Brain-computer interface systems based on the near-infrared spectroscopy

A. V. Korshakov

Matematičeskaâ biologiâ i bioinformatika, Tome 13 (2018) no. 1, pp. 84-129

Résumé

The article describes the modern systems of man and computer interaction, or Brain-Computer Interfaces (BCIs), of several types. The interfaces, based on hemodynamic activity measurements of human cerebral cortex by the means of the near-infrared spectroscopy are discussed and also those, which are based on the combinations of the near-infrared spectroscopy, the electroencephalography and the encephalographies of other modalities. The theoretical fundamentals are provided of near-infrared spectroscopy devices functioning and a number of the typical experiments in the field noted. The special focus is on the functional magnetic resonance imaging and other methods of collecting biometrics data in the context of hemodynamic response. Various practical realizations and technical solutions of devices of mentioned type are described. The highlight of possible practical utilizations of human-machine interfaces in the sphere of medicine and/or medical rehabilitation is given. The analysis of practical solutions and applications of BCI systems is performed, which was added by a systematic publications review. Related disciplines have been also considered. Several examples of simultaneous usage of BCIs and robotics systems, which were built for the compensation of human body's lost functions also are provided. Some attention is paid to the Fast Optical Signals and to the researches in the field of optogenetics. The results of the conducted research make us to believe that the peeked field of knowledge is fairly young and at some areas of it the process of pure research and random search of new facts and technical solutions is still ongoing, although with an extremely high intensity. All things considered, the analised research direction should be judged as highly perspective, all the more so as it is joined with wide area of related disciplines with broad spectra of implementations.  

Bibliographie

[1] A. Frolov, P. Bobrov, O. Mokienko, D. Husek, L. Chernikova, R. Korshakov, A. Konovalov, “Sources of EEG activity most relevant to performance of brain-computer interface based on motor imagery”, Neural Network World, 22:1 (2012), 21–37 | DOI | DOI

[2] J. A. Tavada, M. Lesho, M. J. Tierney, “Keeping Watch on Glucose”, IEEE. Spectrum, 2002, 52–57

[3] V. A. Fesechko, Kh. A. Afana Luai, V. V. Romanov, A. A. Elizarov, “Analizator infrakrasnogo pogloscheniya dlya neinvazivnogo izmereniya kontsentratsii glyukozy v krovi”, Elektronika i svyaz, 2009, no. 2, Tematicheskii vypusk «Elektronika i nanotekhnologii», 230–235

[4] H. Ishizawa, A. Muro, T. Takano, K. Honda, H. Kanai, “Non-invasive blood glucose measurement based on ATR infrared spectroscopy”, SICE Annual Conference, 2008, 321–324

[5] J. L. Smith, The Pursuit of Noninvasive Glucose, 5th Edition, 2017 (data obrascheniya: 01.03.2018) https://www.researchgate.net/publication/317267760_The_Pursuit_of_Noninvasive_Glucose_5th_Edition

[6] O. S. Khalil, “Spectroscopic and clinical aspects of noninvasive glucose measurements”, Clinical Chemistry, 45 (1999), 165–177

[7] G. J. Mueller, B. Chance, R. R. Alfano, S. R. Arridge, J. Beuthan, E. Gratton, M. F. Kaschke, B. R. Masters, S. Svanberg, P. van der Zee, Medical Optical Tomography: Functional Imaging and Monitoring, PIE Inst. Adv. Opt. Techn., IS11, SPIE, Bellingham, 1993, 656 pp.

[8] V. V. Tuchin (ed.), Selected Papers on Tissue Optics: Applications in Medical Diagnostics and Therapy, SPIE Milestone Series, MS102, SPIE, Bellingham, 1994, 700 pp.

[9] C. Kolyva, A. Ghosh, I. Tachtsidis, D. Highton, C. E. Cooper, M. Smith, C. E. Elwell, “Cytochrome c oxidase response to changes in cerebral oxygen delivery in the adult brain shows higher brain-specificity than haemoglobin”, NeuroImage, 85 (2014), 234–244 | DOI | DOI

[10] A. V. Priezzhev, V. V. Tuchin, L. P. Shubochkin, Lazernaya diagnostika v biologii i meditsine, Nauka, M., 1989

[11] fNIRS Analysis Environment User's Manual, (data obrascheniya: 28.02.2018) http://nirx.net/nirscout/

[12] A. Bakker, B. Smith, P. Ainslie, K. Smith, Near-Infrared Spectroscopy, Applied Aspects of Ultrasonography in Humans, ed. Ph. Ainslie, 2012, 65–88

[13] A. Torricelli, D. Contini, A. Pifferi, M. Caffini, R. Re, L. Zucchelli, L. Spinelli, “Time domain functional NIRS imaging for human brain mapping”, NeuroImage, 85 (2014), 28–50 | DOI | DOI

[14] S. Takatani, M. D. Graham, “Theoretical analysis of diffuse reflectance from a two-layer tissue model”, IEEE Trans. Biomed. Eng., 26:12 (1979), 656–664 | DOI | DOI

[15] C. H. Schmitz, M. Löcker, J. M. Lasker, A. H. Hielscher, R. L. Barbour, “Instrumentation for fast functional optical tomography”, Review of Scientific Instruments, 73:2 (2002), 429–439 | DOI | DOI

[16] Y. Pei, H. L. Graber, R. L. Barbour, “Influence of systematic errors in reference states on image quality and on stability of derived information for DC optical imaging”, Applied Optics Copyright Optical Society of America, 40 (2001), 5755–5769

[17] D. Myers, M. McGraw, M. George, K. Mulier, G. Beilman, “Tissue hemoglobin index: a non-invasive optical measure of total tissue hemoglobin”, CriticalCare, 13:5, Suppl. (2009)

[18] T. E.J. Noponen, K. Kotilahti, I. Nissila, T. Kajava, P. T. Merilainen, “Effects of improper source coupling in frequency-domain near-infrared spectroscopy”, Physics in Medicine and Biology, 55:10 (2010), 2941–2960 | DOI | DOI

[19] S. Punwani, C. E. Cooper, M. Clemence, J. Penrice, P. Amess, J. Thorton, R. J. Ordidge, “Correlation between absolute deoxyhaemoglobin [dHb] measured by near infrared spectroscopy (NIRS) and absolute R2 as determined by magnetic resonance imaging (MRI)”, Adv. Exp. Med. Biol., 413 (1997), 129–137 | DOI | DOI

[20] S. Song, Y. Kobayashi, Masakatsu G. Fujie, “Monte-Carlo Simulation of Light Propagation considering Characteristic of Near-infrared LED and Evaluation on Tissue Phantom”, Procedia CIRP: The First CIRP Conference on Biomanufacturing, v. 5, 2013, 25–30

[21] T. Durduran, R. Choe, W. B. Baker, A. G. Yodh, “Diffuse optics for tissue monitoring and tomography”, Rep. Prog. Phys., 73:076701 (2010), 43

[22] F. Martelli, S. Del Bianco, A. Ismaelli, G. Zaccanti, Light Propagation through Biological Tissue and Other Diffusive Media: Theory, Solutions, and Software, SPIE Press, Washington, USA, 2009

[23] A. V. Gorshkov, M. Yu. Kirillin, “Monte Carlo simulation of brain sensing by optical diffuse spectroscopy”, Journal of Computational Science, 3:6 (2012), 498–503 | DOI | DOI

[24] J. B. Jeeva, M. Singh, “Reconstruction of optical scanned images of inhomogeneities in biological tissues by Monte Carlo simulation”, Computers in Biology and Medicine, 60:1 (2015), 92–99 | DOI | DOI

[25] L. Gagnon, M. A. Yucel, M. Dehaes, R. J. Cooper, K. L. Perdue, J. Selb, T. J. Huppert, R. D. Hoge, D. A. Boas, “Quantification of the cortical contribution to the NIRS signal over the motor cortex using concurrent NIRS-fMRI measurements”, NeuroImage, 59:4 (2012), 3933–3940 | DOI | MR | DOI | MR

[26] A. V. Korshakov, “Obrabotka BIKS signala modifitsirovannym metodom empiricheskikh mod s tselyu vydeleniya gemodinamicheskikh otklikov na tselevoe mentalnoe sostoyanie dlya IMK, kharakterizuyuscheesya spetsificheskim vremenem stanovleniya”, XIII Mezhdunarodnyi mezhdistsiplinarnyi kongress “Neironauka dlya meditsiny i psikhologii”, 2017, 222–223

[27] D. V. Sivukhin, “Pogloschenie sveta i ushirenie spektralnykh linii”, Obschii kurs fiziki, v. IV, Optika, Nauka, M., 2005, 582–583

[28] K. S. Shifrin, Rasseyanie sveta v mutnoi srede, M.–L., Gos. izd-vo tekhniko-teoreticheskoi literatury, 1951, 288 pp.

[29] B. N. Klossovskii, Tsirkulyatsiya krovi v mozgu, ed. B.N. Klossovskii, Medgiz, M., 1951, 371 pp.

[30] A. Cichocki, S. Amari, Adaptive blind signal and image processing: Learning Algorithms and Applications, John Wiley Sons, Ltd, 2002, 564 pp. | MR | MR

[31] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”, Royal Society. Proc. R. Soc. Lond. A, 1998, 995 | MR | Zbl | MR | Zbl

[32] K. Dzhonson, A. Gai, “Vozdeistvie neioniziruyuschego elektromagnitnogo izlucheniya na biologicheskie sredy i sistemy”, TIIER, 60:6 (1972), 49–79

[33] R. Shmidt, G. Tevs (red.), Fiziologiya cheloveka, v. 2, Mir, M., 1996, 313 pp.

[34] N. Birbaumer, A. R. Murguialday, L. Cohend, “Brain-computer interface in paralysis”, Current Opinion in Neurology, 21 (2008), 634–638 | DOI | DOI

[35] A. A. Frolov, V. Yu. Roschin, “Interfeis mozg-kompyuter. Realnost i perspektivy”, Nauchnaya sessiya MIFI-2008, Vserossiiskaya nauchno-tekhnicheskaya konferentsiya «Neiroinformatika-2008»: lektsii po neiroinformatike, v. 1, 2008, 82–125

[36] K. K. Ang, C. Guan, “Brain-computer interface in stroke rehabilitation”, Journal of Computing Science and Engineering, 7:2 (2013), 139–146 | DOI | DOI

[37] R. Padmavathi, V. Ranganathan, “A review on EEG based brain computer interface systems”, International Journal of Emerging Technology and Advanced Engineering, 4 (2014)

[38] S. S. Purkayastha, V. K. Jain, H. K. Sardana, “Topical Review: A review of various techniques used for measuring brain activity in brain computer interfaces”, Advance in Electronic and Electric Engineering, 4 (2014), 513–522

[39] N. Birbaumer, L. G. Cohen, “Brain-computer interfaces: communication and restoration of movement in paralysis”, J. Physiol., 579 (2007), 621–636 | DOI | DOI

[40] R. Adams, G. C. Bahr, B. Moreno, “Brain computer interfaces: psychology and pragmatic perspectives for the future”, AISB 2008 Proceedings of the AISB 2008 Symposium on Brain Computer Interfaces and Human Computer Interaction: A Convergence of Ideas, v. 5, 2008, 1–6 | Zbl | Zbl

[41] A. Zepeda, C. Arias, F. Sengpiel, “Optical imaging of intrinsic signals:recent developments in the methodology and its applications”, Neuroscience Methods, 2004, no. 136, 1–21 | DOI | DOI

[42] G. Xu, X. Li, D. Li, X. Liu, “A DAQ-Device-Based continuous wave near-infrared spectroscopy system for measuring human functional brain activity”, Computational and Mathematical Methods in Medicine, 2014 (2014), 107320 | DOI | DOI

[43] R. K. Almajidy, K. S. Le, U. G. Hofmanna, “Novel near infrared sensors for hybrid BCI applications”, Advanced Microscopy Techniques IV and Neurophotonics II, Proc. SPIE, 9536, 2015, 95361H | DOI | DOI

[44] S. Coyle, T. Ward, C. Markham, “Cerebral Blood Flow Changes related to Motor Imagery, using Near-infrared Spectroscopy (NIRS)”, World Congress on Medical Physics and Biomedical Engineering (2003)

[45] F. Matthews, C. Soraghan, T. Ward, C. Markham, B. Pearlmutter, “Software platform for rapid prototyping of NIRS brain computer interfacing techniques”, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2008, 4840–4843 | DOI | DOI

[46] J. B. F. Van Erp, F. Lotte, M. Tangermann, “Brain-computer interfaces: Beyond medical applications”, Computer, 45:4 (2012), 26–34 | DOI | DOI

[47] P. D. Bobrov, M. R. Isaev, A. V. Korshakov, V. V. Oganesyan, J. V. Kerechanin, A. I. Popodko, A. A. Frolov, “Sources of electrophysiological and foci of hemodynamic brain activity most relevant for controlling a hybrid brain-Computer interface based on classification of EEG patterns and near-infrared spectrography signals during motor imagery”, Human Physiology, 42:3 (2016), 241–251 | DOI | DOI

[48] G. Neuper C. Pfurtscheller, “Motor imagery and direct brain-computer communication”, Proc. IEEE Neural Eng., 89 (2001), 1123

[49] L. Citi, R. Poli, C. Cinel, F. Sepulveda, “P300-Based BCI Mouse With GeneticallyOptimized Analogue Control”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16:1 (2008) | DOI | DOI

[50] L. M. Ward, R. T. Aitchison, M. Tawse, A. J. Simmers, U. Shahani, “Reduced Haemodynamic Response in the Ageing Visual Cortex Measured by Absolute fNIRS”, PloS ONE, 10:4 (2015), e0125012 | DOI | DOI

[51] N. N. Soe, M. Nakagawa, “Chaotic properties of hemodynamic response in functional near infrared spectroscopic measurement of brain activity”, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 2:3 (2008), 95–104

[52] T. Limongi, G. Di Sante, M. Ferrari, V. Quaresima, “Detecting Mental Calculation Related Frontal Cortex Oxygenation Changes for Brain Computer Interface Using Multi-Channel Functional Near Infrared Topography”, International Journal of Bioelectromagnetism, 11:2 (2009), 86–90

[53] K. K. Ang, J. Yu, C. Guan, “Extracting and selecting discriminative features from high density NIRS-based BCI for numerical cognition”, The 2012 International Joint Conference on Neural Networks, IJCNN, 2012, 1–6

[54] F. A. Fishburn, M. E. Norr, A. V. Medvedev, C. J. Vaidya, “Sensitivity off NIRS to cognitive state and load”, Frontiers in Human. Neuroscience, 8 (2014), 76 | DOI | DOI

[55] A. Villringer, J. Planck, C. Hock, L. Schlenkofer, U. Dirnagl, “Near infrared spectroscopy (NIRS): a new tool to study hemodynamic changes during activation of brain function in human adults”, Neuroscience Letters, 154 (1993), 101–104 | DOI | DOI

[56] G. Matsuda, K. Hiraki, “Sustained decrease in oxygenated hemoglobin during video games in the dorsal prefrontal cortex: A NIRS study of children”, NeuroImage, 29 (2006), 706–711 | DOI | DOI

[57] M. Guirgis, T. Falk, S. Power, S. Blain, T. Chau, “Harnessing physiological responses to improve NIRS-based brain-computer interface performance”, Proc. ISSNIP Biosignals Biorobotics Conf., 2010, 59–62

[58] C. Herff, D. Heger, F. Putze, C. Guan, T. Schultz, “Self-paced BCI with NIRS based on speech activity”, International BCI Meeting (2013)

[59] W. S. Li, Y. T. Li, “FOMs of Consciousness Measurement”, Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering, 2015

[60] S. Hori, M. Mori, M. Takehisa, S. Akitoshi, “Development and inspection of NIRS-Based real-time feedback BCI system: Fusion of Neuroscience and Media-Art”, Transactions of Japanese Society for Medical and Biological Engineering, 51, Suppl. (2013), R-90 | DOI | DOI

[61] V. Kaiser, G. Bauernfeinda, T. Kaufmannb, A. Kreilingera, A. Kublerb, C. Neupera, “Cortical Effects of User Learning in a Motor-Imagery BCI Training”, International Journal of Bioelectromagnetism, 13:2 (2011), 60–61

[62] H. J. Hwang, J. H. Lim, D. W. Kim, C. H. Ima, “Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces”, Journal of Biomedical Optics, 19:7 (2014) | DOI | MR | Zbl | DOI | MR | Zbl

[63] C. N. Gupta, R. Palaniappan, “Using EEG and NIRS for brain-computer Interface and Cognitive Performance Measures: A Pilot Study”, International Journal of Cognitive Performance Support, 1:1 (2013), 69 | DOI | Zbl | DOI | Zbl

[64] N. Naseer, K.-S. Honga, “Classification of functional near-infrared spectroscopy signalscorresponding to the right- and left-wrist motor imagery fordevelopment of a brain-computer interface”, Neuroscience Letters, 553 (2013), 84–89 | DOI | DOI

[65] J. N. Mak, J. R. Wolpaw, “Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects”, IEEE Rev. Biomed. Eng., 2 (2009), 187–199 | DOI | MR | DOI | MR

[66] J. M. Belda-Lois, S. Mena-del Horno, I. Bermejo-Bosch, J. C. Moreno, J. L. Pons, D. Farina, M. Losa, M. Molinari, F. Tamburella, A. Ramos, A. Caria, T. SolisEscalante, C. Brunner, M. Rea, “Rehabilitation of gait after stroke: a review towards a top-down approach”, Journal of NeuroEngineering and Rehabilitation, 8 (2011), 66 | DOI | DOI

[67] E. Buch, C. Weber, L. G. Cohen, C. Braun, M. A. Dimyan, T. Ard, J. Mellinger, A. Caria, S. Soekadar, A. Fourkas et al., “Think to move: a neuromagnetic braincomputer interface (BCI) system for chronic stroke”, Stroke, 39 (2008), 910–917 | DOI | DOI

[68] C. Soraghan, F. Matthews, C. Markham, B. A. Pearlmutter, R. O'Neill, T. E. Ward, “A 12- Channel, real-time near-infrared spectroscopy instrument for brain-computer interface applications”, Conf. Proc. IEEE Eng. Med. Biol. Soc. (2008), 5648–5651

[69] N. Birbaumer, F. Piccione, S. Silvoni, M. Wildgruber, “Ideomotor silence: the case of complete paralysis and brain-computer interfaces (BCI)”, Psychological Research, 76 (2012), 183–191 | DOI | DOI

[70] L. H. Bi, X. A. Fan, Y. I. Liu, “EEG-Based-Controlled Mobile Robots”, A Survey IEEE Transactions on human-machine systems, 43:2 (2003)

[71] T. E. Ward, C. J. Soraghan, F. Matthews, C. Markham, “A Concept for Extending the Applicability of Constraint-Induced Movement Therapy through Motor Cortex Activity Feedback Using a Neural Prosthesis”, Computational Intelligence and Neuroscience, 2007, 51363

[72] B. T. Volpe, M. Ferraro, D. Lynch, P. Christos, J. Krol, Ch. Trudel, H. I. Krebs, N. Hogan, “Robotics and other devices in the treatment of patients recovering from stroke”, Current Neurology and Neuroscience Reports, 5:6 (2005), 465–470 | DOI | DOI

[73] M. J. Johnson, “Recent trends in robot-assisted therapy environments to improve real-life functional performance after stroke”, Journal of NeuroEngineering and Rehabilitation, 3 (2006), 29 | DOI | Zbl | DOI | Zbl

[74] L. Dipietro, M. Ferraro, J. J. Palazzolo, H. I. Krebs, VolpeB. T., N. Hogan, “Customized interactive robotic treatment for stroke: EMG-triggered therapy”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13:3 (2005), 325–334 | DOI | DOI

[75] A. Portelli, I. Daly, M. Spencer, S. Nasuto, “Low Cost Brain Computer Interface First Results”, Proceedings of the 5th International Brain-Computer Interface Conference (September 22–24, 2011, Graz University of Technology, Austria), 2011, 320–323 (data obrascheniya: 10.03.2018) https://www.tugraz.at/fileadmin/user_upload/Institute/INE/Proceedings/Proceedings_BCI_Conference_2011_neu.pdf

[76] B. T. Volpe, H. I. Krebs, N. Hogan, L. Edelsteinn, C. M. Diels, M. L. Aisen, “Robot training enhanced motor outcome in patients with stroke maintained over 3 years”, Neurology, 53:8 (1999), 1874–1876 | DOI | DOI

[77] M. Ferraro, J. J. Palazzolo, J. Krol, H. I. Krebs, N. Hogan, B. T. Volpe, “Robot-aided sensorimotor arm training improves outcome in patients with chronic stroke”, Neurology, 61:11 (2003), 1604–1607 | DOI | DOI

[78] R. Colombo, F. Pisano, S. Micera, A. Mazzone, C. Delconte, M. Ch. Carrozza, P. Dario, G. Minuco, “Robotic techniques for upper limb evaluation and rehabilitation of stroke patients”, IEEE Transactions on Neural Systems and Rehabilitation Engineering., 13:3 (2005), 311–324 | DOI | DOI

[79] K. Tai, T. Chau, “Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface”, Journal of NeuroEngineering and Rehabilitation., 6 (2009), 39 | DOI | DOI

[80] K. Akilandeswari, G. M. Nasira, “Swarm Optimized Feature Selection of EEG Signals for Brain-Computer Interface”, International Journal of Computational Intelligence and Informatics, 4:1 (2014)

[81] R. Sitaram, H. Zhang, C. Guan, M. Manoj Thulasidas, Y. Hoshi, A. Ishikawa, K. Shimizu, N. Birbaumer, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface”, NeuroImage, 34 (2007), 1416–1427 | DOI | DOI

[82] Y. Tomita, F. B. Vialatte, G. Dreyfus, Y. Mitsukura, H. Bakardjian, A. Cichocki, “Bimodal BCI using simultaneously NIRS and EEG”, IEEE Trans. Biomed. Eng., 61:4 (2014), 1274–1284 | DOI | DOI

[83] G. Bauernfeind, R. Leeb, S. C. Wriessnegger, G. Pfurtscheller, “Development set-up and first results for a one-channel near-infrared spectroscopy system”, Biomed. Tech. (Berl.), 53:1 (2008), 36–43 | DOI | DOI

[84] M. Wolf, U. Wolf, V. Toronov, A. Michalos, L. A. Paunescu, J. H. Choi, E. Gratton, “Different time evolution of oxyhemoglobin and deoxyhemoglobin concentration changes in visual and motor cortices during functional stimulation: a near-infrared spectroscopy study”, NeuroImage, 2002, no. 16, 704–712 | DOI | DOI

[85] S. M. Coyle, T. E. Ward, C. M. Markham, “Brain-computer nterface using a simplified functional near-infrared spectroscopy system”, J. Neural. Eng., 4 (2007), 219–226 | DOI | DOI

[86] S. D. Power, T. H. Falk, T. Chau, “Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain nearinfrared spectroscopy”, Neural. Eng., 7 (2008)

[87] H. Ogata, T. Mukai, T. Yagi, “A study on the frontal cortex in cognitive tasks using nearinfrared spectroscopy”, Proc. IEEE EMBS, 2007, 4731–4734

[88] T. H. Falk, M. Guirgis, S. Sarah Power, T. Chau, “Taking NIRS-BCIs outside the lab: towards achieving robustness against environment noise”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19:2 (2011) | DOI | Zbl | DOI | Zbl

[89] S. D. Power, A. Kushki, T. Chau, “Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS”, PLoS ONE, 7:7 (2012) | DOI | DOI

[90] N. D. Tam, G. Zouridakis, “Optical imaging of motor cortical hemodynamic response to directional arm movements using near-infrared spectroscopy”, International Journal of Biological Engineering, 3:2 (2013), 11–17

[91] K. S. Hong, N. Naseer, Y. H. Kimc, “Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI”, Neuroscience Letters, 587 (2015), 87–92 | DOI | DOI

[92] K. Yanagisawa, H. Sawai, H. Tsunashima, “Development of NIRS-BCI system using Perceptron”, 12th International Conference on Control, Automation and Systems (ICCAS 2012) (17–21 Oct. Korea), 2012, 17–21

[93] S. M. Coyle, T. E. Ward, C. M. Markham, G. McDarby, “On the suitability of near-infrared (NIR) systems for next generation brain-computer interfaces”, Physiol. Meas., 25 (2004), 815–822 | DOI | DOI

[94] M. Strait, M. Scheutz, “Building a literal bridge between robotics and neuroscience using functional near infrared spectroscopy”, HRI '14: Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction (Bielefeld, Germany, March 03–06, 2014) | DOI | DOI

[95] K. K. Ang, K. S. G. Chua, K. S. Phua, C. Wang, Z. Y. Chin, C. W. K. Kuah, C. Guan, “A Randomized controlled trial of EEG-based motor imagery Brain-Computer Interface robotic rehabilitation for stroke”, Clinical EEG and Neuroscience, 46:4 (2015), 310–320 | DOI | DOI

[96] D. Brauchle, M. Vukelic, R. Bauer, A. Ghabaraghi, “Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation”, Frontiers in Human Neuroscience, 9 (2015), 564 | DOI | DOI

[97] J. H. Lee, J. Ryu, F. A. Jolesz, Z. H. Cho, S. S. Yoo, “Brain-machine interface via realtime fMRI: preliminary study on thoughtcontrolled robotic arm”, Neurosci. Lett., 450:1 (2009), 1–6 | DOI | DOI

[98] D. J. McFarland, J. R. Wolpaw, “Brain-computer interface operation of robotic and prosthetic devices”, Computer, 41 (2010), 52–56 | DOI | DOI

[99] G. R. Muller-Putz, R. Scherer, G. Pfurtscheller, R. Rupp, “EEG-based neuroprosthesis control: a step towards clinical practice”, Neurosci. Lett., 382:1–2 (2005), 169–174 | DOI | DOI

[100] F. Galan, M. Nuttin, E. Lew, P. W. Ferrez, G. Vanacker, J. Philips, J. del R. Millan, “A brain-actuated wheelchair: asynchronous and non-invasive Brain-computer interfaces for continuous control of robots”, Clin. Neurophysiol., 9:119 (2008), 2159–2169 | DOI | DOI

[101] K. Tanaka, K. Matsunaga, H. O. Wang, “Electroencephalogrambased control of an electric wheelchair”, IEEE Trans. Robotics, 21 (2005), 762–766 | DOI | DOI

[102] D. Aminaka, S. Makino, T. M. Rutkowski, “SSVEP Brain-computer Interface using Green and Blue Lights”, 10th AEARU Workshop on Computer Science and Web Technology: The Association of East Asian Research Universities (AEARU), University of Tsukuba, Tsukuba, Japan, 2015

[103] D. Aminaka, S. Makino, T. M. Rutkowski, “Chromatic SSVEP BCI paradigm targeting the higher frequency EEG Responses”, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, 2014, 1–7 | DOI | DOI

[104] D.-J. Kim, Z. Wang, N. Paperno, A. Behal, “System design and implementation of UCFMANUS-An intelligent assistive robotic manipulator”, IEEE/ASME Transactions on Mechatronics, 19:1 (2014), 225–237 | DOI | DOI

[105] K. M. Tsui, D. J. Feil-Seifer, M. J. Mataric, H. A. Yanco, “Performance Evaluation Methods for Assistive Robotic Technology”, Performance Evaluation and Benchmarking of Intelligent Systems, eds. Madhavan R., Tunstel E., Messina E., Springer, 2009, 41–66 | DOI | DOI

[106] K. Choi, A. Cichocki, “Control of a wheelchair by motor imagery in real time”, Intelligent Data Engineering and Automated Learning —IDEAL 2008, Lecture Notes in Computer Science, 5326, 2008, 330–337 | DOI | DOI

[107] S. V. Kotov, L. G. Turbina, P. D. Bobrov, A. A. Frolov, O. G. Pavlova, M. E. Kurganskaya, E. V. Biryukova, “Reabilitatsiya bolnykh, perenesshikh insult, s pomoschyu bioinzhenernogo kompleksa «interfeis mozg-kompyuter + ekzoskelet»”, Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova, 2014, no. 12, 66–72

[108] R. Kh. Lyukmanov, O. A. Mokienko, L. A. Chernikova, A. V. Chervyakov, N. A. Suponeva, M. A. Piradov, “Interfeis mozg-kompyuter v reabilitatsii bolnykh s postinsultnym parezom v ruke: pervyi opyt klinicheskogo primeneniya v Rossii”, Izbrannye voprosy neiroreabilitatsii, Materialy VII mezhdunarodnogo kongressa «Neiroreabilitatsiya-2015» (Moskva, 2015)

[109] N. A. Babushkina, E. V. Biryukova, P. D. Bobrov, Ya. V. Kerechanin, M. E. Kurganskaya, A. A. Frolov, “Opredelenie repertuara dvizhenii mnogosustavnogo ekzoskeletaruki, upravlyaemogo interfeisom mozg-kompyuter (IMK)”, Izbrannye voprosy neiroreabilitatsii, Materialy VII mezhdunarodnogo kongressa «Neiroreabilitatsiya-2015» (Moskva, 2015)

[110] C. B. Kotov, L. G. Turbina, P. D. Bobrov, A. A. Frolov, O. G. Pavlova, M. E. Kurganskaya, E. V. Biryukova, Materialy VII mezhdunarodnogo kongressa «Neiroreabilitatsiya-2015» (Moskva, 2015)

[111] A. A. Frolov, E. V. Biryukova, P. D. Bobrov, A. K. Platonov, V. E. Pryanichnikov, “Biologicheski adekvatnye printsipy upravleniya ekzoskeletonom ruki cheloveka”, Informatsionno-izmeritelnye i upravlyayuschie sistemy, 11:1 (2013), 12–19

[112] K. Tumanov, R. Goebel, R. Mockel, B. Sorger, G. Weiss, “fNIRS-based BCI for robot Control”, Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015 (Istanbul, Turkey, May 04–08, 2015)

[113] R. Lakshmi, T. V. Prasad, V. Chandra Prakash, “Survey on EEG signal processing methods”, International Journal of Advanced Research in Computer Science and Software Engineering, 4:1 (2014) | DOI | MR | DOI | MR

[114] J. Mellinger, G. Schalk, C. Braun, H. Preissl, W. Rosenstiel, N. Birbaumer, A. Kubler, “AnMEG-basedbrain-computerinterface (BCI)”, NeuroImage, 36:3 (2007), 581–593 | DOI | DOI

[115] F. Lopes da Silva, “EEG and MEG: Relevance to Neuroscience”, Neuron, 80 (2013), 1112–1128 | DOI | DOI

[116] N. M. Pankratova, M. N. Ustinin, R. R. Linas, “Obnaruzhenie patologicheskoi aktivnosti golovnogo mozga po dannym magnitnoi entsefalografii”, Matematicheskaya biologiya i bioinformatika, 8:2 (2013), 679–690 | DOI | DOI

[117] A. V. Korshakov, M. A. Polikarpov, M. N. Ustinin, V. V. Sychev, S. D. Rykunov, S. P. Naurzakov, A. P. Grebenkin, V. Ya. Panchenko, “Registratsiya i analiz tochnykh chastotnykh EEG/MEG otklikov auditornoi kory golovnorgo mozga cheloveka v otvet na monauralnuyu stimulyatsiyu zvukom s fiksirovannymi chastotnymi sostavlyayuschimi”, Matematicheskaya biologiya i bioinformatika, 9:1 (2014), 296–308 | DOI | DOI

[118] M. N. Ustinin, M. A. Polikarpov, A. N. Pankratov, S. D. Rykunov, S. P. Naurzakov, A. P. Grebenkin, V. Ya. Panchenko, “Sravnitelnyi analiz eksperimentalnykh dannykh magnitnoi entsefalografii”, Matematicheskaya biologiya i bioinformatika, 6:1 (2011), 63–70 | DOI | MR | DOI | MR

[119] M. Jas, D. A. Engemann, Y. Bekhti, F. Raimondo, A. Gramfort, “Autoreject: Automated artifact rejection for MEG and EEG data”, NeuroImage, 159 (2017), 417–429 | DOI | DOI

[120] M. C. Schaeffer, E. Labyt, V. Rohu, N. Tarrin, I. Vergara, S. Cokgungor, A. Eliseyev, G. Charvet, C. M. A. L. Benabid, T. Aksenova, “Hand movement decoding from magnetoencephalographic signals for BCI applications”, Neurophysiologie Clinique/Clinical Neurophysiology, 46:2 (2016), 104 | DOI | DOI

[121] M. N. Ustinin, V. V. Sychev, K. D. Walton, R. R. Llinas, “New Methodology for the Analysis and Representation of Human Brain Function: MEGMRIAn”, Mathematical Biology and Bioinformatics, 9:2 (2014), 464–481 | DOI | DOI

[122] S. D. Rykunov, M. N. Ustinin, A. G. Polyanin, V. V. Sychev, R. R. Linas, “Kompleks programm dlya rascheta partsialnykh spektrov golovnogo mozga cheloveka”, Matematicheskaya biologiya i bioinformatika, 11:1 (2016), 127–140 | DOI | DOI

[123] M. R. Daliri, “A hybrid method for the decoding of spatial attention using the MEG brain signals”, Biomedical Signal Processing and Control, 10 (2014), 308–312 | DOI | DOI

[124] H. Zarshenas, M. Bamdad, H. Grailu, A. A. Shakoori, “Classifier Combination Approach in Motion Imagery Signals Processing for Brain Computer Interface”, World Academy of Science, Engineering and Technology. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 7:6 (2013)

[125] R. R. Yager, “On ordered weighted averaging aggregation operators in multi criteria decision making”, Man and Cybernetics, 18 (1988), 183–190 | DOI | MR | Zbl | DOI | MR | Zbl

[126] M. J. Khan, K. S. Hong, N. Naseer, M. R. Bhutta, S. H. Yoon, “Hybrid EEG-NIRS BCI for rehabilitation using different-source brain signals”, Proc. of the SICE Annual Conference \date September 9–12, 2014, Hokkaido University, Sapporo, Japan, 2014

[127] M. J. Khan, M. J. Hong, K. S. Hong, “Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface”, Frontiers in Human Neuroscience Methods, 8 (2014), 244

[128] M. J. Khan, K. S. Hong, N. Naseer, M. R. Bhutta, “Multi-decision detection using EEGNIRS based hybrid Brain-Computer Interface (BCI)”, 20th Annual Meeting of the Organization for Human Brain Mapping (OHMB) (2014)

[129] S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer, K. R. Muller, B. Blankertz, “Enhanced performance by a hybrid NIRS-EEG brain computer interface”, NeuroImage, 59 (2012), 519–529 | DOI | DOI

[130] B. Koo, H. G. Lee, Y. Nam, H. Kang, C. S. Koh, H. C. Shin, S. Choi, “A hybrid NIRSEEG system for self-paced brain computer interface with online motor imagery”, Neuroscience Methods, 244 (2015), 26–32 | DOI | DOI

[131] R. Leeb, H. Sagha, R. Chavarriaga, R. Del, J. Millan, “Multimodal fusion of muscle and brain signals for a hybrid-BCI”, Conf. Proc. IEEE Eng. Med. Biol., 2010, 4343–4346 | DOI | DOI

[132] G. Pfurtscheller, B. Z. Allison, C. Brunner, G. Bauernfeind, T. Solis-Escalante, R. Scherer, T. O. Zander, G. Mueller-Putz, C. Neuper, N. Birbaumek, “The hybrid BCI”, 2010, 4, 30

[133] S. Fazli, S. Dahne, W. Samek, F. Biemann, K. R. Muller, “Learning from more than one data source: data fusion techniques for sensorimotor rhythm-based Brain-Computer Interfaces”, Proceedings of the IEEE, 103:6 (2015) | DOI | DOI

[134] C. J. Soraghan, F. Matthews, D. Kelly, C. Markham, B. A. Pearlmutter, R. O'Neill, “A Dual-channel optical Brain-Computer Interface in a gaming environment”, Proceedings of 9th International Conference on Computer Games: AI, Animation, Mobile, Educational and Serious Games (CGAMES), Dublin Institute of Technology, Ireland, 2006

[135] M. Strait, C. Canning, M. Scheutz, “Limitations of NIRS-Based BCI for realistic applications in human-computer interaction”, Proceedings of the Fifth International Brain-Computer Interface Meeting, 2013, 002 | DOI | DOI

[136] G. Strangman, J. P. Culver, J. H. Thompson, D. A. Boas, “A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation”, NeuroImage, 17 (2002), 719–731 | DOI | DOI

[137] H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhaupl, A. Villringer, “Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults”, NeuroImage, 12 (2000), 623–639 | DOI | DOI

[138] F. Scarpa, S. Cutini, P. Scatturin, R. Dell'Acqua, G. Sparacino, “Bayesian filtering of human brain hemodynamic activity elicited by visual short-term maintenance recorded through functional near-infrared spectroscopy (fNIRS)”, Optics Express, 18:25 (2010), 26550–26568 | DOI | DOI

[139] T. Q. Duong, D. S. Kim, K. Ugurbil, S. G. Kim, “Spatiotemporal dynamics of the BOLD fMRI signals: Toward mapping submillimeter cortical columns using the early negative response”, Magn. Reson. Med., 44 (2000), 231–242 | 3.0.CO;2-T class='badge bg-secondary rounded-pill ref-badge extid-badge'>DOI | DOI

[140] R. S. Menon, S. Ogawa, X. Hu, J. S. Strupp, P. Andersen, K. Ugurbil, “BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: Echo-planar imaging mirrors previous optical imaging using intrinsic signals”, Magn. Reson. Med., 33 (1995), 453–459 | DOI | DOI

[141] D. Malonek, A. Grinvald, “Vascular regulation at sub millimeter range. Sources of intrinsic signals for high resolution optical imaging”, Adv. Exp. Med. Biol., 413 (1996), 215–220 | DOI | DOI

[142] I. Vanzetta, A. Grinvald, “Increased cortical oxidative metabolism due to sensory stimulation: Implications for functional brain imaging”, Science, 286 (1999), 1555–1558 | DOI | DOI

[143] A. F. Cannestra, N. Pouratian, S. Y. Bookheimer, N. A. Martin, D. P. Beckerand, A. W. Toga, “Temporal spatial differences observed by functional MRI and human intraoperative optical imaging”, Cereb. Cortex, 11 (2001), 773–782 | DOI | DOI

[144] M. Firbank, E. Okada, D. T. Delpy, “A theoretical study of the signal contribution of regions of the adult head to near-infrared spectroscopy studies of visual evoked responses”, NeuroImage, 8 (1998), 69–78 | DOI | DOI

[145] S. Punwani, R. J. Ordidge, C. E. Cooper, P. Amess, M. Clemence, “MRI measurements of cerebral deoxyhaemoglobin concentration [dHb]-Correlation with near infrared spectroscopy (NIRS)”, NMR Biomed., 11 (1998), 281–289 | 3.0.CO;2-6 class='badge bg-secondary rounded-pill ref-badge extid-badge'>DOI | DOI

[146] R. Sitaram, A. Caria, N. Birbaumer, “Hemodynamic brain-computer interfaces for communication and rehabilitation”, Neural Networks, 2009 | Zbl | Zbl

[147] J. C. Ye, S. Tak, K. E. Jang, J. Jung, J. Jang, “NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy”, NeuroImage, 44:2 (2009), 428–447 | DOI | DOI

[148] R. Hoge, M. Franceschini, R. Covolan, T. Huppert, J. Mandeville, D. A. Boas, “Simultaneous recording of task-induced changes in blood oxygenation, volume, and flow using diffuse optical imaging and arterial spin-labeling MRI”, NeuroImage, 25:3 (2005), 701–707 | DOI | DOI

[149] N. Logothetis, “The underpinnings of the BOLD functional magnetic resonance imaging signal”, Neurosci., 23:10 (2003), 3963–3971 | DOI | DOI

[150] D. Benaron, S. Hintz, A. Villringer, D. A. Boas, A. Kleinschmidt, J. Frahm, C. Hirth, H. Obrig, J. van Houten, E. Kermit et al, “Noninvasive functional imaging of human brain using light”, J. Cereb. Blood Flow Metab., 20 (2000), 469–477 | DOI | DOI

[151] D. A. Boas, M. A. O'Leary, B. Chance, A. G. Yodh, “Detection and characterization of optical inhomogeneities with diffuse photon density waves: a signal-to-noise analysis”, Appl. Opt., 36 (1997), 75–92 | DOI | DOI

[152] D. Boas, D. Brooks, E. Miller, C. DiMarzio, M. Kilmer, R. Gaudette, “Imaging the body with diffuse optical tomography”, IEEE Signal Process. Mag., 18:6 (2001), 57–75 | DOI | DOI

[153] D. Boas, G. Strangman, J. Culver, R. Hoge, G. Jasdzewski, R. Poldrack, B. Rosen, J. Mandeville, Can the cerebral metabolic rate of oxygen be estimated with near-infrared spectroscopy?, Phys. Med. Biol., 48:15 (2003), 2405–2418 | DOI | DOI

[154] D. Boas, A. Dale, M. Franceschini, “Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy”, NeuroImage, 23 (2004), 275–288 | DOI | DOI

[155] R. Hoge, S. Diamond, M. Franceschini, D. Boas, “A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans”, NeuroImage, 29:2 (2006), 368–382 | DOI | DOI

[156] D. Boas, B. Chance, A. Yodh, “Experimental images of heterogeneous turbid media by frequency-domain diffusing-photon tomography”, Opt. Lett., 20:5 (1995), 426–428 | DOI | DOI

[157] A. Siegel, J. Culver, J. Mandeville, D. Boas, “Temporal comparison of functional brain imaging with diffuse optical tomography and fMRI during rat forepaw stimulation”, Phys. Med. Biol., 48:10 (2003), 1391–1403 | DOI | DOI

[158] J. H. Theodore, S. G. Diamond, M. A. Franceschini, D. A. Boas, “Homer: a review of timeseries analysis methods for near-infrared spectroscopy of the brain”, Applied Optics, 48:10 (2009), D280–D298 | DOI | DOI

[159] HOMER2 Download Page, (data obrascheniya: 28.02.2018) http://optics.martinos.org/research/software/

[160] Z. Haihong, G. Cuntai, “A Kernel-based signal localization method for NIRS Braincomputer Interfaces”, 18th International Conference on Pattern Recognition, ICPR'06, v. 1, 2006, 1158–1161

[161] J. Sun, “Tail probabilities of the maxima of Gaussian random fields”, Ann. Probab., 21:1 (1993), 34–71 | DOI | MR | Zbl | DOI | MR | Zbl

[162] J. Sun, C. Loader, “Simultaneous confidence bands for linear regression and smoothing”, Ann. Stat., 22:3 (1994), 1328–1345 | DOI | MR | Zbl | DOI | MR | Zbl

[163] X. Cui, S. Bray, A. L. Reiss, “Speeded Near Infrared Spectroscopy (NIRS) response detection”, PloS ONE, 5:11 (2010) | DOI | DOI

[164] M. A. Franceschini, S. Fantini, V. Toronov, M. E. Filiaci, E. Gratton, “Cerebral hemodynamics measured by Near-Infrared Spectroscopy at rest and during motor activation”, Proceedings of the Optical Society of America In Vivo Optical Imaging Workshop, 2000, 73–80

[165] X. Cui, S. Bray, D. M. Bryant, G. H. Glover, A. L. Reiss, “A quantitative comparison of NIRS and fMRI across multiple cognitive tasks”, NeuroImage, 54:4 (2011), 2808–2821 | DOI | DOI

[166] A. V. Medvedev, S. V. Borisov, A. H. Gandjbakhche, J. VanMeter, ““Seeing” electroencephalogram through the skull: imaging prefrontal cortex with fast optical signal”, Biomedical Optics, 15:6 (2010) | DOI | Zbl | DOI | Zbl

[167] J. Huang, S. Wang, S. I Jia, D. Mo, H. C. Chen, “Cortical dynamics of semantic processing during sentence comprehension: evidence from event-related optical signals”, Plos ONE, 8:8 (2013)

[168] A. V. Medvedev, J. M. Kainerstorfer, S. V. Borisov, J. VanMetera, “Functional connectivity in the prefrontal cortex measured by near-infrared spectroscopy during ultrarapid object recognition”, Journal of Biomedical Optics, 16:1 (2011) | DOI | Zbl | DOI | Zbl

[169] A. M. Chiarelli, G. L. Romani, A. Merla, “Fast optical signals in the sensorimotor cortex: General Linear Convolution Model applied to multiple source-detector distance-based data”, NeuroImage, 85 (2014), 245–254 | DOI | DOI

[170] N. D. Tam, G. Zouridakis, “Temporal decoupling of oxy- and deoxy-hemoglobin hemodynamic responses detected by functional near-infrared spectroscopy (fNIRS)”, Journal of Biomedical Engineering and Medical Imaging, 1:2 (2014), 18–28 | DOI | DOI

[171] F. Zhang, A. M. Aravanis, A. Adamantidis, L. de Lecea, K. Deisseroth, “Circuit-breakers: optical technologies for probing neural signals and systems”, Nature Reviews Neuroscience, 8 (2007), 577–581 | DOI | DOI

[172] P. Kim, M. Puoris'haag, D. Cote, C. P. Lin, S. H. Yun, “In vivo confocal and multiphoton microendoscopy”, J. of Biomedical Optics, 13:1 (2008) | DOI | DOI

[173] D. Kobat, M. E. Durst, N. Nishimura, A. W. Wong, C. B. Schaffer, C. Xu, “Deep tissue multiphoton microscopy using longer wavelength excitation”, Optics Express, 17:16 (2009), 11 | DOI | DOI

[174] S. K. Mohanty, R. K. Reinscheid, X. Liu, N. Okamura, T. B. Krasieva, M. W. Berns, “In-Depth Activation of Channelrhodopsin-2 Sensitized Excitable Cells with High Spatial Resolution Using Two-Photon Excitation with a Near-Infrared Laser Microbeam”, Biophysical Journal, 95 (2008), 11 | DOI | DOI

[175] C. L. Ebbesen, H. Bruus, “Analysis of laser-induced heating in optical neuronal guidance”, Journal of Neuroscience Methods, 209 (2012), 168–177 | DOI | DOI

[176] A. Ehrlicher, T. Betz, B. Stuhrmann, D. Koch, V. Milner, M. G. Raizen, J. Kas, “Guiding neuronal growth with light”, PNAS, 2002 | DOI | DOI

[177] I. V. Ilina, A. V. Ovchinnikov, D. S. Sitnikov, O. V. Chefonov, M. B. Agranat, Yu. V. Khramova, M. L. Semenova, “Microsurgery of cell membrane with femtosecond laser pulses for cell fusion and optical injection”, Advanced Laser Technologies, 2012 | DOI | DOI

[178] N. Birbaumer, G. Gallegos-Ayala, M. Wildgruber, S. Silvoni, S. R. Soekadar, “Direct brain control and communication in paralysis”, Brain Topography, 27:1 (2014), 4–11 | DOI | DOI