T. Alotaiby, F. E. A. El-Samie, S. A. Alshebeili, and I. Ahmad, "A review of channel selection algorithms for EEG signal processing," EURASIP Journal on Advances in Signal Processing, vol. 2015, no. 1, p. 66, 2015.
 Z. Zhang and K. K. Parhi, "Seizure prediction using long-term fragmented intracranial canine and human EEG recordings," in Signals, Systems and Computers, 2016 50th Asilomar Conference on, 2016, pp. 361-365: IEEE.
 K. Gadhoumi, J.-M. Lina, F. Mormann, and J. Gotman, "Seizure prediction for therapeutic devices: A review," Journal of neuroscience methods, vol. 260, pp. 270-282, 2016.
 Harvard Health Publications, Harvard Medical School, 2014. Seizure overview. http://www.health.harvard.edu/mind-and-mood/seizure-overview.
 A. Theodorakopoulou, "Machine learning data preparation for epileptic seizures prediction," 2017.
 Y. Park, L. Luo, K. K. Parhi, and T. Netoff, "Seizure prediction with spectral power of EEG using cost‐sensitive support vector machines," Epilepsia, vol. 52, no. 10, pp. 1761-1770, 2011.
 A. T. Tzallas, M. G. Tsipouras, and D. I. J. I. t. o. i. t. i. b. Fotiadis, "Epileptic seizure detection in EEGs using time-frequency analysis," vol. 13, no. 5, pp. 703-710, 2009.
 H. Adeli, S. Ghosh-Dastidar, and N. J. I. T. o. B. E. Dadmehr, "A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy," vol. 54, no. 2, pp. 205-211, 2007.
 R.J. Oweis, E. W. J. B. e. o. Abdulhay, "Seizure classification in EEG signals utilizing Hilbert-Huang transform," vol. 10, no. 1, p. 38, 2011.
V. Bajaj and R. B. J. I. T. o. I. T. i. B. Pachori, "Classification of seizure and nonseizure EEG signals using empirical mode decomposition," vol. 16, no. 6, pp. 1135-1142, 2012.
S. S. Alam, M. I. H. J. I. j. o. b. Bhuiyan, and h. informatics, "Detection of seizure and epilepsy using higher order statistics in the EMD domain," vol. 17, no. 2, pp. 312-318, 2013.
M. Peker, B. Sen, D. J. I. j. o. b. Delen, and h. informatics, "A novel method for automated diagnosis of epilepsy using complex-valued classifiers," vol. 20, no. 1, pp. 108-118, 2016.
G. Wang et al., "Epileptic seizure detection based on partial directed coherence analysis," vol. 20, no. 3, pp. 873-879, 2016.
K. Samiee, P. Kovacs, M.J.I.t.o.B.E. Gabbouj, "Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform," vol. 62, no. 2, pp. 541-552, 2015.
A. B. Das, M. I. H. Bhuiyan, S. S. J. S. Alam, Image, and V. Processing, "Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection," vol. 10, no. 2, pp. 259-266, 2016.
I. Guler E.D.J.I.T.o.I. T. i. B. Ubeyli, "Multiclass support vector machines for EEG-signals classification," vol. 11, no. 2, pp. 117-126, 2007.
L. Guo, D. Rivero, J. Dorado, J. R. Rabunal, and A. J. J. o. n. m. Pazos, "Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks," vol. 191, no. 1, pp. 101-109, 2010.
P. Swami, T. K. Gandhi, B. K. Panigrahi, M. Tripathi, and S. J. E. S. w. A. Anand, "A novel robust diagnostic model to detect seizures in electroencephalography," vol. 56, pp. 116-130, 2016.
A. R. Hassan, S. Siuly, Y. J. C. m. Zhang, and p. i. biomedicine, "Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating," vol. 137, pp. 247-259, 2016.
M. Sharma, R. B. Pachori, and U. R. Acharya, "A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension," Pattern Recognition Letters, vol. 94, pp. 172-179, 2017.
X. Pang, "Seizure forecasting," Stanford University, Autumn 2014.
N. D. Truong, A. D. Nguyen, L. Kuhlmann, M. R. Bonyadi, J. Yang, and O. Kavehei, "A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis," arXiv preprint arXiv:1707.01976, 2017.
Y. Park, L. Luo, K. K. Parhi, and T. Netoff, "Seizure prediction with spectral power of EEG using cost‐sensitive support vector machines," Epilepsia, vol. 52, no. 10, pp. 1761-1770, 2011.
U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, and H. Adeli, "Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals," Computers in biology and medicine, vol. 100, pp. 270-278, 2018.
T. F. Bastos-Filho, A. Ferreira, A. C. Atencio, S. Arjunan, and D. Kumar, "Evaluation of feature extraction techniques in emotional state recognition," in Intelligent human computer interaction (IHCI), 2012 4th international conference on, 2012, pp. 1-6: IEEE.
S. Mallat and Z. Zhang, "Matching pursuit with time-frequency dictionaries," Courant Institute of Mathematical Sciences New York United States1993.
Naderahmadian, Yashar, Soosan Beheshti, and Mohammad Ali Tinati. “Correlation based online dictionary learning algorithm.” IEEE Transactions on signal processing 64.3 (2016): 592-602.
Z. Mousavi, T. Yousefi Rezaii, S. Sheykhivand, A. Farzamnia, and S. N. Razavi. "Deep convolutional neural network for classification of sleep stages from single-channel EEG signals." Journal of neuroscience methods 324 (2019): 108312.