Human Communication Technology. Группа авторов
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A review. J. Vis., 15, 1–46, 2015.
23. Palani Thanaraj, K. and Chitra, K., Multichannel feature extraction and classification of epileptic states using higher order statistics and complexity measures. Int. J. Eng. Technol., 6, 1, 102–109, 2014.
24. Picton, T.W., The P300 wave of the human event-related potential. J. Clin. Neurophysiol., 9, 4, 456–479, 1992.
25. Krishna, R.R., Kumar, P.S., Sudharsan, R.R., Optimization of wire-length and block rearrangements for a modern IC placement using evolutionary techniques. IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, pp. 1–4, 2017.
26. Mayaud, L. et al., A comparison of recording modalities of P300 event-related potentials (ERP) for brain-computer interface (BCI) paradigm. Neurophysiol. Clin., 43, 4, 217–227, 2013.
27. Nuwer, M.R., Dawson, E.G., Carlson, L.G., Kanim, L.E.A., Sherman, J.E., Somatosensory evoked potential spinal cord monitoring reduces neurologic deficits after scoliosis surgery: Results of a large multicenter survey. Electroencephalogr. Clin. Neurophysiol. Evoked Potentials, 96, 1, 6–11, 1995.
28. Turnip, A. and Hong, K.S., Classifying mental activities from EEG-P300 signals using adaptive neural networks. Int. J. Innov. Comput. Inf. Control, 8, 9, 6429–6443, 2012.
29. Sarma, P., Tripathi, P., Sarma, M.P., Sarma, K.K., Pre-processing and feature extraction techniques for EEGBCI applications—A review of recent research, ADBU. J. Eng. Technol., 5, 2348–7305, 2016.
30. Li, K., Sun, G., Zhang, B., Wu, S., Wu, G., Correlation between forehead EEG and sensorimotor area EEG in motor imagery task, in: Eighth IEEE Int. Symp. Dependable, Auton. Secur. Comput. DASC 2009, pp. 430–435, 2009.
31. Petrov, Y., Analysis of EEG signals for EEG-based brain-computer interface. PLoS One, 7, 10, e44439, 2012.
32. Adelmann, R., Langheinrich, M., Floerkemeier, C., A toolkit for bar code recognition and resolving on camera phones—Jump-starting the Internet of Things. In: Hochberger, C. and R. Liskowsky (Eds.), GI Jahrestagung. (2). LNI, GI, 94, 366–373, Informatik 2006, Dresden, Germany, 2006.
33. Arnsten, A.F.T., Berridge, C.W., McCracken, J.T., The neurobiological basis of attention-deficit/hyperactivity disorder. Prim. Psychiatry, 16, 47–54, 2009.
34. Baranyi, P. and Csapo, A., Definition and synergies of cognitive info communications. Acta Polytech. Hung., 9, 1, 67–83, 2012.
35. Baranyi, P., Csapo, A., Gyula, S., Cognitive info communications (CogInfoCom), p. 378, Springer, Heidelberg, 2015.
36. Benedek, A. and Molnar, G., Supporting them-learning based knowledge transfer in university education and corporate sector, in: Proceedings of the 10th international conference on mobile learning 2014, Madrid, Spain, pp. 339–343, 2014.
37. Brown, V.J. and Bowman, E.M., Rodent models of prefrontal cortical function. Trends Neurosci., 25, 340–343, 2002.
38. Cardinal, R.N., Parkinson, J.A., Hall, J., Everitt, B.J., Emotion and motivation: The role of the amygdala, ventral striatum, and prefrontal cortex. Neurosci. Biobehav. Rev., 26, 321–352, 2002.
39. Cauda, F., Cavanna, A.E., Dágata, F., Sacco, K., Duca, S., Geminiani, G.C., Functional connectivity and coactivation of the nucleus accumbens: A combined functional connectivity and structure-based meta-analysis. J. Cognit. Neurosci., 23, 2864–2877, 2011.
40. Chen, F., Jia, Y., Xi, N., Non-invasive EEG based mental state identification using nonlinear combination, in: 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2013, https://doi.org/10.1109/robio.2013.6739789.
41. Christian, F. et al., The Internet of Things, in: IoT 2008: First International Conference, Zurich, Switzerland, p. 4952, 378, 2008.
42. Dalley, J.W., Cardinal, R.N., Robbins, T.W., Prefrontal executive and cognitive functions in rodents: neural and neurochemical substrates. Neurosci. Biobehav. Rev., 28, 771–784, 2004.
43. Feenstra, M., Botterblom, M., Uum, J.V., Behavioral arousal and increased dopamine efflux after blockade of NMDA-receptors in the prefrontal cortex are dependent on activation of glutamatergic neurotransmission. Neuropharmacology, 42, 752–763, 2002.
44. Fortino, G., Agents meet the IoT: Toward ecosystems of networked smart objects. IEEE Syst. Man Cybern. Mag., 2, 43–47, 2016.
45. Freedman, M. and Oscar-Berman, M., Bilateral frontal lobe disease and selective delayed response deficits in humans. Behav. Neurosci., 100, 337–342, 1986.
46. Friedemann, M. and Christian, F., From the internet of computers to the internet of things, in: From active data management to event-based systems and more. Papers in Honor of Alejandro Buchmann on the Occasion of His 60th Birthday, vol. 6462, Sachs, K., Petrov, I., Guerrero, P. (Eds.), pp. 242–259, 2010.
47. Friganovic, K., Medved, M., Cifrek, M., Brain–computer interface based on steady-state visual evoked potentials, in: 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2016, https://doi.org/10.1109/mipro.
48. Gallo, D.A., Mcdonough, I.M., Scimeca, J., Dissociating source memory decisions in the prefrontal cortex: fMRI of diagnostic and disqualifying monitoring. J. Cognit. Neurosci., 22, 955–969, 2010.
49. Hakiri, A., Berthou, P., Gokhale, A., Abdellatif, S., Publish/subscribe-enabled software defined networking for efficient and scalable IoT communications. IEEE Commun. Mag., 53, 48–54, 2015.
50. Heidbreder, C.A. and Groenewegen, H.J., The medial prefrontal cortex in the rat: Evidence for a dorso-ventral distinction based upon functional and anatomical characteristics. Neurosci. Biobehav. Rev., 27, 555–579, 2003.
51. Horvath, I., Disruptive technologies in higher education, in: 2016 7th IEEE international conference on cognitive info communications, Wroclaw, Poland, pp. 347–352, 2016.
52. Horvath, I. and Kvasznicza, Z., Innovative engineering training—Today’s answer to the challenges of the future, in: 2016 International Education Conference, Venice, Italy, pp. 647-1–647-7, 2016.
53. Horvath, I., Innovative engineering education in the cooperative R environment, in: 2016 7th IEEE International Conference on Cognitive Info Communications (CogInfoCom), Wroclaw, Poland, pp. 359–364, 2016, https://doi.org/10.1109/CogInfoCom.2016.7804576.
54. Horvath, I., Digital life gap between students and lecturers, in: 2016 7th IEEE International Conference on Cognitive Info Communications (CogInfoCom), Wroclaw, Poland, pp. 353–358, 2016, https://doi.org/10.1109/CogInfoCom.2016.7804575.
55. Kalaivani, M., Kalaivani, V., Devi, V.A., Analysis of EEG signal for the detection of brain abnormalities. Int. J. Comput. Appl., 1, 2, 1–6, 2014.
56. Cárdenas-Barrera, J.L., Lorenzo-Ginori, J.V., Rodríguez-Valdivia, E., A wavelet-packets based algorithm for EEG signal compression. Inform. Health Soc. Care, 29, 1, 15–27, 2004.
57. Kameswara, T., Rajyalakshmi, M., Prasad, T.V., An exploration on brain computer interface and its recent trends. Int. J. Adv. Res. Artif. Intell., 1, 8, 17–22, 2013.
58. Motamedi-Fakhr, S., Moshrefi-Torbati, M., Hill, M., Hill, C.M., White, P.R., Signal processing techniques applied to human sleep EEG signals—A review. Biomed. Signal Process. Control, 10, 1, 21–33, 2014.
59.