D. Hammond, Neurofeedback with anxiety and affective disorders, Child Adolesc. Psychiatr. Clin. N. Am, vol.14, pp.105-123, 2005.

I. Daly, F. Pichiorri, J. Faller, V. Kaiser, A. Kreilinger et al., What does clean EEG look like?, IEEE Eng. Med. Biol. Soc, pp.3963-3966, 2012.

A. M. Tautan, V. Mihajlovic, Y. H. Chen, B. Grundlehner, J. Penders et al., Signal Quality in Dry Electrode EEG and the Relation to Skin-electrode Contact Impedance Magnitude, Proceedings of the International Conference on Biomedical Electronics and Devices, vol.1, pp.12-22, 2014.

E. Niedermeyer, The normal EEG of the waking adult, Electroencephalography. Basic Principles, Clinical Applications, and Related Fields, pp.131-152, 1993.

B. S. Chang, D. L. Schomer, and E. Niedermeyer, Normal EEG and Sleep: Adults and Elderly, Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, pp.183-214, 2011.

J. D. Bronzino, Principles of electroencephalography, The Biomedical Engineering Handbook, vol.1, 1995.

A. Delorme, S. Makeig, and T. Sejnowski, Automatic artifact rejection for EEG data using high-order statistics and independent component analysis, Proceedings of the 3rd International Workshop on ICA, vol.457, p.462, 2001.

M. Nakamura, Q. Chen, T. Sugi, A. Ikeda, and H. Shibasaki, Technical quality evaluation of EEG recording based on electroencephalographers' knowledge, Med. Eng. Phys, vol.27, pp.93-100, 2005.

D. Brunner, R. Vasko, C. Detka, J. Monahan, C. R. Iii et al., Muscle artifacts in the sleep EEG: Automated detection and effect on all-night EEG power spectra, J. Sleep Res, vol.5, pp.155-164, 1996.

B. Hu, H. Peng, Q. Zhao, B. Hu, D. Majoe et al., Signal Quality Assessment Model for Wearable EEG Sensor on Prediction of Mental Stress, IEEE Trans. NanoBiosci, vol.14, pp.553-561, 2015.

A. Temko, C. Nadeu, W. Marnane, G. B. Boylan, and G. Lightbody, EEG Signal Description with Spectral-Envelope-Based Speech Recognition Features for Detection of Neonatal Seizures, IEEE Trans. Inf. Technol. Biomed, vol.15, pp.839-847, 2011.

N. A. Chadwick, D. A. Mcmeekin, and T. Tan, Classifying eye and head movement artifacts in EEG signals, Proceedings of the 5th IEEE International Conference on Digital Ecosystems and Technologies (DEST), pp.285-291, 2011.

R. Singla, B. Chambayil, A. Khosla, and J. Santosh, Comparison of SVM and ANN for classification of eye events in EEG, J. Biomed. Sci. Eng, 2011.

E. Nedelcu, R. Portase, R. Tolas, R. Muresan, M. Dinsoreanu et al., Artifact detection in EEG using machine learning, Proceedings of the 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp.77-83, 2017.

S. O'regan and W. Marnane, Multimodal detection of head-movement artefacts in EEG, J. Neuroscience. Methods, vol.218, pp.110-120, 2013.

H. Yang, C. Guan, K. K. Ang, K. S. Phua, and C. Wang, Quality assessment of EEG signals based on statistics of signal fluctuations, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp.1853-1857, 2014.

S. D. Muthukumaraswamy, High-frequency brain activity and muscle artifacts in MEG/EEG: A review and recommendations, Front. Hum. Neurosci, vol.7, 2013.

B. H. Kim and S. Jo, Real-time motion artifact detection and removal for ambulatory BCI, Proceedings of the 3rd International Winter Conference on Brain-Computer Interface, pp.1-4, 2015.

Z. Tiganj, M. Mboup, C. Pouzat, and L. Belkoura, An Algebraic Method for Eye Blink Artifacts Detection in Single Channel EEG Recordings, Proceedings of the 17th International Conference on Biomagnetism Advances in Biomagnetism-Biomag, pp.175-178, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00518627

C. A. Majmudar, R. Mahajan, and B. I. Morshed, Real-time hybrid ocular artifact detection and removal for single channel EEG, Proceedings of the IEEE International Conference on Electro/Information Technology (EIT), pp.330-334, 2015.

M. Anastasiadou, A. Hadjipapas, M. Christodoulakis, E. S. Papathanasiou, S. S. Papacostas et al., Detection and Removal of Muscle Artifacts from Scalp EEG Recordings in Patients with Epilepsy, Proceedings of the IEEE International Conference on Bioinformatics and Bioengineering, pp.291-296, 2014.

R. Scherer, J. Faller, E. V. Friedrich, E. Opisso, U. Costa et al., Individually adapted imagery improves brain-computer interface performance in end-users with disability, PLoS ONE, vol.10, 2015.

C. Sinderby, L. Lindstrom, and A. E. Grassino, Automatic assessment of electromyogram quality, J. Appl. Physiol, vol.79, pp.1803-1815, 1995.

G. D. Fraser, A. D. Chan, J. R. Green, and D. T. Macisaac, Automated Biosignal Quality Analysis for Electromyography Using a One-Class Support Vector Machine, IEEE Trans. Instrum. Meas, vol.63, pp.2919-2930, 2014.

B. Greene, S. Faul, W. Marnane, G. Lightbody, I. Korotchikova et al., A comparison of quantitative EEG features for neonatal seizure detection, Clin. Neurophysiol, vol.119, pp.1248-1261, 2008.

A. Moura, S. Lopez, I. Obeid, and J. Picone, A comparison of feature extraction methods for EEG signals, Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp.1-2, 2015.

B. Scholkopft and K. R. Mullert, Fisher discriminant analysis with kernels, Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468), 1999.

C. Fraley and A. E. Raftery, Model-based clustering, discriminant analysis, and density estimation, J. Am. Stat. Assoc, vol.97, pp.611-631, 2002.

K. A. Aboalayon, W. S. Almuhammadi, and M. Faezipour, A comparison of different machine learning algorithms using single channel EEG signal for classifying human sleep stages, Proceedings of the Long Island Systems, Applications and Technology Conference (LISAT), pp.1-6, 2015.

C. W. Hsu and C. J. Lin, A comparison of methods for multiclass support vector machines, IEEE Trans. Neural Netw, vol.13, pp.415-425, 2002.

K. Q. Weinberger, J. Blitzer, and L. K. Saul, Distance metric learning for large margin nearest neighbor classification, J. Mach. Learn. Res, vol.10, pp.207-244, 2009.

N. Bhatia, Survey of nearest neighbor techniques, Int. J. Comput. Sci. Inf. Secur. (IJCSIS), vol.8, pp.302-305, 2010.

A. Gray and J. Markel, Distance measures for speech processing, IEEE Trans. Acoust. Speech Signal Process, vol.24, pp.380-391, 1976.

F. Itakura, Minimum prediction residual principle applied to speech recognition, IEEE Trans. Acoust. Speech Signal Process, vol.23, pp.67-72, 1975.

M. Chavez, F. Grosselin, A. Bussalb, F. D. Fallani, and X. Navarro-sune, Surrogate-based artifact removal from single-channel EEG, IEEE Trans. Neural Syst. Rehabil. Eng, vol.26, pp.540-550, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01757330

T. Fawcett and . Graphs, Notes and Practical Considerations for Researchers, Pattern Recognit. Lett, vol.31, pp.1-38, 2004.

A. M. Desodt-lebrun, Conception d'Un SystèMe de DéTection des Artefacts Dans un Signal éLectroencé Phalographique, 1986.

A. Kondacs and M. Szabó, Long-term intra-individual variability of the background EEG in normals, Clin. Neurophysiol, vol.110, pp.1708-1716, 1999.

S. Cateni, M. Vannucci, M. Vannocci, and V. Coll, Variable Selection and Feature Extraction Through Artificial Intelligence Techniques, Multivariate Analysis in Management, Engineering and the Sciences

L. Freitas, . Ed, and . Intech, , 2013.

R. E. Bellman, Adaptive Control Processes: A Guided Tour, 2015.

L. Yu and H. Liu, Feature selection for high-dimensional data: A fast correlation-based filter solution, Proceedings of the 20th International Conference on Machine Learning (ICML), vol.3, pp.856-863, 2003.

W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, 1988.

J. M. Ehrenfeld and M. Cannesson, Monitoring Technologies in Acute Care Environments-A Comprehensive Guide to Patient Monitoring Technology, Licensee MDPI, 2014.