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Brain Computer Interfaces for Silent Speech

Published online by Cambridge University Press:  22 December 2016

Yousef Rezaei Tabar  and
Ugur Halici
Yousef Rezaei Tabar
Affiliation:
Biomedical Engineering, Middle East Technical University, Ankara, Turkey. E-mail: [email protected]
Ugur Halici
Affiliation:
Biomedical Engineering, Neuroscience and Neurotechnology, Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey. E-mail: [email protected]
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Abstract

Brain Computer Interface (BCI) systems provide control of external devices by using only brain activity. In recent years, there has been a great interest in developing BCI systems for different applications. These systems are capable of solving daily life problems for both healthy and disabled people. One of the most important applications of BCI is to provide communication for disabled people that are totally paralysed. In this paper, different parts of a BCI system and different methods used in each part are reviewed. Neuroimaging devices, with an emphasis on EEG (electroencephalography), are presented and brain activities as well as signal processing methods used in EEG-based BCIs are explained in detail. Current methods and paradigms in BCI based speech communication are considered.

Type
In Honour of Erol Gelenbe
Information
European Review , Volume 25 , Issue 2 , May 2017 , pp. 208 - 230
Copyright
© Academia Europaea 2016 

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References

1. Graimann, B., Allison, B. and Pfurtscheller, G. (2010) Brain–computer interfaces: a gentle introduction. Brain-Computer Interfaces (Berlin Heidelberg: Springer), pp. 1–27.Google Scholar
2. Sellers, E.W., Vaughan, T.M. and Wolpaw, J.R. (2010) A brain-computer interface for long-term independent home use. Amyotrophic Lateral Sclerosis, 11(5), pp. 449–455.CrossRefGoogle ScholarPubMed
3. Rebsamen, B., Guan, C., Zhang, H., Wang, C., Teo, C., Ang, M.H. Jr and Burdet, E. (2010) A brain controlled wheelchair to navigate in familiar environments. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(6), pp. 590–598.CrossRefGoogle ScholarPubMed
4. Krepki, R., Blankertz, B., Curio, G. and Muller, K.-R. (2007) The Berlin Brain-Computer Interface (BBCI) towards a new communication channel for online control in gaming applications. Multimedia Tools and Applications, 33, pp. 73–90.CrossRefGoogle Scholar
5. Farwell, L.A. and Donchin, E. (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70(6), pp. 510–523.CrossRefGoogle Scholar
6. Townsend, G., LaPallo, B.K., Boulay, C.B., Krusienski, D.J., Frye, G.E., Hauser, C.K., Schwartz, N.E., Vaughan, T.M., Wolpaw, J.R. and Sellers, E.W. (2010) A novel P300-based brain-computer interface stimulus presentation paradigm: Moving beyond rows and columns. Clinical Neurophysiology, 121, pp. 1109–1120.CrossRefGoogle ScholarPubMed
7. Ahi, S.T., Kambara, H. and Koike, Y. (2011) A dictionary-driven P300 speller with a modified interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19, pp. 6–14.CrossRefGoogle ScholarPubMed
8. Takano, K., Komatsu, T., Hata, N., Nakajima, Y. and Kansaku, K. (2009) Visual stimuli for the P300 brain–computer interface: a comparison of white/gray and green/blue flicker matrices. Clinical Neurophysiology, 120, pp. 1562–1566.CrossRefGoogle ScholarPubMed
9. Li, Y., Nam, C.S., Shadden, B.B. and Johnson, S.L. (2011) A P300-based brain–computer interface: effects of interface type and screen size. International Journal of Human–Computer Interactface, 27(1), pp. 52–68.CrossRefGoogle Scholar
10. Cheng, M., Gao, X., Gao, S. and Xu, D. (2002) Design and implementation of a brain-computer interface with high transfer rates. IEEE Transactions on Biomedical Engineering, 49(10), pp. 1181–1186.CrossRefGoogle ScholarPubMed
11. Trejo, L.J., Rosipal, R. and Matthews, B. (2006) Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), pp. 225–229.CrossRefGoogle ScholarPubMed
12. Allison, B.Z., McFarland, D.J., Schalk, G., Zheng, S.D., Jackson, M.M. and Wolpaw, J.R. (2008) Towards an independent brain - computer interface using steady state visual evoked potentials. Clinical Neurophysiology :Official Journal of the International Federation of Clinical Neurophysiology, 119(2), pp. 399–408.CrossRefGoogle ScholarPubMed
13. Segers, H., Combaz, A., Manyakov, N.V., Chumerin, N., Vanderperren, K., Van Huffel, S. and Van Hulle, M.M. (2011) Steady State Visual Evoked Potential (SSVEP)-based brain spelling system with synchronous and asynchronous typing modes, In 15th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC 2011), Aalborg, Denmark, 14–17, pp. 164–167.Google Scholar
14. Obermaier, B., Muller, G.R. and Pfurtscheller, G. (2003) Virtual keyboard controlled by spontaneous EEG activity. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11, pp. 422–426.CrossRefGoogle ScholarPubMed
15. Scherer, R., Müller, G.R., Neuper, C., Graimann, B. and Pfurtscheller, G. (2004) An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Transactions on Biomedical Engineering, 51(6), pp. 979–984.CrossRefGoogle ScholarPubMed
16. Blankertz, B., Müller, K.R., Krusienski, D.J., Schalk, G., Wolpaw, J.R., Schlögl, A., Pfurtscheller, G., Millan, J.R., Schröder, M. and Birbaumer, N. (2006) The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), pp. 153–159.CrossRefGoogle ScholarPubMed
17. Nicolas-Alonso, L.F. and Gomez-Gil, J. (2012) Brain computer interfaces, a review. Sensors, 12(2), pp. 1211–1279.CrossRefGoogle ScholarPubMed
18. Suner, S., Fellows, M.R., Vargas-Irwin, C., Nakata, G.K. and Donoghue, J.P. (2005) Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13, pp. 524–541.CrossRefGoogle ScholarPubMed
19. Freeman, W.J., Holmes, M.D., Burke, B.C. and Vanhatalo, S. (2003) Spatial spectra of scalp EEG and EMG from awake humans. Clinical Neurophysiology, 114, pp. 1053–1068.CrossRefGoogle ScholarPubMed
20. Levine, S.P., Huggins, J.E., BeMent, S.L., Kushwaha, R.K., Schuh, L.A., Passaro, E.A., Rohde, M.M. and Ross, D.A. (1999) Identification of electrocorticogram patterns as the basis for a direct brain interface. Journal of Clinical Neurophysiology, 16, pp. 439–447.CrossRefGoogle ScholarPubMed
21. Kennedy, P.R., Kirby, M.T., Moore, M.M., King, B. and Mallory, A. (2004) Computer control using human intracortical local field potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12, pp. 339–344.CrossRefGoogle ScholarPubMed
22. Wolpaw, J.R., Loeb, G.E., Allison, B.Z., Donchin, E., do Nascimento, O.F., Heetderks, W.J., Nijboer, F., Shain, W.G. and Turner, J.N., BCI Meeting (2005) workshop on signals and recording methods. IEEE Transactions on Neural Systems and Rehabilitation and Engineering, 14, pp. 138–141.CrossRefGoogle Scholar
23. Bauernfeind, G., Leeb, R., Wriessnegger, S.C. and Pfurtscheller, G. (2008) Development, set-up and first results for a one-channel near-infrared spectroscopy system. Biomedizinische Technik, 53, pp. 36–43.CrossRefGoogle ScholarPubMed
24. Ward, B.D. and Mazaheri, Y. (2008) Information transfer rate in fMRI experiments measured using mutual information theory. Journal of Neuroscience Methods, 167, pp. 22–30.CrossRefGoogle ScholarPubMed
25. Coyle, S.M., Ward, T.E. and Markham, C.M. (2007) Brain-computer interface using a simplified functional near-infrared spectroscopy system. Journal of Neural Engineering, 4(3), p. 219.CrossRefGoogle Scholar
26. Power, S.D., Kushki, A. and Chau, T. (2011) Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state. Journal of Neural Engineering, 8, 066004.CrossRefGoogle ScholarPubMed
27. Lal, T.N., Schröder, M., Hill, N.J., Preissl, H., Hinterberger, T., Mellinger, J., Bogdan, M., Rosenstiel, W., Hofmann, T., Birbaumer, N. and Schölkopf, B. (2005) A Brain Computer Interface with Online Feedback Based on Magnetoencephalography. In Proceedings of the 22nd International Conference on Machine Learning (ICML’ 05), Bonn, Germany, pp. 7–11, 465–472.Google Scholar
28. Mellinger, J., Schalk, G., Braun, C., Preissl, H., Rosenstiel, W., Birbaumer, N. and Kübler, A. (2007) An MEG-based brain-computer interface (BCI). Neuroimage, 36, pp. 581–593.CrossRefGoogle ScholarPubMed
29. Jinyin, Z., Sudre, G., Xin, L., Wei, W., Weber, D.J. and Bagic, A. (2011) Clustering linear discriminant analysis for MEG-Based brain computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19, pp. 221–231.Google Scholar
30. Citi, L., Poli, R., Cinel, C. and Sepulveda, F. (2008) P300-based BCI mouse with genetically-optimized analogue control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16.CrossRefGoogle ScholarPubMed
31. Bell, C.J., Shenoy, P., Chalodhorn, R. and Rao, R.P.N. (2008) Control of a humanoid robot by a noninvasive brain-computer interface in humans. Journal of Neural Engineering, 5, pp. 214–220.CrossRefGoogle ScholarPubMed
32. Herrmann, C.S. (2001) Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena. Experimental Brain Research, 137(3), pp. 346–353.CrossRefGoogle ScholarPubMed
33. Hinterberger, T., Schmidt, S., Neumann, N., Mellinger, J., Blankertz, B., Curio, G. and Birbaumer, N. (2004) Brain-computer communication and slow cortical potentials. IEEE Transactions on Biomedical Engineering, 51, pp. 1011–1018.CrossRefGoogle ScholarPubMed
34. Iversen, I.H., Ghanayim, N., Kübler, A., Neumann, N., Birbaumer, N. and Kaiser, J. (2008) A brain-computer interface tool to assess cognitive functions in completely paralyzed patients with amyotrophic lateral sclerosis. Clinical Neurophysiology, 119, pp. 2214–2223.CrossRefGoogle ScholarPubMed
35. Pfurtscheller, G. and da Silva, F.H.L. (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 110(11), pp. 1842–1857.CrossRefGoogle ScholarPubMed
36. Schlögl, A., Lee, F., Bischof, H. and Pfurtscheller, G. (2005) Characterization of four-class motor imagery EEG data for the BCI-competition 2005. Journal of Neural Engineering, 2, pp. L14–L22.CrossRefGoogle ScholarPubMed
37. Pfurtscheller, G., Brunner, C., Schlogl, A. and da Silva, F.H.L. (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage, 31, pp. 153–159.CrossRefGoogle ScholarPubMed
38. Fabiani, G.E., McFarland, D.J., Wolpaw, J.R. and Pfurtscheller, G. (2004) Conversion of EEG activity into cursor movement by a brain-computer interface (BCI). IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12, pp. 331–338.CrossRefGoogle ScholarPubMed
39. Long, J.Y., Li, Y.Q., Wang, H.T., Yu, T.Y., Pan, J.H. and Li, F. (2012) A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(5), pp. 720–729.CrossRefGoogle ScholarPubMed
40. Horki, P., Solis-Escalante, T., Neuper, C. and Müller-Putz, G. (2011) Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb. Medical and Biological Engineering and Computing, 49(5), pp. 567–577.CrossRefGoogle ScholarPubMed
41. Al-ani, T. and Trad, D. (2010) signal processing and classification approaches for brain-computer interface. Intelligent and Biosensors. V.S. Somerset, (Ed.), (InTech), pp. 25–66.Google Scholar
42. Jolliffe, I. (2002) Principal Component Analysis (New York: Springer-Verlag), DOI: 10.1007/b98835.Google Scholar
43. Comon, P. (1994) Independent component analysis: a new concept? Signal Processing, 36(3), pp. 287–314.CrossRefGoogle Scholar
44. An, X., Kuang, D., Guo, X., Zhao, Y. and He, L. (2014) A deep learning method for classification of EEG data based on motor imagery. Intelligent Computing in Bioinformatics, pp. 203–210.CrossRefGoogle Scholar
45. Ince, N.F., Arica, S. and Tewfik, A. (2006) Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time–frequency tilings. Journal of Neural Engineering, 3, 3.CrossRefGoogle ScholarPubMed
46. Kaiser, V., Bauernfeind, G., Kreilinger, A., Kaufmann, T., Kübler, A., Neuper, C. and Müller-Putz, G.R. (2014) Cortical effects of user training in a motor imagery based brain computer interface measured by fNIRS and EEG. NeuroImage, 85(1), pp. 432–444.CrossRefGoogle Scholar
47. Hwang, H.J., Kwon, K. and Im, C.H. (2009) Neurofeedback-based motor imagery training for brain-computer interface (BCI). Journal of Neuroscience Methods, 179(1), pp. 150–156.CrossRefGoogle ScholarPubMed
48. Boye, A.T., Kristiansen, U.Q., Billinger, M., do Nascimento, O.F. and Farina, D. (2008) Identification of movement-related cortical potentials with optimized spatial filtering and principal component analysis. Biomedical Signal Process . Control, 3, pp. 300–304.Google Scholar
49. Lin, C.J. and Hsieh, M.H. (2009) Classification of mental task from EEG data using neural networks based on particle swarm optimization. Neurocomputing, 72, pp. 1121–1130.CrossRefGoogle Scholar
50. Yıldırım, A. and Halici, U. (2013) Analysis of dimension reduction by PCA and AdaBoost on spelling paradigm EEG data Sixth International Conference on Biomedical Engineering and Informatics.CrossRefGoogle Scholar
51. Talukdar, M.T., Sakib, S.K., Pathan, N.S. and Fattah, S.A. (2014) Motor imagery EEG signal classification scheme based on autoregressive reflection coefficients. Informatics, Electronics & Vision (ICIEV), International Conference on. IEEE.CrossRefGoogle Scholar
52. Te-Won, L., Lewicki, M.S., Girolami, M. and Sejnowski, T.J. (1999) Blind source separation of more sources than mixtures using overcomplete representations. IEEE Signal Processing Letters, 6, pp. 87–90.CrossRefGoogle Scholar
53. Gao, J., Yang, Y., Lin, P., Wang, P. and Zheng, C. (2010) Automatic removal of eye-movement and blink artifacts from EEG signals. Brain Topography, 23, pp. 105–114.CrossRefGoogle ScholarPubMed
54. Erfanian, A. and Erfani, A. (2004) ICA-based classification scheme for EEG-based brain-computer interface: the role of mental practice and concentration skills. In Engineering in Medicine and Biology Society, IEMBS'04. 26th Annual International Conference of the IEEE, 1, pp. 235–238.CrossRefGoogle Scholar
55. Ramoser, H., Muller-Gerking, J. and Pfurtscheller, G. (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4), pp. 441–446.CrossRefGoogle ScholarPubMed
56. Grosse-Wentrup, M. and Buss, M. (2008) Multiclass common spatial patterns and information theoretic feature extraction. IEEE Transactions on Biomedical Engineering, 55(8), pp. 1991–2000.CrossRefGoogle ScholarPubMed
57. Ang, K.K., Chin, Z.Y., Zhang, H. and Guan, C. (2008) Filter bank common spatial pattern (FBCSP) in brain-computer interface. In Neural Networks, IJCNN. IEEE World Congress on Computational Intelligence, pp. 2390-2397.Google Scholar
58. Holland, J.H. (1975) Adaption in Natural and Artificial Systems (Cambridge, MA: MIT Press).Google Scholar
59. Corralejo, R., Hornero, R. and Alvarez, D. (2011) Feature selection using a genetic algorithm in a motor imagery-based Brain Computer Interface. Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE.CrossRefGoogle Scholar
60. Seno, D.B., Matteucci, M. and Mainardi, L. (2008) A genetic algorithm for automatic feature extraction in P300 detection. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN’08), Hong Kong, China, pp. 3145–3152.Google Scholar
61. Freund, R.E. and Schapire, Y. (1997) A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), pp. 119–139.CrossRefGoogle Scholar
62. Boostani, R. and Moradi, M.H. (2004) A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier. Journal of Neural Engineering, 1(4), p. 212.CrossRefGoogle ScholarPubMed
63. Fix, E. and Hodges, J.L. (1951) Discriminatory analysis-nonparametric discrimination: consistency properties. Technical Report 4. USAF School of Aviation Medicine, Randolph Field, TX.CrossRefGoogle Scholar
64. Fukunaga, K. (1972) Introduction to Statistical Pattern Recognition (Oxford, UK: Clarendon).Google Scholar
65. Hoffmann, U., Vesin, J.M., Ebrahimi, T. and Diserens, K. (2008) An efficient P300-based brain-computer interface for disabled subjects. Journal of Neuroscience Methods, 167, pp. 115–125.CrossRefGoogle ScholarPubMed
66. Garrett, D., Peterson, D.A., Anderson, C.W. and Thaut, M.H. (2003) Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Transactions of Neural Systems and Rehabilitation Engineering, 11, pp. 141–144.CrossRefGoogle ScholarPubMed
67. Cortes, C. and Vapnik, V. (1995) Support-vector networks. Machine Learning, 20, 273–297.CrossRefGoogle Scholar
68. Burges, C.J.C. (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, pp. 121–167.CrossRefGoogle Scholar
69. Blankertz, B., Curio, G. and Muller, K.R. (2002) Classifying single trial EEG: towards brain computer interfacing. Advances in Neural Information Processing Systems, 14, pp. 157–164.Google Scholar
70. Rakotomamonjy, A. and Guigue, V. (2008) BCI 2008, competition III: data set II—Ensemble of SVMs for BCI p300 speller. IEEE Transactions on Biomedical Engineering, 55(3), pp. 1147–1154.CrossRefGoogle Scholar
71. Jensen, F.V. (2001) Bayesian Networks and Decision Graphs (Berlin: Springer).CrossRefGoogle Scholar
72. Moon, T.K. (1996) The expectation-maximization algorithm. Signal Processing Magazine, IEEE, 13(6), pp. 47–60.CrossRefGoogle Scholar
73. Rabiner, L.R. and Juang, B.H. (1986) An introduction to hidden Markov models. IEEE ASSP Magazine, pp. 4–16.CrossRefGoogle Scholar
74. Obermaier, B., Guger, C., Neuper, C. and Pfurtscheller, G. (2001) Hidden Markov models for online classification of single trial EEG data. Pattern Recognition Letters, 22(12), pp. 1299–1309.CrossRefGoogle Scholar
75. Zhong, S. and Gosh, J. (2002) HMMs and coupled HMMs for multi-channel EEG classification. Proceedings of the IEEE International Joint Conference on. Neural Networks, 2, pp. 1154–1159.Google Scholar
76. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning internal representations by error propagation. Parallel Distributed Processing, 1, pp. 151–193.Google Scholar
77. Masic, N. and Pfurtscheller, G. (1993) Neural network based classification of single-trial EEG data. Artificial Intelligence in Medicine, 5(6), pp. 503–513.CrossRefGoogle ScholarPubMed
78. Anderson, C.W., Devulapalli, S.V. and Stolz, E.A. (1995) Determining mental state from EEG signals using parallel implementations of neural networks. Proceedings of the IEEE Workshop on Neural Networks for Signal in Processing, pp. 475–483.Google Scholar
79. Felzer, T. and Freisieben, B. (2003) Analyzing EEG signals using the probability estimating guarded neural classifier. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(4), pp. 361–371.CrossRefGoogle ScholarPubMed
80. Cecotti, H. and Graser, A. (2008) Time delay neural network with Fourier transform for multiple channel detection of steady-state visual evoked potential for brain-computer interfaces. Proceedings of the European Signal Processing Conference.Google Scholar
81. Haselsteiner, E. and Pfurtscheller, G. (2000) Using time dependent neural networks for EEG classification. IEEE Transactions on Rehabilitation Engineering, 8(4), pp. 457–463.CrossRefGoogle ScholarPubMed
82. Masic, N., Pfurtscheller, G. and Flotzinger, D. (2008) Neural network-based predictions of hand movements using simulated and real EEG data. Neurocomputing, 7(3), pp. 259–274.CrossRefGoogle Scholar
83. Hamedi, M., Salleh, S.H., Noor, A.M. and Mohammad-Rezazadeh, I. (2014) Neural network-based three-class motor imagery classification using time-domain features for BCI applications. Region 10 Symposium.CrossRefGoogle Scholar
84. LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998) Gradient-based learning applied to document recognition. Proceedings of IEEE, 86(11), pp. 2278–2324.CrossRefGoogle Scholar
85. Bengio, Y., Lamblin, P., Popovici, D. and Larochelle, H. (2007) Greedy layer-wise training of deep networks. Advances in Neural Information Processing Systems 19 (NIPS’06), pp. 153–160.Google Scholar
86. Hinton, G.E. (2002) Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8), pp. 1711–1800.CrossRefGoogle ScholarPubMed
87. Cecotti, H. and Axel, G. (2011) Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3), pp. 433–445.CrossRefGoogle ScholarPubMed
88. Junhua, L. and Cichocki, A. (2014) Deep learning of multifractal attributes from motor imagery induced EEG. Neural Information Processing (Springer International Publishing).Google Scholar
89. Rezaeitabar, Y. and Halici, U. (2016) A novel deep learning approach for classification of EEG motor imagery signals. Journal of Neural Engineering, in press.CrossRefGoogle Scholar
90. Delorme, A. and Makeig, S. (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. Journal of Neuroscience Methods, 134, pp. 9–21.CrossRefGoogle ScholarPubMed
91. Kothe, C.A. and Makeig, S. (2013) BCILAB: a platform for brain–computer interface development. Journal of Neural Engineering, 10(5), 056014.CrossRefGoogle ScholarPubMed
92. Vidaurre, C., Sander, T.H. and Schlögl, A. (2011) BioSig: the free and open source software library for biomedical signal processing. Computational Intelligence and Neuroscience, 935364. doi: 10.1155/2011/935364. pmid:21437227.CrossRefGoogle Scholar
93. Brainard, D.H. (1997) The psychophysics toolbox. Spatial Vision, 10, pp. 433–436.CrossRefGoogle ScholarPubMed
94. Blankertz, B. (2003) BCI Competition II–P300 speller dataset webpage. Online: http://www.bbci.de/competition/ii/,http://www.bbci.de/competition/ii/albany_desc/albany_desc_ii.html.Google Scholar
95. Blankertz, B. BCI Competition III– P300 speller dataset webpage. Online: http://www.bbci.de/competition/iii/, Documentation: http://www.bbci.de/competition/iii/desc_II.pdf, 2005, Retrieved 20/11/2010.Google Scholar
96. Blankertz, B. (2008) BCI Competition IV, Fraunhofer FIRST (IDA), http://ida. first.fraunhofer.de/projects/bci/competition_iv.Google Scholar
97. Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V. and Lécuyer, A. (2010) OpenViBE: an open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments. Presence: Teleoperators and Virtual Environments, 19(1), pp. 35–53.CrossRefGoogle Scholar
98. Blankertz, B., Dornhege, G., Krauledat, M., Schroder, M., Williamson, J., Murray-Smith, R. and Müller, K.-R. (2006) The Berlin brain-computer interface presents the novel mental typewriter Hex-o-Spell. In Proceedings of the Third International Brain Computer Interface Workshop and Training Course, Graz, Austria, pp. 108–109.Google Scholar
99. Cecotti, H. (2011) Spelling with non-invasive Brain–Computer Interfaces – current and future trends. Journal of Physiology-Paris, 105(1–3), pp. 106–114.CrossRefGoogle ScholarPubMed
100. Mora-Cortes, A., Manyakov, N.V., Chumerin, N. and Van Hulle, M.M. (2014) Language model applications to spelling with Brain-Computer Interfaces. Sensors (Basel), 14(4), pp. 5967–5993.CrossRefGoogle ScholarPubMed
101. Jia, C., Gao, X., Hong, B. and Gao, S. (2011) Frequency and phase mixed coding in SSVEP-based brain-computer interface. IEEE Transactions on Biomedical Engineering, 58, pp. 200–206.Google ScholarPubMed
102. D’albis, T., Blatt, R., Tedesco, R., Sbattella, L. and Matteucci, M. (2012) A predictive speller controlled by a brain-computer interface based on motor imagery. ACM Transactions on Computer–Human Interactions, 19, pp. 1–25.CrossRefGoogle Scholar
103. Palaniappan, R., Paramesran, R., Nishida, S. and Saiwaki, N. (2002) A new brain-computer interface design using fuzzy ARTMAP. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 10(3), pp. 140–148.CrossRefGoogle ScholarPubMed
104. Nicolaou, N. and Georgiou, J. (2008) Towards a Morse code-based non-invasive thought-to-speech converter. In BIOSTEC (Selected Papers), pp. 123–135.Google Scholar
105. Gelenbe, E., Feng, Y. and Krishnan, K.R.R. (1996) Neural network methods for volumetric magnetic resonance imaging of the human brain. Proceedings of the IEEE, 84(10), pp. 1488–1496.CrossRefGoogle Scholar
106. Gelenbe, E. and Fourneau, J.M. (1999) Random neural networks with multiple classes of signals. Neural Computation, 11(4), pp. 953–963.CrossRefGoogle ScholarPubMed
107. Gelenbe, E., Mao, Z.-H. and Li, Y.-D. (1999) Function approximation with spiked random networks. IEEE Transactions on Neural Networks, 10(1), pp. 3–9.CrossRefGoogle ScholarPubMed
108. Gelenbe, E. and Timotheou, S. (2008) Random neural networks with synchronized interactions. Neural Computation, 20(9), pp. 2308–2324.CrossRefGoogle ScholarPubMed
109. Keirn, Z.A. and Aunon, J.I. (1990) A new mode of communication between man and his surroundings. IEEE Transactions on Biomedical Engineering, 37, 1209–1214.CrossRefGoogle ScholarPubMed
110. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G. and Vaughan, T.M. (2002) Brain computer interfaces for communication and control. Clinical Neurophysiology, 113, pp. 767–791.CrossRefGoogle ScholarPubMed
111. Ryan, D.B., Frye, G.E., Townsend, G., Berry, D.R., Mesa, G.S., Gates, N.A. and Sellers, E.W. (2010) Predictive spelling with a P300-based brain-computer interface: increasing the rate of communication. International Journal of Human–Computer Interactions, 27, pp. 69–84.CrossRefGoogle Scholar
112. Gelenbe, E. and Yin, Y. (2016) Deep learning with random neural networks. IJCNN 2016, IEEE World Congress on Computational Intelligence, Vancouver, BC, July 2016.CrossRefGoogle Scholar