Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Common spatial pattern method for real-time eye state identification by using electroencephalogram signals

Common spatial pattern method for real-time eye state identification by using electroencephalogram signals

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Cross-channel maximum and minimum are used to monitor real-time electroencephalogram signals in 14 channels. On detection of a possible change, multivariate empirical mode decomposed the last 2 s of the signal into narrow-band intrinsic mode functions. Common spatial pattern is then utilised to create discriminating features for classification purpose. Logistic regression, artificial neural network, and support vector machine classifiers all could detect the eye state change with 83.4% accuracy in <2 s. This algorithm provides a valuable improvement in comparison with a recent procedure that took about 20 min to classify new instances with 97.3% accuracy. Application of the introduced algorithm in the real-time eye state classification is promising. Increasing the training examples could even improve the accuracy of the classification analytics.

References

    1. 1)
      • 20. Rehman, N., Mandic, D.P.: ‘Multivariate empirical mode decomposition’, Proc. R. Soc. A, 2009, 466, (2117), pp. 12911302.
    2. 2)
      • 22. Tan, D.S., Nijholt, A.: ‘Brain–computer interfaces’ (Springer-Verlag, London, 2010).
    3. 3)
      • 9. Wang, T., Guan, S.U., Man, K.L., et al: ‘EEG eye state identification using incremental attribute learning with time-series classification’, Math. Probl. Eng., 2014, 2014, (2014), pp. 158161.
    4. 4)
      • 7. Correa, A.G., Orosco, L., Laciar, E.: ‘Automatic detection of drowsiness in EEG records based on multimodal analysis’, Med. Eng. Phys., 2014, 36, (2), pp. 244249.
    5. 5)
      • 3. Estévez, P.A., Held, C.M., Holzmann, C.A., et al: ‘Polysomnograhic pattern recognition for automated classification of sleep-waking states in infants’, Med. Biol. Eng. Comput., 2002, 40, (1), pp. 105113.
    6. 6)
      • 21. Koles, Z.J.: ‘The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG’, Electroencephalogr. Clin. Neurophysiol., 1991, 79, (6), pp. 440447.
    7. 7)
      • 6. Mardi, Z., Ashtiani, S.N.M., Mikaili, M.: ‘EEG-based drowsiness detection for safe driving using chaotic features and statistical tests’, J. Med. Signals Sens., 2011, 1, (2), pp. 130137.
    8. 8)
      • 8. Huabiao, Q., Jun, L., Tianyi, H.: ‘An eye state identification method based on the embedded hidden Markov model’. IEEE Int. Conf. Vehicular Electronics and Safety, Istanbul, Turkey, July 2012, pp. 255260.
    9. 9)
      • 19. Huang, N.E., Shen, Z., Long, S.R., et al: ‘The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis’, Proc. R. Soc. A, 1998, 454, pp. 903995.
    10. 10)
      • 12. Schalk, G., Mellinger, J.: ‘A practical guide to brain–computer interfacing with BCI2000’ (Springer Science & Business Media, 2010).
    11. 11)
      • 11. Sabanci, K., Koklu, M.: ‘The classification of eye state by using kNN and MLP classification models according to the EEG signals’, Int. J. Intell. Syst. Appl. Eng., 2015, 3, (4), pp. 127130.
    12. 12)
      • 4. Norizam, S., Taib, M.N., Aris, M., et al: ‘Stress features identification from EEG signals using EEG asymmetry & spectral centroids techniques’. Proc. IEEE EMBS Conf. Biomedical Engineering and Sciences, Kuala Lumpur, Malaysia, December 2010, pp. 417421.
    13. 13)
      • 23. Blankertz, B., Tomioka, R., Lemm, S., et al: ‘Optimizing spatial filters for robust EEG single-trial analysis’, IEEE Signal Process. Mag., 2008, 25, (1), pp. 4156.
    14. 14)
      • 13. Kamel, N., Malik, A.S.: ‘EEG/ERP analysis methods and applications’ (CRC Press, 2015).
    15. 15)
      • 5. Królak, A., Strumiłło, P.: ‘Eye-blink detection system for human–computer interaction’, Univers. Access. Inf. Soc., 2012, 11, (4), pp. 409419.
    16. 16)
      • 10. Rösler, O., Suendermann, D.: ‘First step towards eye state prediction using EEG’. Proc. Int. Conf. Applied Informatics for Health and Life Sciences (AIHLS 13), Istanbul, Turkey, September 2013.
    17. 17)
      • 15. Rösler, O., Bader, L., Forster, J., et al: ‘Comparison of EEG devices for eye state classification’. Int. Conf. Applied Informatics for Health and Life Sciences (AIHLS 14), Kusadasi, Turkey, October 2014.
    18. 18)
      • 17. Park, C., Looney, D., Rehman, N., et al: ‘Classification of motor imagery BCI using multivariate empirical mode decomposition’, IEEE Trans. Neural Syst. Rehabil. Eng., 2013, 21, (1), pp. 1021.
    19. 19)
      • 14. Frank, A., Asuncion, A.: ‘UCI machine learning repository’. Available at http://archive.ics.uci.edu/ml, accessed 2016.
    20. 20)
      • 2. Genuth, I.: ‘All in the mind [EEG]’, Eng. Technol., 2015, 10, (5), pp. 3739.
    21. 21)
      • 24. Li, L.: ‘The differences among eyes-closed, eyes-open and attention states: an EEG study’. Proc. Sixth Int. Conf. Wireless Communications Networking and Mobile Computing (WiCOM), Chengdu City, China, September 2010.
    22. 22)
      • 18. Mandic, D., Rehman, N., Wu, Z., et al: ‘Empirical mode decomposition-based time–frequency analysis of multivariate signals’, IEEE Signal Process. Mag., 2013, 30, (6), pp. 7486.
    23. 23)
      • 1. Azevedo, F.A., Carvalho, L.R., Grinberg, L.T., et al: ‘Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain’, J. Comp. Neurol., 2009, 513, (5), pp. 532541.
    24. 24)
      • 16. Delorme, A., Makeig, S.: ‘EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics’, J. Neurosci. Methods, 2004, 134, pp. 921.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2016.0520
Loading

Related content

content/journals/10.1049/iet-spr.2016.0520
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address