfaces with an early stopping MLP committee. In: Int. Conf. on Pattern Recognition; Barcelona, Spain; 2000. pp. 907-910
[9] Polak M, Kostov A. Feature Extraction in development of brain-computer interface: a case study. In: Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society; Hong Kong; 1998. pp. 2058-2064.
[10] Mensh BD, Werfel J, Seung HS. BCI competition 2003–data set ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals. IEEE Trans. Biomed. Eng. 2004; 51 (6): 1052–1056.
[11] Einalou Z, Mehran YZ, Maghuly K. Diagnosis and classification of digestive diseases by wavelet transform. InFirst Joint Congress on Fuzzy and Intelligent Systems (ISFS2007) 2007.
[12] Einalou Z, Maghooli K. Fuzzy neural network approach for noninvasive diagnosis of digestive diseases using wavelet comparing to classification followed by fuzzy C-mean algorithm. In2010 17th Iranian Conference of Biomedical Engineering (ICBME) 2010 Nov 3 (pp. 1-4). IEEE.
[13] Dadgostar M, Setarehdan SK, Akin A. Detection of motion artifacts in fNIRS via the continuous wavelet transform. In2013 20th Iranian Conference on Biomedical Engineering (ICBME) 2013 Dec 18 (pp. 243-246). IEEE.
[14] R. K. Singla, A. Khoslaet al., “Influence of stimuli colour on feature classification for BCI applications, ”Int. J. Biomed. Eng. Technol., vol. 15, no. 1, pp. 82–93, 2014
[15] Amin HU, Malik AS, Ahmad RF, Badruddin N, Kamel N, Hussain M, Chooi WT. Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques, Australasian Physical and Engineering Sciences in Medicine 2015; 38 (1): 139-149.
[16] Xue JZ, Zhang H, Zheng CX. Wavelet packet transform for feature extraction of EEG during mental tasks. In: Proceedings of the Second Int. Conf. on Machine Learning and Cybernetics; Xi’an, China; 2003.
[17] Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back propagating errors. Nature 1986. 323: 533-536.
[18] Cortes C, Vapnik V. Support vector networks. Machine Learning 1995, 20: 273–297.
[19] Mirmohammadi P, Rasooli A, Ashtiyani M, Amin MM, Deevband MR. Automatic recognition of acute lymphoblastic leukemia using multi-SVM classifier. Current Science. 2018 Oct 25;115(8):1512
[20] Ashtiyani M, Lavasani SN, Alvar AA, Deevband MR. Heart rate variability classification using support vector machine and genetic algorithm. Journal of Biomedical Physics & Engineering. 2018 Dec;8(4):423.
[21] Dadgostar M, Setarehdan SK, Shahzadi S, Akin A. Classification of schizophrenia using SVM via fNIRS. Biomedical Engineering: Applications, Basis and Communications. 2018 Apr 9;30(02):1850008.
[22] Einalou Z, Maghooli K, Setarehdan SK, Akin A. Effective channels in classification and functional connectivity pattern of prefrontal cortex by functional near infrared spectroscopy signals. Optik. 2016 Mar 1;127(6):3271-5.
[23] Witten IH, Frank E. Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Burlington, 2005.
[24] Liang N-Y, Saratchandran P, Huang G-B, Sundararajan N. Classification of mental tasks from EEG signals using extreme learning machine. Int. J. Neural Systems 2006; 16 (1): 29–38.
[25] Li ZW, Shen MF. Classification of mental task EEG signals using wavelet packet entropy and SVM. In: 8th Int. Conf. on Electronic Measurement and Instruments; Xian, China; 2007. pp. 906–909.
[26] Yazdani A, Ebrahimi T, Hoffmann U. Classification of EEG signals using Dempster Shafer theory and a k-nearest neighbor classifier. In: 4th Int. IEEE EMBS conf. on neural engineering; Antalya, Turkey; 2009. pp. 327–330.
[27] Guo L, Wu Y, Zhao L, Cao T, Yan W, Shen X. Classification of mental task from EEG signals using immune feature weighted support vector machines. IEEE Transactions on Magnetics 2011, 47 (5): 866–869.
[28] Heidari H, Einalou Z. SSVEP Extraction Applying Wavelet Transform and Decision Tree with Bays Classification. International Clinical Neuroscience Journal 2017, 4 (3): 91-97.