Narzędzia help

Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
first previous next last
cannonical link button


Biocybernetics and Biomedical Engineering

Tytuł artykułu

An application of wireless brain–computer interface for drowsiness detection

Autorzy Tripathy, A. K.  Chinara, S.  Sarkar, M. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN Wirelessly networked systems of sensors could enable revolutionary applications at the intersection of biomedical science, networking and control systems. It has a strong potential to take ahead the applications of wireless sensor networks. In this paper, a wireless brain computer interface (BCI) framework for drowsiness detection is proposed, which uses electroencephalogram (EEG) signals produced from the brain wave sensors. The proposed BCI framework comprises of a braincap containing EEG sensors, wireless signal acquisition unit and a signal processing unit. The signal processing unit continuously monitor the preprocessed EEG signals and to trigger a warning tone if a drowsy state happens. This experimental setup provides longer time EEG monitoring and drowsiness detection by incorporating the clustering mechanism into the wireless networks.
Słowa kluczowe
PL sieć bezprzewodowa   grupowanie   wykrywanie senności   interfejs mózg-komputer  
EN wireless network   clustering   drowsiness detection   brain computer interface  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2016
Tom Vol. 36, no. 1
Strony 276--284
Opis fizyczny Bibliogr. 16 poz., rys., tab., wykr.
autor Tripathy, A. K.
  • Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India,
autor Chinara, S.
  • Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India,
autor Sarkar, M.
  • Department of Electrical and Computer Engineering, San Diego State University, CA, USA,
[1] Qiang J, Zhiwei Z, Lan P. Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Trans Veh Technol 2004;53:1052–68.
[2] Flores M, Armingol J, Escalera A. Real-time drowsiness detection system for an intelligent vehicle. IEEE Intelligent Vehicles Symposium. 2008. pp. 637–42.
[3] Lin C, Chang C, Lin B, Hung S, Chao C, Wang I. A real-time wireless brain–computer interface system for drowsiness detection. IEEE Trans Biomed Circ Syst 2010;4:214–22.
[4] Eskandarian A, Mortazavi A. Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection. IEEE Intelligent Vehicles Symposium. 2007. pp. 553–9.
[5] Lotte F, Congedo M, Lecuyer A, Lamarche F, Arnaldi B. A review of classification algorithms for EEG bases brain computer interface. Hum Factors 2007;4.
[6] NHTSA National Highway Traffic Safety Administration. Washington, DC; 2014 [online]. Available from:
[7] Sleep facts and stats, National Sleep Foundation. Washington, DC; 2014 [online]. Available from:
[8] Filipe S, Charvet G, Foerster M, et al. A wireless multichannel EEG recording platform. Engineering in Medicine and Biology Society, EMBC. 2011. pp. 6319–22.
[9] Hong T, Qin H. Drivers drowsiness detection in embedded system. IEEE International Conference on Vehicular Electronics and Safety, ICVES. 2007. pp. 1–5.
[10] Orden KV, Limbert W, Makeig S, Jung TP. Eye activity correlates of workload during a visual spatial memory task. Hum Factors 2001;43:111–21.
[11] Heinzelman W, Chandrakasan A, Balakrishnan H. An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wireless Commun 2002;1:660–70.
[12] Bashyal S, Venayagamoorthy GK. Collaborative routing algorithm for wireless sensor network longevity. IEEE 3rd International Conference on Intelligent Sensors, Sensor Networks and Information. 2007. pp. 515–20.
[13] Sucholeiki R. EEG description. aw2aab6b3 [accessed 20.01.15].
[14] Mardia KV. Mardia's test of multinormality in encyclopedia of statistical sciences; 1985.
[15] Farshchi S, Nuyujukian P, Pesterev A, Mody I, Judy J. A TinyOS-enabled MICA2-basedwireless neural interface. IEEE Trans Biomed Eng 2006;53:1416–24.
[16] Yan N, Wang J, Liu MY, Zong L, Jiao YF, Yue J, et al. Designing a brain–computer interface device for neurofeedback using virtual environments. J Med Biol Eng 2008;28:167–72.
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-4e240189-63e8-43c2-a03e-0077af22a683
DOI 10.1016/j.bbe.2015.08.001