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Hybrid BCI Systems as HCI in Ambient Assisted Living Scenarios

  • Niccolò MoraEmail author
  • Ilaria De Munari
  • Paolo Ciampolini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9738)

Abstract

Brain Computer Interface (BCI) technology is an alternative/augmentative communication channel, based on the interpretation of the user’s brain activity, who can then interact with the environment without relying on neuromuscular pathways. Such technologies can act as alternative HCI devices towards AAL (Ambient Assisted Living) systems, thus opening their services to people for whom interacting with conventional interfaces could be troublesome, or even not viable. A complete BCI implementation is presented and discussed, briefly introducing the customized hardware and focusing more on the signal processing aspects. The BCI is based on SSVEP signals, featuring self-paced calibration-less operation, aiming at a “plug&play” approach. The signal processing chain is presented, introducing a novel method for improving accuracy and immunity to false positives. The results achieved, especially in terms of false positive rate containment (0.16 min−1) significantly improve over the literature. In addition, a possible integration of EMG signals in a hybrid-BCI scheme is discussed, serving as a binary switch to turn on/off the EEG-based BCI section (and the flashing stimuli unit). This can have positive impact on both the user’s comfort as well as on the resilience towards false positives. Preliminary results for jaw clench recognition show good detectability, proving that such integration can be implemented.

Keywords

Brain Computer Interface (BCI) Hybrid Brain Computer Interface (hBCI) Steady State Visual Evoked Potential (SSVEP) ElectroMyoGraphy (EMG) ElectroEncephaloGraphy(EEG) 

References

  1. 1.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)CrossRefGoogle Scholar
  2. 2.
    Bianchi, V., Grossi, F., De Munari, I., Ciampolini, P.: Multi sensor assistant: A multisensor wearable device for ambient assisted living. J. Med. Imaging Health Inf. 2(1), 70–75 (2012)CrossRefGoogle Scholar
  3. 3.
    Mora, N., Bianchi, V., De Munari, I., Ciampolini, P.: A BCI platform supporting aal applications. In: Stephanidis, C., Antona, M. (eds.) UAHCI 2014, Part I. LNCS, vol. 8513, pp. 515–526. Springer, Heidelberg (2014)Google Scholar
  4. 4.
    Mora, N., De Munari, I., Ciampolini, P.: Improving BCI usability as HCI in ambient assisted living system control. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) AC 2015. LNCS, vol. 9183, pp. 293–303. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  5. 5.
    del Millan, J.R., Mourino, J.: Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project. Trans. Neur. Sys. Rehab. Eng. 11(2), 159–161 (2003)CrossRefGoogle Scholar
  6. 6.
    Hinterberger, T., Schmidt, S., Neumann, N., Mellinger, J., Blankertz, B., Curio, G., Birbaumer, N.: Brain-computer communication and slow cortical potentials. IEEE Trans. Biomed. Eng. 51(6), 1011–1018 (2004)CrossRefGoogle Scholar
  7. 7.
    Pfurtscheller, G., Brunner, C., Schlögl, A., Lopes da Silva, F.H.: Mu rhythm (de) syn-chronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31(1), 153–159 (2006)CrossRefGoogle Scholar
  8. 8.
    Nijboer, F., Sellers, E.W., Mellinger, J., Jordan, M.A., Matuz, T., Furdea, A., Halder, S., et al.: A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clin. Neurophysiol. 119(8), 1909–1916 (2008)CrossRefGoogle Scholar
  9. 9.
    Carabalona, R., Grossi, F., Tessadri, A., Castiglioni, P., Caracciolo, A., De Munari, I.: Light on! Real world evaluation of a P300-based brain-computer interface (BCI) for environment control in a smart home. Ergonomics 55(5), 552–563 (2012)CrossRefGoogle Scholar
  10. 10.
    Cecotti, H.: A Self-Paced and Calibration-Less SSVEP-Based Brain-Computer Interface Speller. IEEE Trans. Neural Syst. Rehabil. Eng. 18(2), 127–133 (2010)CrossRefGoogle Scholar
  11. 11.
    Mora, N., De Munari, I., Ciampolini, P.: A plug&play Brain Computer Interface solution for AAL systems. Stud. Health Technol. Inf. 217, 152–158 (2015)Google Scholar
  12. 12.
    Mora, N., Bianchi, V., De Munari, I., Ciampolini, P.: Simple and efficient methods for steady state visual evoked potential detection in BCI embedded system. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2044–2048 (2014)Google Scholar
  13. 13.
    Mora, N., De Munari, I., Ciampolini, P.: Exploitation of a compact, cost-effective EEG module for plug-and-play, SSVEP-based BCI. In: 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 142–145 (2015)Google Scholar
  14. 14.
    Volosyak, I.: SSVEP-based Bremen-BCI interface - boosting information transfer rates. J. Neural Eng. 8(3), 447–450 (2011)CrossRefGoogle Scholar
  15. 15.
    Garcia-Molina, G., Zhu, D.: Optimal spatial filtering for the steady state visual evoked potential: BCI application. In: 5th International IEEE/EMBS Conference on Neural Engineering, pp. 156–160 (2011)Google Scholar
  16. 16.
    Lin, Z., Zhang, C., Wu, W., Gao, X.: Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans. Biomed. Eng. 54, 1172–1176 (2007)CrossRefGoogle Scholar
  17. 17.
    Mora, N., De Munari, I., Ciampolini, P.: Subject-independent, SSVEP-based BCI: trading off among accuracy, responsiveness and complexity. In: 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER) (2015)Google Scholar
  18. 18.
    Pfurtscheller, G., Solis-Escalante, T., Ortner, R., Linortner, P., Muller-Putz, G.R.: Self-paced operation of an ssvep-based orthosis with and without an imagery-based “brain switch:” a feasibility study towards a hybrid BCI. IEEE Trans. Neur. Sys. and Rehab. Eng. 18(4), 409–414 (2010)CrossRefGoogle Scholar
  19. 19.
    Pan, J., Li, Y., Zhang, R., Zhenghui, G., Li, F.: Discrimination Between Control and Idle States in Asynchronous SSVEP-Based Brain Switches: A Pseudo-Key-Based Approach. IEEE Trans. Neur. Sys. and Rehab. Eng. 21(3), 435–443 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Niccolò Mora
    • 1
    Email author
  • Ilaria De Munari
    • 1
  • Paolo Ciampolini
    • 1
  1. 1.Information Engineering DepartmentUniversità degli Studi di ParmaParmaItaly

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