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Wireless Personal Communications

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28.02.2023

Demystifying Cognitive Informatics and its Applications in Brain-Computer Interface

verfasst von: Prakash Chandra Sharma, Rohit Raja, Santosh Kumar Vishwakarma, Venkatesh Gauri Shankar

Erschienen in: Wireless Personal Communications | Ausgabe 2/2023

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Abstract

Cognitive informatics is a transdisciplinary inquiry of data sciences, intellectual science, and knowledge science that explores the inward data preparing systems. It also explores the procedures of the brain and normal insight, just as their building applications in intellectual registering. Cognitive processing is a developing worldview of clever figuring strategies and frameworks dependent on intellectual informatics that executes computational insight through self-sufficient inductions. This article presents a lot of aggregate points of view on Cognitive informatics and intellectual figuring, just as their applications in unique insight, computational insight, computational phonetics, information portrayal, cooperative processing, granular registering, semantic registering, AI, and social processing. In this assessment paper, an endeavor has been made towards decoding an intellectual movement (conscious eye squint) of human subjects caught utilizing electroencephalography (EEG) energetically. A noteworthy ascent in occasion associated prospective remains seen through frontal flaps of the cerebral cortex. The created ideal has remained conveyed in Arduino utilizing Simulink towards control yield gadgets freely. The outcomes exhibit the achievability of the Cognitive mechanism system towards wards interpreting purposeful goals into orders through EEG centered BCI for the rest towards the ration of truly tested patients.

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Abraham, A., & Windmann, S. (2007). Creative cognition: The diverse operations and the prospect of applying a cognitive neuroscience perspective. Methods, 42(1), 38–48. https://doi.org/10.1016/j.ymeth.2006.12.007CrossRef

Sahu, A., Sharma, S., & Raja, R. (2022). Deep learning-based continuous authentication for an IoT-enabled healthcare service. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2022CrossRef

Patel, V. L., Arocha, J. F., Diermeier, M., Greenes, R. A., & Shortliffe, E. H. (2001). Methods of cognitive analysis towards support the design and evaluation of biomedical systems: The case of clinical practice guidelines. Journal of Biomedical Informatics, 34(1), 52–66. https://doi.org/10.1006/jbin.2001.1002CrossRef

Patel, V. L., Arocha, J. F., Diermeier, M., How, J. J., & Mottur-Pilson, C. (2001). Cognitive psychological studies of representation and use of clinical practice guidelines. International Journal of Medical Informatics, 63(3), 147–167. https://doi.org/10.1016/S1386-5056(01)00165-4CrossRef

Patel, V. L., Yoskowitz, N. A., Arocha, J. F., & Shortliffe, E. H. (2009). Cognitive and learning sciences in biomedical and health instructional design: A review with lessons for biomedical informatics education. Journal of Biomedical Informatics, 42(1), 176–197. https://doi.org/10.1016/j.jbi.2008.12.002CrossRef

Aflalo, T., Kellis, S., Klaes, C., Lee, B., Shi, Y., Pejsa, K., Shanfield, K., Hayes-Jackson, S., Aisen, M., Heck, C., Liu, C., & Andersen, R. A. (2015). Decoding towards r imagery from the posterior parietal cortex of a tetraplegic human. Science, 348(6237), 906–910. https://doi.org/10.1126/science.aaa5417CrossRef

Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., McMorland, A. J. C., Velliste, M., Boninger, M. L., & Schwartz, A. B. (2013). High-performance neuroprosthetic control by an individual with tetraplegia. The Lancet, 381(9866), 557–564. https://doi.org/10.1016/S0140-6736(12)61816-9CrossRef

Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., Van Der Smagt, P., & Donoghue, J. P. (2012). Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 485(7398), 372–375. https://doi.org/10.1038/nature11076CrossRef

King, C. E., Wang, P. T., McCrimmon, C. M., Chou, C. C., Do, A. H., & Nenadic, Z. (2015). The feasibility of a brain-computer interfaces functional electrical stimulation system for the rest towards the ration of overground walking after paraplegia. Journal of NeuroEngineering and Rehabilitation. https://doi.org/10.1186/s12984-015-0068-7CrossRef

10.

Raja, R., Gupta, D., Varshney, N., & Kumar, A. (2022). Detection of behavioural patterns employing a hybrid approach of computational techniques. Computers, Materials and Continua, 72(1), 2015–2031.CrossRef

11.

Hundia, R. (2015). Brain-computer interface-controlling devices utilizing the alpha brain waves. Undefined.

12.

Abdulkader, S. N., Atia, A., & Mostafa, M. S. M. (2015). Brain-computer interfacing: Applications and challenges. Egyptian Informatics Journal, 16(2), 213–230. https://doi.org/10.1016/j.eij.2015.06.002CrossRef

13.

Bell, M. A., & Cuevas, K. (2012). Using EEG towards study cognitive development: issues and practices. Journal of Cognition and Development, 13(3), 281–294. https://doi.org/10.1080/15248372.2012.691143CrossRef

14.

Nelson C. (2007). Methods for acquiring and analyzing infant event-related potentials. Retrieved August 15, 2020, from https://www.researchgate.net/publication/284222402_Methods_for_acquiring_and_analysing_infant_event-related_potentials

15.

Jatupaiboon, N., Pan-Ngum, S., & Israsena, P. (2013). Real-time EEG-based happiness detection system. The Scientific World Journal. https://doi.org/10.1155/2013/618649CrossRef

16.

Karimifard, S., & Ahmadian, A. (2007). Morphological heart arrhythmia classification using the Hermitian model of higher-order statistics. Annual International Conference of the IEEE Engineering in Medicine and Biology—Proceedings, 3132–3135. https://doi.org/10.1109/IEMBS.2007.4352993

17.

Martis, R. J., Acharya, U. R., Mandana, K. M., Ray, A. K., & Chakraborty, C. (2013). Cardiac decision making using higher-order spectra. Biomedical Signal Processing and Control, 8(2), 193–203. https://doi.org/10.1016/j.bspc.2012.08.004CrossRef

18.

Mahajan R. (2007). A real-time set up for retrieval of Emotional states from human neural responses. https://publications.waset.org/9997644/a-real-time-set-up-for-retrieval-of-emotional-states-from-human-neural-responses

19.

Sharma, P. C., Raja, R., Vishwakarma, S. K., Sharma, S., Mishra, P. K., & Kushwah, V. S. (2022). Analysis of brain signal processing and real-time EEG signal enhancement. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-12887-zCrossRef

20.

Lelievre, Y., Washizawa, Y., & Rutkowski, T. M. (2013). Single-trial BCI classification accuracy improvement for the novel virtual sound movement-based spatial audio towards ry paradigm. 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013. https://doi.org/10.1109/APSIPA.2013.6694317

21.

Wang, Q., Wang, H., Zhao, C., & Yang, J. (2011). Driver fatigue detection technology in active safety systems. 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011—Proceedings, 3097–3100. https://doi.org/10.1109/RSETE.2011.5964969.

22.

Wang, W., Degenhart, A. D., Sudre, G. P., Pomerleau, D. A., & Tyler-Kabara, E. C. (2011). Decoding semantic information from human electrocorticographic (ECoG) signals. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2011, 6294–6298. https://doi.org/10.1109/IEMBS.2011.6091553CrossRef

23.

Brumberg, J. S., Nietowards Castanon, A., Kennedy, P. R., & Guenther, F. H. (2010). Brain-computer interfaces for speech communication. Speech Communication, 52(4), 367–379. https://doi.org/10.1016/j.specom.2010.01.001CrossRef

24.

Van De Laar, B., Gürkök, H., Plass-Oude Bos, D., Poel, M., & Nijholt, A. (2013). Experiencing BCI control in a popular computer game. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 176–184. https://doi.org/10.1109/TCIAIG.2013.2253778CrossRef

25.

Vourvopoulos, A., & Liarokapis, F. (2012). Robot navigation using brain-computer interfaces. Proc. of the 11th IEEE Int. Conference on Trust, Security, and Privacy in Computing and Communications, TrustCom-2012—11th IEEE Int. Conference on Ubiquitowards us Computing and Communications, IUCC-2012, 1785–1792. https://doi.org/10.1109/TrustCom.2012.247

26.

Prataksita, N., Lin, Y. T., Chou, H. C., & Kuo, C. H. (2014). Brain-robot control interface: Development and application. 2014 IEEE International Symposium on Bioelectronics and Bioinformatics, IEEE ISBB 2014. https://doi.org/10.1109/ISBB.2014.6820928

27.

Brouwer, A.-M., van Erp, J., Heylen, D., Jensen, O., & Poel, M. (2013). Effortless Passive BCIs for Healthy Users. In LNCS (Vol. 8009, pp. 615–622). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39188-0_66

28.

Garcia-Molina, G., Tsoneva, T., & Nijholt, A. (2013). Emotional brain-computer interfaces. International Journal of Autowards enormous and Adaptive Communications Systems, 6(1), 9–25. https://doi.org/10.1504/IJAACS.2013.050687CrossRef

29.

Tan, D., & Nijholt, A. (2010). Brain-Computer Interfaces and Human-Computer Interaction (pp. 3–19). Springer. https://doi.org/10.1007/978-1-84996-272-8_1CrossRef

30.

Fukushima, M., Inoue, A., & Niwa, T. (2010). Emotional evaluation of TV-CM using the fractal dimension and the largest Lyapunov exponent. Conference Proceedings—IEEE International Conference on Systems, Man and Cybernetics, pp. 1473–1476. https://doi.org/10.1109/ICSMC.2010.5642336

31.

Sorudeykin, K. A. (2010). An Educative Brain-Computer Interface. http://arxiv.org/abs/1003.2660

32.

Venthur, B., Blankertz, B., Gugler, M. F., & Curio, G. (2010). Novel applications of BCI technology: Psychophysiological optimization of working conditions in the industry. Conference Proceedings—IEEE International Conference on Systems, Man and Cybernetics, pp. 417–421. https://doi.org/10.1109/ICSMC.2010.5641772.

33.

Hanafiah, Z. M., Taib, M. N., & Hamid, N. H. A. (2010). EEG pattern of smokers for Theta, Alpha, and Beta Band frequencies. Proceeding, 2010 IEEE Student Conference on Research and Development-Engineering: Innovation and Beyond, SCOReD 2010, pp. 320–323. https://doi.org/10.1109/SCORED.2010.5704025

34.

Padma Shri, T. K., & Sriraam, N. (2012). EEG based detection of alcoholics using spectral entropy with neural network classifiers. 2012 International Conference on Biomedical Engineering, ICoBE 2012, 89–93. https://doi.org/10.1109/ICoBE.2012.6178961

35.

Shooshtari, M. A., & Setarehdan, S. K. (2010). Selection of optimal EEG channels for classification of signals correlated with alcohol abusers. International Conference on Signal Processing Proceedings, ICSP, 1745–1748. https://doi.org/10.1109/ICOSP.2010.5656482

36.

Fan, X., Bi, L., & Wang, Z. (2012). Detecting emergencies by monitor ring drivers’ states from EEG. 2012 ICME International Conference on Complex Medical Engineering, CME 2012 Proceedings, pp. 245–248. https://doi.org/10.1109/ICCME.2012.6275717

37.

Lin, C. T., Tsai, S. F., & Ko, L. W. (2013). EEG-based learning system for online motion sickness level estimation in a dynamic vehicle environment. IEEE Transactions on Neural Networks and Learning Systems, 24(10), 1689–1700. https://doi.org/10.1109/TNNLS.2013.2275003CrossRef

38.

Wei, C. S., Chuang, S. W., Wang, W. R., Ko, L. W., Jung, T. P., & Lin, C. T. (2011). Implementation of a motion sickness evaluation system based on EEG spectrum analysis. Proceedings—IEEE International Symposium on Circuits and Systems, 1081–1084. https://doi.org/10.1109/ISCAS.2011.5937757

39.

Paulraj, M. P., Bin Yaccob, S., Bin Adom, A. H., Hema, C. R., & Subramaniam, K. (2012). EEG based hearing perception level estimation for normal-hearing persons. Proceedings of 2012 IEEE Conference on Control, Systems and Industrial Informatics, ICCSII 2012, 160–162. https://doi.org/10.1109/CCSII.2012.6470493

40.

Ko, L. W., Lee, H. C., Tsai, S. F., Shih, T. C., Chuang, Y. T., Huang, H. L., Ho, S. Y., & Lin, C. T. (2013). EEG-based motion sickness classification system with genetic feature selection. Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013–2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, 158–164. https://doi.org/10.1109/CCMB.2013.6609180

41.

Bagchi, S., & Chattowards Upadhyay, M. (2012). An easy-towards -adopt the approach for regular and routine monitor ring of the consciousness level of the human brain of the stayed alone sick person. Proceedings of the International Conference on Sensing Technology, ICST, pp. 698–703. https://doi.org/10.1109/ICSensT.2012.6461767.

42.

Selvam, V. S., & Shenbagadevi, S. (2011). Brain tumor detection using scalp EEG with modified Wavelet-ICA and multi-layer feed-forward neural network. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 6104–6109. https://doi.org/10.1109/IEMBS.2011.6091508

43.

Sharanreddy, P. K. K. (2013). Detection of primary brain tumor present in EEG signal using wavelet transform and neural network.

44.

Poulos, M., Felekis, T., & Evangelou, A. (2012). Is it possible to extract a fingerprint for early breast cancer via EEG analysis? Medical Hypotheses, 78(6), 711–716. https://doi.org/10.1016/j.mehy.2012.02.016CrossRef

45.

Sharanreddy, M., & Kulkarni, P. K. (2013). Autowards mated EEG signal analysis for the identification of epilepsy seizures and brain tumors. Journal of Medical Engineering and Technology, 37(8), 511–519. https://doi.org/10.3109/03091902.2013.837530CrossRef

46.

Helini Kulasuriya, K. A., & Perera, M. U. S. (2011). Forecasting epileptic seizures using EEG signals, wavelet transform, and artificial neural networks. ITME 2011—Proceedings: 2011 IEEE International Symposium on IT in Medicine and Education, 1, 557–562. https://doi.org/10.1109/ITiME.2011.6130899

47.

Liang, S. F., Shaw, F. Z., Young, C. P., Chang, D. W., & Liao, Y. C. (2010). A closed-loop brain-computer interface for real-time seizure detection and control. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10, 4950–4953. https://doi.org/10.1109/IEMBS.2010.5627243

48.

Fadzal, C. W. N. F. C. W., Mansor, W., & Khuan, L. Y. (2011). Review of brain-computer interface application in diagnosing dyslexia. Proceedings—2011 IEEE Control and System Graduate Research Colloquium, ICSGRC 2011, pp. 124–128. https://doi.org/10.1109/ICSGRC.2011.5991843

49.

Hansen, I. H., Marcussen, M., Christensen, J. A. E., Jennum, P., & Sorensen, H. B. D. (2013). Detection of a sleep disorder predicting Parkinson’s disease. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 5793–5796. https://doi.org/10.1109/EMBC.2013.6610868

50.

Koch, H., Christensen, J. A. E., Frandsen, R., Arvastson, L., Christensen, S. R., Sorensen, H. B. D., & Jennum, P. (2013). Classification of iRBD and Parkinson’s patients using a general data-driven sleep staging model built on EEG. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 4275–4278. https://doi.org/10.1109/EMBC.2013.6610490

51.

Wei, L., Hong, Q., Yue, H., & Xu, C. (2010). The research in a plantar pressure measuring system is connected with EEG. International Conference on Signal Processing Proceedings, ICSP, 434–437. https://doi.org/10.1109/ICOSP.2010.5655374

52.

Siang Ang, C., Sakel, M., Pepper, M., & Phillips, M. (2011). Use of brain-computer interfaces in neurological rehabilitation. British Journal of Neuroscience Nursing, 7(3), 523–528. https://doi.org/10.12968/bjnn.2011.7.3.523CrossRef

53.

Tan, H. G., Kong, K. H., Shee, C. Y., Wang, C. C., Guan, C. T., & Ang, W. T. (2010). Post-acute stroke patients use a brain-computer interface to activate electrical stimulation. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10, 4234–4237. https://doi.org/10.1109/IEMBS.2010.5627381

54.

Birbaumer, N., Ruiz, S., & Sitaram, R. (2013). Learned the regulation of brain metabolism. Trends in Cognitive Sciences, 17(6), 295–302. https://doi.org/10.1016/j.tics.2013.04.009CrossRef

55.

Ruiz, S., Buyukturkoglu, K., Rana, M., Birbaumer, N., & Sitaram, R. (2014). Real-time fMRI brain-computer interfaces: Self-regulation of single brain regions towards networks. Biological Psychology, 95(1), 4–20. https://doi.org/10.1016/j.biopsycho.2013.04.010CrossRef

56.

Barbosa, A. O. G., Achanccaray, D. R., & Meggiolaro, M. A. (2010). Activation of a mobile robot through a brain-computer interface. Proceedings—IEEE International Conference on Robotics and Autowards mation, pp. 4815–4821. https://doi.org/10.1109/ROBOT.2010.5509150

57.

Ang, K. K., Guan, C., Chua, K. S. G., Ang, B. T., Kuah, C., Wang, C., Phua, K. S., Zheng Chin, Y., & Zhang, H. (2010). Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10, pp. 5549–5552. https://doi.org/10.1109/IEMBS.2010.5626782

58.

King, C. E., Wang, P. T., Mizuta, M., Reinkensmeyer, D. J., Do, A. H., Moromugi, S., & Nenadic, Z. (2011). Noninvasive brain-computer interface driven hand orthosis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2011, 5786–5789. https://doi.org/10.1109/IEMBS.2011.6091432CrossRef

59.

Meyer, T., Peters, J., Brtz, D., Zander, T. O., Scholkopf, B., Soekadar, S. R., & Grosse-Wentrup, M. (2012). A brain-robot interface for studying motor learning after stroke. IEEE International Conference on Intelligent Robots and Systems, pp. 4078–4083. https://doi.org/10.1109/IROS.2012.6385646

60.

Presacco, A., Forrester, L., & Contreras-Vidal, J. L. (2011). Towards wards a non-invasive brain-machine interface system towards rest towards re gait function in humans. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2011, 4588–4591. https://doi.org/10.1109/IEMBS.2011.6091136CrossRef

61.

Contreras-Vidal, J. L., Presacco, A., Agashe, H., & Paek, A. (2012). Restowards ration of whole-body movement: Towards ward a noninvasive brain-machine interface system. IEEE Pulse, 3(1), 34–37. https://doi.org/10.1109/MPUL.2011.2175635CrossRef

62.

Regenbrecht, H., Hoermann, S., Ott, C., Muller, L., & Franz, E. (2014). Manipulating the experience of reality for rehabilitation applications. Proceedings of the IEEE, 102(2), 170–184. https://doi.org/10.1109/JPROC.2013.2294178CrossRef

63.

Benaim, C., Pérennou, D. A., Villy, J., Rousseaux, M., Pelissier, J. Y., Liepert, J., Breitenstein, C., Wang, Y. T., Kent, R. D., Duffy, J. R., Thomas, J. E., Tjaden, K., Watling, E., Lifshitz-Ben-Basat, A., Mashal, N., Monti, A., Ferrucci, R., Fumagalli, M., Mameli, F., Black, N., et al. (2016). Neurophysiological substrates of stroke patients with motor imagery-based brain-computer interface training. Occupational Therapy International, 7(1), 310–320. https://doi.org/10.1002/oti.1422CrossRef

64.

Tung, S. W., Guan, C., Ang, K. K., Phua, K. S., Wang, C., Zhao, L., Teo, W. P., & Chew, E. (2013). Motowards r imagery BCI for upper limb stroke rehabilitation: An evaluation of the EEG recordings using coherence analysis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2013, 261–264. https://doi.org/10.1109/EMBC.2013.6609487CrossRef

65.

Höller, Y., Bergmann, J., Kronbichler, M., Crone, J. S., Schmid, E. V., Thomschewski, A., Butz, K., Schütze, V., Höller, P., & Trinka, E. (2013). Real movement vs. motowards r imagery in healthy subjects. International Journal of Psychophysiology, 87(1), 35–41. https://doi.org/10.1016/j.ijpsycho.2012.10.015CrossRef

66.

Kang, H., Park, W., Kang, J. H., Kwon, G. H., Kim, S. P., & Kim, L. (2012). A neural analysis of motor imagery and passive movement using a haptic device (pp. 1536–1541). https://hanyang.elsevierpure.com/en/publications/a-neural-analysis-on-motowardsr-imagery-and-passive-movement-using-a-h

67.

Domingo, M. C. (2012). An overview of the Internet of Things for people with disabilities. In Journal of Network and Computer Applications., 35(2), 584–596. https://doi.org/10.1016/j.jnca.2011.10.015CrossRef

68.

Lin, C. T., Lin, B. S., Lin, F. C., & Chang, C. J. (2014). Brain-computer interface-based smart living environment towards—adjustment control system in UPnP home networking. IEEE Systems Journal, 8(2), 363–370. https://doi.org/10.1109/JSYST.2012.2192756CrossRef

69.

Ou, C. Z., Lin, B. S., Chang, C. J., & Lin, C. T. (2012). Brain-computer interface-based smart environmental control system. Proceedings of the 2012 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2012, 281–284. https://doi.org/10.1109/IIH-MSP.2012.74

70.

Peng, H., Hu, B., Qi, Y., Zhao, Q., & Ratcliffe, M. (2011). An improved EEG de-noising approach in the electroencephalogram (EEG) for home care. 2011 5th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, PervasiveHealth 2011, 469–474. https://doi.org/10.4108/icst.pervasivehealth.2011.246021

71.

Roy, R. N., Bonnet, S., Charbonnier, S., & Campagne, A. (2013). Mental fatigue and working memory load estimation: Interaction and implications for EEG-based passive BCI. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2013, 6607–6610. https://doi.org/10.1109/EMBC.2013.6611070CrossRef

72.

Duru, D. G., Duru, A. D., Barkana, D. E., Sanli, O., & Ozkan, M. (2013). Assessment of the surgeon’s stress level and alertness using EEG during laparoscopic simple nephrectomy. International IEEE/EMBS Conference on Neural Engineering, NER, pp. 452–455. https://doi.org/10.1109/NER.2013.6695969

73.

Dong, Y., Hu, Z., Uchimura, K., & Murayama, N. (2011). Driver inattention monitor ring system for intelligent vehicles: A review. IEEE Transactions on Intelligent Transportation Systems, 12(2), 596–614. https://doi.org/10.1109/TITS.2010.2092770CrossRef

74.

Coetzer, R. C., & Hancke, G. P. (2009). Driver fatigue detection: A survey. IEEE AFRICAN Conference. https://doi.org/10.1109/AFRCON.2009.5308101CrossRef

75.

Dahal, N., Nandagopal, N., Nafalski, A., & Nedic, Z. (2011). Modeling of cognition using EEG: A review and a new approach. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 1045–1049. https://doi.org/10.1109/TENCON.2011.6129270

76.

Pritchett, S., Zilberg, E., Xu, Z. M., Karrar, M., Burtowards n, D., & Lal, S. (2011). Comparing the accuracy of two algorithms for detecting driver drowsiness Single source (EEG) and hybrid (EEG and body movement). IB2COM 2011—6th International Conference on Broadband Communications and Biomedical Applications, Program, pp. 179–184. https://doi.org/10.1109/IB2Com.2011.6217916

77.

Borghini, G., Vecchiato, G., Toppi, J., Astolfi, L., Maglione, A., Isabella, R., Caltagirone, C., Kong, W., Wei, D., Zhou, Z., Polidori, L., Vitiello, S., & Babiloni, F. (2012). Assessment of mental fatigue during car driving by using high-resolution EEG activity and neurophysiologic indices. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012, 6442–6445. https://doi.org/10.1109/EMBC.2012.6347469

78.

Lin, C. T., Wang, Y. K., & Chen, S. A. (2011). An EEG-based brain-computer interface for dual-task driving detection. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7062 LNCS(PART 1), 701–708. https://doi.org/10.1007/978-3-642-24955-6_83

79.

Shin, D., Kim, T., Kim, S., & Shin, D. (2011). Design and implementation of a smart driving system using a context recognition system. ISCI 2011–2011 IEEE Symposium on Computers and Informatics, pp. 84–89. https://doi.org/10.1109/ISCI.2011.5958889

80.

Murata, K., Fujita, E., Kojima, S., Maeda, S., Ogura, Y., Kamei, T., Tsuji, T., Kaneko, S., Yoshizumi, M., & Suzuki, N. (2011). Noninvasive biological sensor system for the detection of drunk driving. IEEE Transactions on Information Technology in Biomedicine, 15(1), 19–25. https://doi.org/10.1109/TITB.2010.2091646CrossRef

81.

Liang, J., Xu, G., Ge, J., Zhu, F., & Feng, X. (2010). Analysis of virtual simulation environment for driving behavior based on imaging movements. 2010 International Conference on Networking and Digital Society, ICNDS 2010, 1, 246–248. https://doi.org/10.1109/ICNDS.2010.5479148

82.

Lin, C. T., Chen, S. A., Ko, L. W., & Wang, Y. K. (2011). EEG-based brain dynamics of driving distraction. Proceedings of the International Joint Conference on Neural Networks, pp. 1497–1500. https://doi.org/10.1109/IJCNN.2011.6033401

83.

Kawamura, R., Bhuiyan, M. S., Kawanaka, H., & Oguri, K. (2011). Simultaneous stimuli of vibration and audio for in-vehicle driver activation. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, pp. 1710–1715. https://doi.org/10.1109/ITSC.2011.6082947

84.

Yoshioka, M., Inoue, T., & Ozawa, J. (2012). Brain signal pattern of engrossed subjects using near-infrared spectroscopy (NIRS) and its application towards TV commercial evaluation. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN.2012.6252752CrossRef

85.

Vecchiato, G., Babiloni, F., Asto lfi, L., Towards PPI, J., Cherubino, P., Dai, J., Kong, W., & Wei, D. (2011). Enhance of theta EEG spectral activity related to the memorization of commercial advertisings in Chinese and Italian subjects. Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011, 3, 1491–1494. https://doi.org/10.1109/BMEI.2011.6098615

86.

Johnstowardsn, S. J., Boehm, S. G., Healy, D., Goebel, R., & Linden, D. E. J. (2010). Neurofeedback: A promising towards ol for the self-regulation of emotion networks. NeuroImage, 49(1), 1066–1072. https://doi.org/10.1016/j.neuroimage.2009.07.056CrossRef

87.

Zotev, V., Phillips, R., Yuan, H., Misaki, M., & Bodurka, J. (2014). Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage, 85, 985–995. https://doi.org/10.1016/j.neuroimage.2013.04.126CrossRef

88.

Márquez, B. Y., Alanis, A., Lopez, M. Á., & Magdaleno-Palencia, J. S. (2012). Sport education-based technology: Stress measurement incompetence. 2012 International Conference on E-Learning and E-Technologies in Education, ICEEE 2012, 247–252. https://doi.org/10.1109/ICeLeTE.2012.6333422

89.

Royer, A. S., Doud, A. J., Rose, M. L., & He, B. (2010). EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(6), 581–589. https://doi.org/10.1109/TNSRE.2010.2077654CrossRef

90.

Bonnet, L., Lotte, F., & Lécuyer, A. (2013). Two brains, one game: Design and evaluation of a multiuser bci video game based on motor imagery. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 185–198. https://doi.org/10.1109/TCIAIG.2012.2237173CrossRef

91.

Raja, R., Sharma, P. C., & Mahmood, R. (2022). Analysis of anomaly detection in surveillance video: recent trends and future vision. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-13954-1CrossRef

92.

Svogor, I., & Kisasondi, T. (2012). Two-factor authentication using EEG augmented passwords. Proceedings of the International Conference on Information Technology Interfaces, ITI, 373–378. https://doi.org/10.2498/iti.2012.0441

93.

Karthikeyan, T., & Sabarigiri, B. (2011). Enhancement of Multi-Modal Biometric Authentication Based on IRIS and Brain NeuroImage Coding. Undefined.

94.

Revett, K., Deravi, F., & Sirlantzis, K. (2010). Biosignals for user authentication - Towards wards cognitive biometrics? Proceedings—EST 2010– 2010 International Conference on Emerging Security Technologies, ROBOSEC 2010—Robots and Security, LAB-RS 2010 - Learning and Adaptive Behavior in Robotic Systems, pp. 71–76. https://doi.org/10.1109/EST.2010.32

95.

Su, F., Zhou, H., Feng, Z., & Ma, J. (2012). A biometric-based covert warning system using EEG. Proceedings—2012 5th IAPR International Conference on Biometrics, ICB 2012, 342–347. https://doi.org/10.1109/ICB.2012.6199830

96.

Nakanishi, I., Baba, S., & Li, S. (2011). Evaluation of brain waves as biometrics for driver authentication using a simplified driving simulator. Proceedings—2011 International Conference on Biometrics and Kansei Engineering, ICBAKE 2011, 71–76. https://doi.org/10.1109/ICBAKE.2011.27

97.

Nakanishi, I., Baba, S., Ozaki, K., & Li, S. (2013). Using brain waves as transparent biometrics for on-demand driver authentication. International Journal of Biometrics, 5(3–4), 288–305. https://doi.org/10.1504/IJBM.2013.055965CrossRef

98.

Nakanishi, I., Ozaki, K., & Li, S. (2012). Evaluation of the brain wave as biometrics in a simulated driving environment. Undefined.

99.

Real-time acquisition and analysis of neural response for rehabilitative control—Sujit Roy. (2020). Retrieved August 15, 2020, from http://sujitroy.co.in/publications/real-time-acquisition-and-analysis-of-neural-response-for-rehabilitative-control/

100.

The study of brain activity during the observation of commercial advertising by using high-resolution EEG techniques—Italian Ministry of Health. (2020). Retrieved August 15, 2020, from https://moh-it.pure.elsevier.com/en/publications/the-study-of-brain-activity-during-the-observation-of-commercial

Titel: Demystifying Cognitive Informatics and its Applications in Brain-Computer Interface
verfasst von: Prakash Chandra Sharma
Rohit Raja
Santosh Kumar Vishwakarma
Venkatesh Gauri Shankar
Publikationsdatum: 28.02.2023
Verlag: Springer US
Erschienen in: Wireless Personal Communications / Ausgabe 2/2023
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-023-10192-y

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