[1] H. Do, P. T. Wang, C. E. King, A. Abiri and Z. Nenadic, "Brain-Computer Interface Controlled Functional Electrical Stimulation System for Ankle Movement," NeuroEngineering and Rehabilitation, vol. 8, no. 49, 2011.
[2] H. Do, P. T. Wang, C. E. King, S. N. Chun and a. Z. Nenadic, "Brain-computer interface controlled robotic gait orthosis," NeuroEngineering and Rehabilitation, vol. 10, no. 111, 2013.
[3] G. Pfurtscheller, B. Graimann and B. Allison, Brain-computer interfaces : revolutionizing human-computer interaction, Springer-Verlag, 2010.
[4] S. Gao, Y. Wang, X. Gao and B. Hong, "Visual and Auditory Brain–Computer Interfaces," Biomedical engineering, IEEE Transactions on, vol. 61(5), pp. 1436-1447, 2014.
[5] J. Wolpawa, N. Birbaumer, D. McFarlanda, G. Pfurtscheller and T. Vaughan, "Brain–computer interfaces for communication and control," Clinical Neurophysiology, vol. 113, pp. 767-791, 2002.
[6] J. R. Wolpaw and a. D. J. McFarland, "Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans," Proceedings of the National Academy of Sciences, vol. 101, no. 51, p. 17849–17854, 2004.
[7] D. McFarland, A. S. R Wolpaw and J. William, "Electroencephalographic (EEG) control of three-dimensional movement," NEURAL ENGINEERING, vol. 7, 2010.
[8] 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, no. 5, pp. 1131-1144, 2012.
[9] J. d. Millán, F. Renkens, J. Mouriño and W. Gerstner, "Noninvasive Brain-Actuated Control of a Mobile Robot by Human EEG," Biomedical engineering, IEEE Transactions on, vol. 51, pp. 1026-1033, 2004.
[10] G. Pfurtscheller, R. Lee, C.Keinrath, D. Friedman, C. Neuper, C. Guger and M. Slater, "Walking from thought," Brain Research, vol. 1071, p. 145 – 152, 2006.
[11] 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 wheel chair," Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 20, pp. 720-729, 2012.
[12] J. Li, J. Liang, Q. Zhao, J. Li, K. Hong and L. Zhang, "Design of assistive wheelchair system directly steered by human thoughts," International Journal of Neural Systems, vol. 23, 2013.
[13] G. Pfurtscheller, C. Brunner, A. Schlogl and F. da Silva, "Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks," NeuroImage, vol. 31, p. 153–159, 2006.
[14] Y. Li, X. Gao, H. Liu and S. Gao, "Classification of Single-Trial Electroencephalogram During Finger Movement," Biomedical engineering, IEEE Transactions on, vol. 51, 2004.
[15] S. Lemm, K.-R. M. ller and G. Curio, "A Generalized Framework for Quantifying the Dynamics of EEG Event-Related Desynchronization," PLoS Computational Biology, vol. 5, 2009.
[16] J. McFarland, R. Wolpaw and a. D. J., "Multichannel EEG-based brain-computer communication," Electroencephalography and Clinical Neurophysiology, vol. 90, p. 444–449, 1994.
[17] M.Pregenzer, G.Pfurtscheller, J.Kalcher, Ch.Neuper, G.Pfurtscheller, J.Kalcher and C. D.Flotzinger, "On-line EEG classification during externally-paced hand movements using a neural network-based classifier," Neurophysioligy, pp. 416-425, 1996.
[18] A. Schlogl, F. Lee, H. Bischof and G. Pfurtscheller, "Characterization of four-class motor imagery EEG data for the BCI-competition 2005," Journal of Neural Engineering, pp. 14-22, 2005.
[19] S. ,. R. C. ,. R. S. C. Vidaurre and G. Pfurtscheller, "Adaptive On-line Classification for EEG-based Brain Computer Interfaces with AAR parameters and band power estimates," Biomed. Technik, vol. 50, pp. 350-354, 2005.
[20] Vidaurre, N. Krämer, B. Blankertz and A. Schlögl, "Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces," Neural Networks, vol. 22, pp. 1313-1319, 2009.
[21] Z. Allison, C. Brunner, V. Kaiser, G. R. M. uller-Putz and C. Neuper, "Toward a hybrid brain–computer interface based on imagined movement and visual attention," Neural Engineering, vol. 7, 2010.
[22] W.-Y. HSU, "Single-trial motor imagery classification using asymmetry ratio, phase relation, wavelet-based fractal, and their selected combination.," Int J Neural Syst, vol. 23, 2013.
[23] J. Jiang, Z. Zhou, E. Yin, Y. Yu, Y. Liu and D. Hu, "A novel Morsecode-inspired method for multi class motor imagery brain–computer interface(BCI)design," Computers in Biology and Medicine, vol. 66, pp. 11-19, 2015.
[24] H. Z. Y. W. Qi Xu and J. Huang, "Fuzzy support vector machine for classification of EEG signals using wavelet-based features," Medical Engineering & Physics, vol. 31, pp. 858-865, 2009.
[25] Y. F. Minyou Chena and X. Zheng, "Phase space reconstruction for improving the classification of singlesingle trial EEG," Biomedical Signal Processing and Control, vol. 11, pp. 10-16, 2014.
[26] M. C. Yonghui Fang and X. Zheng, "Extracting features from phase space of EEG signals in brain-computer interfaces," Neurocomputing, 2015.
[27] H. M. Z. S. Mobarakeh, "Improvement of EEG-based motor imagery classification using ringtopology-based particle swarm optimization," Biomedical Signal Processing and Control, vol. 32, pp. 69-75, 2017.
[28] N. M. Hamidreza Abbaspour and S. M. Razavi, "An Effective Brain-Computer Interface System Based on the Optimal Timeframe Selection of Brain Signals," Int Clin Neurosci, vol. 5, pp. 35-42, 2018.
[29] W.-K. Tam, K.-y. Tong, F. Meng and a. S. Gao, "A Minimal Set of Electrodes for Motor Imagery BCI to Control an Assistive Device in Chronic Stroke Subjects: A Multi-Session Study," Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 19, no. 6, pp. 617-627, 2011.
[30] [Online]. Available: http://www.bbci.de/competition/iii/#data_set_iiib.
[31] N. Brodu, F. Lotte and A. Lécuyer, "Comparative Study of Band-Power Extraction Techniques for Motor Imagery Classification," in IEEE Symposium on Computational Intelligence,Cognitive Algorithms, Mind, and Brain,pp.1-6, Paris, France, 2011.
[32] XinMa, "The research of brain-computer interface based on AAR parameters and neural networks classifier," in International Conference on Computer Science and Network Technology, 2011.
[33] Y. &. G. X. &. H. b. &. G. S. Wang, " Practical Designs of Brain–Computer Interfaces Based on the Modulation of EEG Rhythms. Brain-Computer Interfaces," in The Frontiers Collection, Berlin Heid, Springer-Verlag, 2010, pp. 137-154.
[34] J. Yue, Z. Zhou, J. Jiang, Y. Liu and D. Hu, "Balancing a simulated inverted pendulum through motor imagery: An EEG-based real-time control paradigm," Neuroscience Letters, vol. 524, p. 95– 100, 2012.
[35] A. Schlogl, J. Kronegg, J. E. Huggins and S. G. Mason, "Evaluation Criteria for BCI Research," in Toward Brain-computer Interfacing, London, England, The MIT Press, 2007, pp. 327-342.
[36] J. Cohen, "A coefficient of agreement for nominal scales," Educational and Psychological Measurement, vol. 20, no. 1, pp. 37-46, 1960.
[37] Monge-Pereira, J. Ibañez-Pereda, I. Alguacil-Diego, J. Serrano, M. Spottorno-Rubio and F. Molina-Rueda, "Use of Electroencephalography Brain Computer Interface systems as a rehabilitative approach for upper limb function after a stroke. A systematic review," PM&R, vol. 9, no. 9, pp. 918-932, 2017.
[38] Iturrate, J. M Antelis, A. Kubler and J. Minguez, "A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation," Robatics, IEEE Transactions on, vol. 25, pp. 614-627, 2009.
[39] Lehtonen, P. Jylänki, L. Kauhanen and M. Sams, "Online Classification of Single EEG Trials During Finger Movements," Biomedical engineering, IEEE Transactions on, vol. 55, pp. 713-720, 2008.
[40] G. Onose, C. Grozea, A. Anghelescu, C. Daia, C. Sinescu, A. Ciurea, T. Spircu and A. Mirea, "On the feasibility of using motor imagery EEG-based brain–computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trialfollow-up," Spinal Cord, vol. 50, pp. 599-608, 2012.
[41] T. M. Mitchell, Machine learning, Boston: McGraw-Hill, 1997.