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Fizyolojik Sinyallerin Askerî Amaçlı Kullanılabilirliği: Elektroensefalografi ve Yakın Kızılaltı Spektroskopisi Örnekleri

Year 2018, , 895 - 900, 01.12.2018
https://doi.org/10.2339/politeknik.385474

Abstract

İnsan beynindeki nöronlar vücut
fonksiyonlarıyla ilişkili olarak elektriksel aktivite üretirler. Oluşan
potansiyel dağılımı, saçlı deri üzerine yerleştirilen elektrotlar ile
ölçülebilmektedir (elektroansefalografi, EEG). EEG sinyalleri; spontan,  olaya ilişkin veya uyarılmış potansiyel
kayıtları ile ilişkili olabilmekte ve tetikte olma, dinlenme veya uyku
durumlarında değişmektedirler. µ ve P300 dalgalarının işlenmesiyle dış dünya
ile iletişim kurulabilmekte ve bilgisayar ya da başka bir cihazın kontrolü yapılabilmektedir.  Bu sistemler insan beyin ara yüzleri ya da
etkileşimleri (BCI) olarak geniş uygulama alanı bulmaktadır. Beyindeki
oksijenlenme fonksiyonel kızılaltı spektroskopisiyle (fNIRS)
gözlemlenebilmektedir. Bu sistemlerin kullanımıyla yüksek dikkat gerektiren
görevlerde çalışan personelin beyin fonksiyonlarının takibi mümkün
olabilmektedir. Bu çalışmada EEG ve fNIRS’in askerî amaçlı olarak
kullanılabileceği önerilmektedir. Baskı altında hızlı ve doğru karar vermek
zorunda kalınan ve bilinç kaybının yaşanabildiği jet pilotluğu gibi görevlerde,
hassas bölge ya da cihazlar için takip ve kontrol görevi yapan personelin
durumları, bir merkez tarafından izlenerek gerektiğinde ikaz edilebilmeleri,
görevin tam olarak yapılmasını destekleyecektir. Ayrıca EEG sinyallerinin,
savaş sonrasında gazilerin beyin travmalarının incelenmesinde, beyin
fonksiyonları normal olan ancak konuşma ve hareket zorluğu çekenlerin
yaşamlarını kolaylaştırmak için iletişim ve kontrol için kullanılması
mümkündür. 

References

  • [1] Rosenberg, W.V., Chanwimalueang, T., Goverdovsky, V., Looney, D., Sharp,D., and Mandic, D.P., “Smart Helmet: Wearable Multichannel ECG and EEG”, IEEE Journal of Translational Engineering in Health and Medicine, 4, (2016).
  • [2] Fisher, J.A.N., Huang, S., Ye, M., Nabili, M., Wilent, W.B., Krauthamer, V., Myers, M.R. and Welle, C.G., “Real-Time Detection And Monitoring Of Acute Brain Injury Utilizing Evoked Electroencephalographic Potentials”, IEEE Transactions On Neural Systems And Rehabilitation Engineering, 24(9): (2016).
  • [3] Scholl, C.A., Chi, Y.M., Elconin, M., Gray, W.R., Chevillet, M.A., and Pohlmeyer, E.A., “Classification of Pilot-Induced Oscillations during In-Flight Piloting Exercises Using Dry EEG Sensor Recordings”, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2016).
  • [4] Egan, B.F., Mizutani, T., Thurlow, A., “Data Fusion for Wearable Physiological Sensor Platforms”, Information Fusion, 2005 8th International Conference, 2: 1412-1419, (2005).
  • [5] Guizzo, E., “Smart Cars That Can Tell When You're Bored To Death”, Albuquerque, Sandia National Laboratories, IEEE Spectrum, (2007).
  • [6] Belenky, G., Sing, H.C., Thomas, M.L., Shaham, Y., Balwinski, S., Redmond, D.P., Balkin, T.J., “Discrimination of rested from sleep-deprived EEG in awake normal humans by artificial neural network”, Proc. IEEE Int Conf Neural Networks, IEEE Piscataway, NJ, 3521–3524, (1994).
  • [7] Lennox-Buchthal, M., Buchthal, F., Rosenfalck, P., “Correlation of Electroencephalographic Findings with Crash Rate of Military Jet Pilots“ Epilepsia, 1(1-5): 366–372, (1959).
  • [8] Yarman-Vural, F., Onaral, B., Çetin, E., “Enhanced Parametric Estimation of Electroencephalograms Under Acceleration Stress”, Conference of the IEEE Engineering in Medicine and Biology Society, 12(2): 837-839, (1990).
  • [9] Wilson, G.F., Purvis, B., Skelly, J., Fullenkamp, P., Davis, I., “Physiological data used to measure pilot workload in actual flight and simulator conditions”, Proceedings of the Human Factors Society, 779-783, (1987).
  • [10] Sterman, B., Schummer, G., Dushenko, T., Smith, J., “Electroencephalographic correlates of pilot performance: simulation and flight studies” Jesse, K. (editor) Electrical and Magnetic Activity of the Central Nervous System: Research and Clinical Applications in Aerospace Medicine AGARD, CP 432, (1987).
  • [11] Izzetoglu, K., Bunce, S., Izzetoglu, M., Onaral, B., Pourrezaei, K., “fNIR spectroscopy as a measure of cognitive task load”, Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International, Conference of the IEEE 4, 3431- 3434 (2003).
  • [12] Thatcher, R.W., North, D.M., Curtin, R.T., Walker, R.A., Biver, C.J., Gomez, J.F., Salazar, A.M. “An EEG Severity Index of Traumatic Brain Injury” J Neuropsychiatry Clin Neurosci, 13(1): 77-87, (2001).
  • [13] Stefan, A., “America’s Army Game: Its (Virtual) Reality Representation and Cocaine”, Proceedings of the International Conference on Cyberworlds (CW’04), (2004).
  • [14] Berka, C., Levendowski, D.J. Cvetinovic, M., Petrovic, M., Miroslav, M., et al. “Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset”, International Journal of Human- Computer Interaction, Norwood, 17(2): 151, (2004).
  • [15] Usakli, A.B. Gencer, N.G. “USB-Based 256-Channel Electroencephalographic Data Acquisition System for Electrical Source Imaging of the Human Brain”, Instrumentation Science & Technology, 35(3): 255-273, (2007).
  • [16] Uşaklı, A.B., Gurkan, S., Aloise, F., Vecchiato, G., Babiloni, F., “A hybrid platform based on EOG and EEG signals to restore communication for patients afflicted by progressive motor neurons diseases”, Engineering The Future of the Biomedicine IEEE EMBC 2009 Conference, Minneapolis USA, 543 - 546 DOI 10.1109/IEMBS.2009.5333742, (2009).

Usability of Physiological Signals for Military Purpose: Examples of Electroencephalography and Near Infrared Spectroscopy

Year 2018, , 895 - 900, 01.12.2018
https://doi.org/10.2339/politeknik.385474

Abstract

The neurons of the human brain generate electrical
activities related to body functions. The generated potential distribution can
be measured with elctrodes placed on the scalp (electroencephalography, EEG).
EEG signals can be related to sponteneous, event related or evoked potential
recordings. The EEG pattern change in stiuations of alertness, resting or
sleep. It is possible to communicate with the environment and control a
computer or another device, by using µ and P300 waves. These systems are called
brain computer interfaces or interactions, and widely used. Oxygenation in the
brain can be observed with functional near-inrared spectroscopy (fNIRS). By
using these systems, brain functions of people whose work require attention,
can be monitored.  In this study, EEG and
fNIRS are proposed for some military purpose. A person whose task requires high
alertness can be monitored using their brain functions. Observance of a person
by a command center in some critical tasks such as the jet piloting where it is
required to give rapid and correct decisions under stress or  under the risk of  loss of conscious, or the surveillance
of  critical areas or devices would
highly support the performance of the mission. Inaddition, it is possible to
use EEG signals to make easier the  lives
of veterans who have normal brain functions but suffer from physical or speech
disabilities or in the investigation of their brain trauma.

References

  • [1] Rosenberg, W.V., Chanwimalueang, T., Goverdovsky, V., Looney, D., Sharp,D., and Mandic, D.P., “Smart Helmet: Wearable Multichannel ECG and EEG”, IEEE Journal of Translational Engineering in Health and Medicine, 4, (2016).
  • [2] Fisher, J.A.N., Huang, S., Ye, M., Nabili, M., Wilent, W.B., Krauthamer, V., Myers, M.R. and Welle, C.G., “Real-Time Detection And Monitoring Of Acute Brain Injury Utilizing Evoked Electroencephalographic Potentials”, IEEE Transactions On Neural Systems And Rehabilitation Engineering, 24(9): (2016).
  • [3] Scholl, C.A., Chi, Y.M., Elconin, M., Gray, W.R., Chevillet, M.A., and Pohlmeyer, E.A., “Classification of Pilot-Induced Oscillations during In-Flight Piloting Exercises Using Dry EEG Sensor Recordings”, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2016).
  • [4] Egan, B.F., Mizutani, T., Thurlow, A., “Data Fusion for Wearable Physiological Sensor Platforms”, Information Fusion, 2005 8th International Conference, 2: 1412-1419, (2005).
  • [5] Guizzo, E., “Smart Cars That Can Tell When You're Bored To Death”, Albuquerque, Sandia National Laboratories, IEEE Spectrum, (2007).
  • [6] Belenky, G., Sing, H.C., Thomas, M.L., Shaham, Y., Balwinski, S., Redmond, D.P., Balkin, T.J., “Discrimination of rested from sleep-deprived EEG in awake normal humans by artificial neural network”, Proc. IEEE Int Conf Neural Networks, IEEE Piscataway, NJ, 3521–3524, (1994).
  • [7] Lennox-Buchthal, M., Buchthal, F., Rosenfalck, P., “Correlation of Electroencephalographic Findings with Crash Rate of Military Jet Pilots“ Epilepsia, 1(1-5): 366–372, (1959).
  • [8] Yarman-Vural, F., Onaral, B., Çetin, E., “Enhanced Parametric Estimation of Electroencephalograms Under Acceleration Stress”, Conference of the IEEE Engineering in Medicine and Biology Society, 12(2): 837-839, (1990).
  • [9] Wilson, G.F., Purvis, B., Skelly, J., Fullenkamp, P., Davis, I., “Physiological data used to measure pilot workload in actual flight and simulator conditions”, Proceedings of the Human Factors Society, 779-783, (1987).
  • [10] Sterman, B., Schummer, G., Dushenko, T., Smith, J., “Electroencephalographic correlates of pilot performance: simulation and flight studies” Jesse, K. (editor) Electrical and Magnetic Activity of the Central Nervous System: Research and Clinical Applications in Aerospace Medicine AGARD, CP 432, (1987).
  • [11] Izzetoglu, K., Bunce, S., Izzetoglu, M., Onaral, B., Pourrezaei, K., “fNIR spectroscopy as a measure of cognitive task load”, Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International, Conference of the IEEE 4, 3431- 3434 (2003).
  • [12] Thatcher, R.W., North, D.M., Curtin, R.T., Walker, R.A., Biver, C.J., Gomez, J.F., Salazar, A.M. “An EEG Severity Index of Traumatic Brain Injury” J Neuropsychiatry Clin Neurosci, 13(1): 77-87, (2001).
  • [13] Stefan, A., “America’s Army Game: Its (Virtual) Reality Representation and Cocaine”, Proceedings of the International Conference on Cyberworlds (CW’04), (2004).
  • [14] Berka, C., Levendowski, D.J. Cvetinovic, M., Petrovic, M., Miroslav, M., et al. “Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset”, International Journal of Human- Computer Interaction, Norwood, 17(2): 151, (2004).
  • [15] Usakli, A.B. Gencer, N.G. “USB-Based 256-Channel Electroencephalographic Data Acquisition System for Electrical Source Imaging of the Human Brain”, Instrumentation Science & Technology, 35(3): 255-273, (2007).
  • [16] Uşaklı, A.B., Gurkan, S., Aloise, F., Vecchiato, G., Babiloni, F., “A hybrid platform based on EOG and EEG signals to restore communication for patients afflicted by progressive motor neurons diseases”, Engineering The Future of the Biomedicine IEEE EMBC 2009 Conference, Minneapolis USA, 543 - 546 DOI 10.1109/IEMBS.2009.5333742, (2009).
There are 16 citations in total.

Details

Subjects Engineering
Journal Section Research Article
Authors

Ali Bülent Uşaklı

Publication Date December 1, 2018
Submission Date July 26, 2017
Published in Issue Year 2018

Cite

APA Uşaklı, A. B. (2018). Fizyolojik Sinyallerin Askerî Amaçlı Kullanılabilirliği: Elektroensefalografi ve Yakın Kızılaltı Spektroskopisi Örnekleri. Politeknik Dergisi, 21(4), 895-900. https://doi.org/10.2339/politeknik.385474
AMA Uşaklı AB. Fizyolojik Sinyallerin Askerî Amaçlı Kullanılabilirliği: Elektroensefalografi ve Yakın Kızılaltı Spektroskopisi Örnekleri. Politeknik Dergisi. December 2018;21(4):895-900. doi:10.2339/politeknik.385474
Chicago Uşaklı, Ali Bülent. “Fizyolojik Sinyallerin Askerî Amaçlı Kullanılabilirliği: Elektroensefalografi Ve Yakın Kızılaltı Spektroskopisi Örnekleri”. Politeknik Dergisi 21, no. 4 (December 2018): 895-900. https://doi.org/10.2339/politeknik.385474.
EndNote Uşaklı AB (December 1, 2018) Fizyolojik Sinyallerin Askerî Amaçlı Kullanılabilirliği: Elektroensefalografi ve Yakın Kızılaltı Spektroskopisi Örnekleri. Politeknik Dergisi 21 4 895–900.
IEEE A. B. Uşaklı, “Fizyolojik Sinyallerin Askerî Amaçlı Kullanılabilirliği: Elektroensefalografi ve Yakın Kızılaltı Spektroskopisi Örnekleri”, Politeknik Dergisi, vol. 21, no. 4, pp. 895–900, 2018, doi: 10.2339/politeknik.385474.
ISNAD Uşaklı, Ali Bülent. “Fizyolojik Sinyallerin Askerî Amaçlı Kullanılabilirliği: Elektroensefalografi Ve Yakın Kızılaltı Spektroskopisi Örnekleri”. Politeknik Dergisi 21/4 (December 2018), 895-900. https://doi.org/10.2339/politeknik.385474.
JAMA Uşaklı AB. Fizyolojik Sinyallerin Askerî Amaçlı Kullanılabilirliği: Elektroensefalografi ve Yakın Kızılaltı Spektroskopisi Örnekleri. Politeknik Dergisi. 2018;21:895–900.
MLA Uşaklı, Ali Bülent. “Fizyolojik Sinyallerin Askerî Amaçlı Kullanılabilirliği: Elektroensefalografi Ve Yakın Kızılaltı Spektroskopisi Örnekleri”. Politeknik Dergisi, vol. 21, no. 4, 2018, pp. 895-00, doi:10.2339/politeknik.385474.
Vancouver Uşaklı AB. Fizyolojik Sinyallerin Askerî Amaçlı Kullanılabilirliği: Elektroensefalografi ve Yakın Kızılaltı Spektroskopisi Örnekleri. Politeknik Dergisi. 2018;21(4):895-900.
 
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