Advertisement

Brain Signal for Smart Offices

  • Ghada Al-HudhudEmail author
  • Noha Alrajhi
  • Nouf Alonaizy
  • Aysha Al-Mahmoud
  • Latifah Almazrou
  • Dalal bin Muribah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9189)

Abstract

Many people in their work environment are interested and focused on their work, and they do not want to interrupt their work progress by doing simple office tasks like Increasing or decreasing the light brightness in the office or the temperature of the office. In addition, a more important issue is to consider cases where some of people have major disabilities in their bodies that prevent them from doing that. In this situation, Brain Signals for Smart Offices (BSSO) is considered to be a preferable solution.

Keywords

Feature Extraction Finite Impulse Response Brain Signal Emotion Recognition System False Match Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This research project was supported by a grant from the “Research Center of the Female Scientific and Medical Colleges”, Deanship of Scientific University.

References

  1. 1.
    Mikulecký, P.: Smart environments for smart learning. In: 9th International Scientific Conference on Distance Learning in Applied Informatics, Sturovo, Slovakia (2012)Google Scholar
  2. 2.
    Martin, J., Le Gal, C., Lux, A., Crowley, J.: Smart office: design of an intelligent environment. IEEE Intell. Syst. 16(4), 60–66 (2001)CrossRefGoogle Scholar
  3. 3.
    IEEE Xplore Abstract - Brain computer interface (BCI) with EEG signals for automatic vowel recognition based on articulation. IEEE Xplore Abstract - Brain computer interface (BCI) with EEG signals for automatic vowel recognition based on articulation (2014). http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6880997. Accessed 28 Oct 2014
  4. 4.
    IEEE Xplore Abstract - Emotional stress recognition system using EEG and psychophysiological signals: Using New Labelling P… IEEE Xplore Abstract - Emotional Stress Recognition System Using EEG and Psychophysiological Signals: Using New Labelling P…. (2014). http://ieeexplore.ieee.org/xpl/login.jsp?tp=&;arnumber=5462520. Accessed 28 Oct 1314
  5. 5.
  6. 6.
    Aarabi, A., Fazel-Rezai, R., Aghakhani, Y.: A fuzzy rule-based system for epileptic seizure detection in intracranial EEG. Clin. Neurophysiol. 113(12), 1648–1657 (2009)CrossRefGoogle Scholar
  7. 7.
    Adeli, H., Ghosh-Dastidar, S., Dadmehr, N.: A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans. Biomed. Eng. 54(2), 205–211 (2007)CrossRefGoogle Scholar
  8. 8.
    He, P., Kahle, M., Wilson, G., Russell, C.: Removal of ocular artifacts from EEG: a comparison of adaptive filtering method and regression method using simulated data. In: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp. 1110–1113 (2005)Google Scholar
  9. 9.
    Senthil Kumar, P., Arumuganathan, R., Vimal, C.: An adaptive method to remove ocular artifacts from EEG signals using wavelet transform. J. Appl. Sci. Res. 5, 741–745 (2009)Google Scholar
  10. 10.
    Mourad, N., Reilly, J.P., de Bruin, H., Hasey, G., MacCrimmon, D.: A simple and fast algorithm for automatic suppression of high-amplitude artifacts in EEG data. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. I393–I396 (2007)Google Scholar
  11. 11.
    Correa, A.G., Laciar, E., Patiño, H.D., Valentinuzzi, M.E.: Artifact removal from EEG signals using adaptive filters in cascade. J. Phys. Conf. Ser. 90, 1–10 (2007). 011381Google Scholar
  12. 12.
    Varsavsky, A., Mareels, I., Cook, M.: Epileptic Seizures and the EEG. CRC Press, Boca Raton (2011)Google Scholar
  13. 13.
    Guo, L., Rivero, D., Dorado, J., Rabuñal, J.R., Pazos, A.: Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J. Neurosci. Methods 191, 101–109 (2010)CrossRefGoogle Scholar
  14. 14.
    Yuan, Q., Zhou, W., Li, S., Cai, D.: Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res. 96, 29–38 (2011)CrossRefGoogle Scholar
  15. 15.
    Deburchgraeve, W., Cherian, P.J., De Vos, M., Swarte, R.M., Blok, J.H., Visser, G.H., Govaert, P., Van Huffel, S.: Automated neonatal seizure detection mimicking a human observer reading EEG. Clin. Neurophysiol. 119, 2447–2454 (2008)CrossRefGoogle Scholar
  16. 16.
    Polat, K., Günes, S.: Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl. Math. Comput. 187, 1017–1026 (2007)CrossRefMathSciNetzbMATHGoogle Scholar
  17. 17.
    Subasi, A.: EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst. Appl. 32, 1084–1093 (2007)CrossRefGoogle Scholar
  18. 18.
    Kannathal, N., Choob, M.L., Acharya, U.R., Sadasivana, P.K.: Entropies for detection of epilepsy in EEG. Comput. Meth. Programs Biomed. 80, 187–194 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ghada Al-Hudhud
    • 1
    Email author
  • Noha Alrajhi
    • 1
  • Nouf Alonaizy
    • 1
  • Aysha Al-Mahmoud
    • 1
  • Latifah Almazrou
    • 1
  • Dalal bin Muribah
    • 1
  1. 1.Department of Information Technology, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia

Personalised recommendations