Advertisement

Towards Noise-Enhanced Augmented Cognition

  • Alexander J. Casson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8027)

Abstract

Workload classification Augmented Cognition systems aim to detect when an operator is in a high or low workload state, and then to modify their work flow and operating environment based upon this knowledge. This paper reviews state-of-the-art electroencephalography (EEG) recorders for use in such systems and investigates the impact of EEG noise on an example system performance. It is found that adding up to 15 μV\(_{\mbox{\scriptsize RMS}}\) of artificially generated noise still leaves EEG signals that have correlations in-line with the correlations found between conventional wet EEG electrodes and new dry electrodes. The workload classification system is found to be robust in the presence of small amounts of noise, and there is initial evidence of small stochastic resonance effects whereby better performance can actually be obtained in the noisy case compared to the traditional noise-less case.

Keywords

EEG Augmented Cognition Workload classification Noise-enhanced signal processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Casson, A.J., Yates, D.C., Smith, S.J., Duncan, J.S., Rodriguez-Villegas, E.: Wearable electroencephalography. IEEE Eng. Med. Biol. Mag. 29, 44–56 (2010)CrossRefGoogle Scholar
  2. 2.
    Verma, N., Shoeb, A., Bohorquez, J., Dawson, J., Guttag, J., Chandrakasan, A.P.: A micro-power EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system. IEEE J. Solid-State Circuits 45, 804–816 (2010)CrossRefGoogle Scholar
  3. 3.
    Xu, J., Yazicioglu, R.F., Grundlehner, B., Harpe, P., Makinwa, K.A.A., Van Hoof, C.: A 160 μW 8-channel active electrode system for EEG monitoring. IEEE Trans. Biomed. Circuits Syst. 5, 555–567 (2011)CrossRefGoogle Scholar
  4. 4.
    Kim, D.H., Lu, N., Ma, R., Kim, Y.S., Kim, R.H., Wang, S., Wu, J., Won, S.M., Tao, H., Islam, A., Yu, K.J., Kim, T.I., Chowdhury, R., Ying, M., Xu, L., Li, M., Chung, H.J., Keum, H., McCormick, M., Liu, P., Zhang, Y.W., Omenetto, F.G., Huang, Y., Coleman, T., Rogers, J.A.: Epidermal electronics. Science 333, 838–843 (2011)CrossRefGoogle Scholar
  5. 5.
    Nikulin, V.V., Kegeles, J., Curio, G.: Miniaturized electroencephalographic scalp electrode for optimal wearing comfort. Clin. Neurophysiol. 121, 1007–1014 (2010)CrossRefGoogle Scholar
  6. 6.
    Chi, Y., Jung, T.P., Cauwenberghs, G.: Dry-contact and noncontact biopotential electrodes: Methodological review. IEEE Rev. Biomed. Eng. 3, 106–119 (2010)CrossRefGoogle Scholar
  7. 7.
    camntech Actiwave: Home page (2013), http://www.camntech.com/
  8. 8.
    Emotiv EEG systems: Home page (2013), http://www.emotiv.com/
  9. 9.
    Advanced Brain Monitoring B-Alert X4: Home page (2013), http://advancedbrainmonitoring.com/
  10. 10.
    NeuroSky MindWave: Home page (2013), http://www.neurosky.com/
  11. 11.
    Sleep Zeo: Home page (2013), http://www.myzeo.com/sleep/
  12. 12.
    Shambroom, J.R., Fabregas, S.E., Johnstone, J.: Validation of an automated wireless system to monitor sleep in healthy adults. J. Sleep Res. 21, 221–230 (2012)CrossRefGoogle Scholar
  13. 13.
    Neuroelectrics Enobio: Home page (2013), http://neuroelectrics.com/
  14. 14.
    Quasar DSI 10/20: Home page (2013), http://www.quasarusa.com/
  15. 15.
    IMEC: Holst centre and panasonic present wireless low-power active-electrode EEG headset (2012), http://www.imec.be/
  16. 16.
    Patki, S., Grundlehner, B., Verwegen, A., Mitra, S., Xu, J., Matsumoto, A., Yazicioglu, R.F., Penders, J.: Wireless EEG system with real time impedance monitoring and active electrodes. In: IEEE BioCAS, Hsinchu (2012)Google Scholar
  17. 17.
    Mindo 4H Earphone: Home page (2013), http://www.mindo.com.tw/
  18. 18.
    Looney, D., Kidmose, P., Park, C., Ungstrup, M., Rank, M.L., Rosenkranz, K., Mandic, D.P.: The in-the-ear recording concept: User-centered and wearable brain monitoring. IEEE Pulse 3, 32–42 (2012)CrossRefGoogle Scholar
  19. 19.
    g.tec g.sahara: Home page (2013), http://www.gtec.at/
  20. 20.
    Slater, J.D., Kalamangalam, G.P., Hope, O.: Quality assessment of electroencephalography obtained from a “dry electrode” system. J. Neurosci. Methods 208, 134–137 (2012)CrossRefGoogle Scholar
  21. 21.
    Gandhi, N., Khe, C., Chung, D., Chi, Y.M., Cauwenberghs, G.: Properties of dry and non-contact electrodes for wearable physiological sensors. In: Int. Conf. BSN, Dallas (2011)Google Scholar
  22. 22.
    Matthews, R., McDonald, N.J., Hervieux, P., Turner, P.J., Steindorf, M.A.: A wearable physiological sensor suite for unobtrusive monitoring of physiological and cognitive state. In: IEEE EMBC, Lyon (2007)Google Scholar
  23. 23.
    Gargiulo, G., Bifulco, P., Calvo, R.A., Cesarelli, M., Jin, C., van Schaik, A.: A mobile EEG system with dry electrodes. In: IEEE BioCAS, Baltimore (2008)Google Scholar
  24. 24.
    Estepp, J.R., Christensen, J.C., Monnin, J.W., Davis, I.M., Wilson, G.F.: Validation of a dry electrode system for EEG. In: Proc. HFES, San Antonio (2009)Google Scholar
  25. 25.
    Huigen, E., Peper, A., Grimbergen, C.A.: Investigation into the origin of the noise of surface electrodes. Med. Biol. Eng. Comput. 40, 332–338 (2002)CrossRefGoogle Scholar
  26. 26.
    Casson, A.J., Rodriguez-Villegas, E.: Utilising noise to improve an interictal spike detector. J. Neurosci. Methods 201, 262–268 (2011)CrossRefGoogle Scholar
  27. 27.
    De Clercq, W., Vergult, A., Vanrumste, B., Van Paesschen, W., Van Huffel, S.: Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans. Biomed. Eng. 53, 2583–2587 (2006)CrossRefGoogle Scholar
  28. 28.
    Vergult, A., De Clercq, Q., Palmini, A., Vanrumste, B., Dupont, P., Van Huffel, S., Van Paesschen, W.: Improving the interpretation of ictal scalp EEG: BSS-CCA algorithm for muscle artifact removal. Epilepsia 45, 950–958 (2007)CrossRefGoogle Scholar
  29. 29.
    Kay, S.: Can detectability be improved by adding noise? IEEE Signal Processing Lett. 7, 8–10 (2000)CrossRefGoogle Scholar
  30. 30.
    Christensen, J.C., Estepp, J.R., Wilson, G.F., Russell, C.A.: The effects of day-to-day variability of physiological data on operator functional state classification. Neuroimage 59, 57–63 (2012)CrossRefGoogle Scholar
  31. 31.
    Estepp, J.R., Klosterman, S.L., Christensen, J.C.: An assessment of non-stationarity in physiological cognitive state assessment using artificial neural networks. In: IEEE EMBC, Boston (2011)Google Scholar
  32. 32.
    Comstock, J.R., Arnegard, R.J.: The multi-attribute task battery for human operator workload and strategic behavior research. Technical report, TM-104174, National Aeronautics and Space Administration Langley Research Center (1992)Google Scholar
  33. 33.
    Miller Jr., W.D.: The U.S. air force-developed adaptation of the multi-attribute task battery for the assessment of human operator workload and strategic behavior. Technical report, AFRL-RH-WP-TR-2010-0133, U.S. Air Force Research Laboratory (2010)Google Scholar
  34. 34.
    Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, Oxford (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexander J. Casson
    • 1
  1. 1.Department of Electrical and Electronic EngineeringImperial College LondonUK

Personalised recommendations