Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2017 (2017), Article ID 3072870, 12 pages
https://doi.org/10.1155/2017/3072870
Research Article

EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone

1Neuropsychology Lab, Department of Psychology, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany
2Cluster of Excellence Hearing4All, Oldenburg, Germany
3Systems in Medical Engineering Lab, Department of Health Services Research, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany

Correspondence should be addressed to Sarah Blum; ed.grubnedlo-inu@mulb.haras

Received 18 August 2017; Accepted 30 October 2017; Published 16 November 2017

Academic Editor: Frederic Dehais

Copyright © 2017 Sarah Blum et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Luck, An Introduction to the Event-Related Potential Technique, 2014.
  2. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clinical Neurophysiology, vol. 113, no. 6, pp. 767–791, 2002. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Blankertz, G. Curio, and K.-R. Müller, “Classifying single trial EEG: towards brain computer interfacing,” in Advances in Neural Information Processing Systems 14 (NIPS 2001), T. G. Dietterich, S. Becker, and Z. Ghahramani, Eds., vol. 1, pp. 157–164, MIT Press, Cambridge, Mass, USA, 2002. View at Google Scholar
  4. M. J. Vansteensel, E. G. M. Pels, M. G. Bleichner et al., “Fully implanted brain-computer interface in a locked-in patient with ALS,” The New England Journal of Medicine, vol. 375, no. 21, pp. 2060–2066, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. N. Braun, C. Kranczioch, J. Liepert et al., “Motor Imagery Impairment in Postacute Stroke Patients,” Neural Plasticity, vol. 2017, 13 pages, 2017. View at Publisher · View at Google Scholar
  6. R. Spataro, A. Chella, B. Allison et al., “Reaching and grasping a glass of water by locked-In ALS patients through a BCI-controlled humanoid robot,” Frontiers in Human Neuroscience, vol. 11, article no. 68, 2017. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Haufe, M. S. Treder, M. F. Gugler, M. Sagebaum, G. Curio, and B. Blankertz, “EEG potentials predict upcoming emergency brakings during simulated driving,” Journal of Neural Engineering, vol. 8, no. 5, Article ID 056001, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Debener, R. Emkes, M. De Vos, and M. Bleichner, “Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear,” Scientific Reports, vol. 5, Article ID 16743, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Stopczynski, C. Stahlhut, M. K. Petersen et al., “Smartphones as pocketable labs: Visions for mobile brain imaging and neurofeedback,” International Journal of Psychophysiology, vol. 91, no. 1, pp. 54–66, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Debener, F. Minow, R. Emkes, K. Gandras, and M. de Vos, “How about taking a low-cost, small, and wireless EEG for a walk?” Psychophysiology, vol. 49, no. 11, pp. 1617–1621, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. R. Zink, B. Hunyadi, S. V. Huffel, and M. D. Vos, “Mobile EEG on the bike: Disentangling attentional and physical contributions to auditory attention tasks,” Journal of Neural Engineering, vol. 13, no. 4, Article ID 046017, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. M. G. Bleichner and S. Debener, “Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG,” Frontiers in Human Neuroscience, vol. 11, p. 163, 2017. View at Publisher · View at Google Scholar
  13. C. Gretton and M. Honeyman: “The digital revolution: eight technologies that will change health and care | The King’s Fund,” https://www.kingsfund.org.uk/publications/articles/eight-technologies-will-change-health-and-care.
  14. Google Research: “TensorFlow: Large-scale machine learning on heterogeneous systems.” pp. 19, 2015.
  15. M. De Vos and S. Debener, “Mobile eeg: towards brain activity monitoring during natural action and cognition,” International Journal of Psychophysiology, vol. 91, no. 1, pp. 1-2, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. B. Griffiths, A. Mazaheri, S. Debener, and S. Hanslmayr, “Brain oscillations track the formation of episodic memories in the real world,” NeuroImage, vol. 143, pp. 256–266, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Stopczynski, C. Stahlhut, J. E. Larsen, M. K. Petersen, and L. K. Hansen, “The smartphone brain scanner: A portable real-time neuroimaging system,” PLoS ONE, vol. 9, no. 2, Article ID e86733, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. A. T. Campbell, T. Choudhury, S. Hu et al., “Neuro-phone: brain-mobile phone interface using a wireless EEG headset,” in Proceedings of the 2nd ACM SIGCOMM Workshop on Networking, Systems, and Applications on Mobile Handhelds (MobiHeld '10), pp. 3–8, New Delhi, India, September 2010. View at Publisher · View at Google Scholar
  19. Y.-T. Wang, Y. Wang, and T.-P. Jung, “A cell-phone-based brain-computer interface for communication in daily life,” Journal of Neural Engineering, vol. 8, no. 2, p. 25018, 2011. View at Google Scholar
  20. Y.-T. Wang, Y. Wang, C.-K. Cheng, and T.-P. Jung, “Developing stimulus presentation on mobile devices for a truly portable SSVEP-based BCI,” in Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, pp. 5271–5274, Japan, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. F. Keller and S. Wendt, “FMC: An approach towards architecture-centric system development,” in Proceedings of the 10th IEEE International Conference and Workshop on the Engineering of Computer-Based Systems, ECBS 2003, pp. 173–182, USA, April 2003. View at Publisher · View at Google Scholar · View at Scopus
  22. I. Choi, S. Rajaram, L. A. Varghese, and B. G. Shinn-Cunningham, “Quantifying attentional modulation of auditory-evoked cortical responses from single-trial electroencephalography,” Frontiers in Human Neuroscience, no. 2013, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. B. Mirkovic, M. G. Bleichner, M. De Vos, and S. Debener, “Target speaker detection with concealed EEG around the ear,” Frontiers in Neuroscience, vol. 10, article no. 349, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. mBrainTrain | Fully Mobile EEG Devices, https://mbraintrain.com/.
  25. Swartz Center for Computational Neuroscience and C. Kothe: Lab Streaming Layer (lsl), https://github.com/sccn/labstreaminglayer.
  26. C. Neurobehavioral Systems, Inc., Berkeley: Presentation®, https://www.neurobs.com/.
  27. S. Reid, “Concurrent Planning and Execution for Autonomous Robots,” IEEE Control Systems Magazine, vol. 12, no. 1, pp. 46–50, 1992. View at Publisher · View at Google Scholar · View at Scopus
  28. The Apache Commons Mathematics Library, Version: 4.0-SNAPSHOT, http://commons.apache.org/proper/commons-math/.
  29. Google Developers: Audio Latency Measurements | Android Open Source Project, https://source.android.com/devices/audio/latency_measurements.
  30. M. G. Bleichner, B. Mirkovic, and S. Debener, “Identifying auditory attention with ear-EEG: CEEGrid versus high-density cap-EEG comparison,” Journal of Neural Engineering, vol. 13, no. 6, Article ID 066004, 2016. View at Publisher · View at Google Scholar · View at Scopus
  31. P. L. Nunez and R. Srinivasan, “Electric fields of the brain: the neurophysics of EEG,” in Medicine Health Science Books @ Amazon.com, 9780195050387, Oxford University Press, 2nd edition, 2006. View at Google Scholar
  32. J. Hine and S. Debener, “Late auditory evoked potentials asymmetry revisited,” Clinical Neurophysiology, vol. 118, no. 6, pp. 1274–1285, 2007. View at Publisher · View at Google Scholar · View at Scopus
  33. J. R. Kerlin, A. J. Shahin, and L. M. Miller, “Attentional gain control of ongoing cortical speech representations in a "cocktail party",” The Journal of Neuroscience, vol. 30, no. 2, pp. 620–628, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” Journal of Neuroscience Methods, vol. 134, no. 1, pp. 9–21, 2004. View at Publisher · View at Google Scholar · View at Scopus
  35. N. Bigdely-Shamlo, K. Kreutz-Delgado, C. Kothe, and S. Makeig, “EyeCatch: Data-mining over half a million EEG independent components to construct a fully-automated eye-component detector,” in Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, pp. 5845–5848, Japan, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. F. Campos Viola, J. Thorne, B. Edmonds, T. Schneider, T. Eichele, and S. Debener, “Semi-automatic identification of independent components representing EEG artifact,” Clinical Neurophysiology, vol. 120, no. 5, pp. 868–877, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. W. H. R. Miltner, C. H. Braun, and M. G. H. Coles, “Event-related brain potentials following incorrect feedback in a time-estimation task: evidence for a ‘generic’ neural system for error detection,” Cognitive Neuroscience, vol. 9, no. 6, pp. 788–798, 1997. View at Publisher · View at Google Scholar · View at Scopus
  38. Y. Huang and R. Yu, “The feedback-related negativity reflects ‘more or less’ prediction error in appetitive and aversive conditions,” Frontiers in Neuroscience, vol. 8, article 108, 2014. View at Publisher · View at Google Scholar · View at Scopus
  39. E. Combrisson and K. Jerbi, “Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy,” Journal of Neuroscience Methods, vol. 250, pp. 126–136, 2015. View at Publisher · View at Google Scholar · View at Scopus
  40. B. Blankertz, S. Lemm, M. Treder, S. Haufe, and K.-R. Müller, “Single-trial analysis and classification of ERP components—a tutorial,” NeuroImage, vol. 56, no. 2, pp. 814–825, 2011. View at Publisher · View at Google Scholar · View at Scopus
  41. P. W. Ferrez and J. Del R. Millán, “Error-related EEG potentials generated during simulated brain-computer interaction,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 3, pp. 923–929, 2008. View at Publisher · View at Google Scholar · View at Scopus
  42. T. O. Zander and C. Kothe, “Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general,” Journal of Neural Engineering, vol. 8, no. 2, Article ID 025005, 2011. View at Publisher · View at Google Scholar · View at Scopus
  43. S. Mathôt, D. Schreij, and J. Theeuwes, “OpenSesame: an open-source, graphical experiment builder for the social sciences,” Behavior Research Methods, vol. 44, no. 2, pp. 314–324, 2012. View at Publisher · View at Google Scholar · View at Scopus
  44. C. Hollis, R. Morriss, J. Martin et al., “Technological innovations in mental healthcare: Harnessing the digital revolution,” The British Journal of Psychiatry, vol. 206, no. 4, pp. 263–265, 2015. View at Publisher · View at Google Scholar · View at Scopus
  45. W. H. Frist, “Connected health and the rise of the patient-consumer,” Health Affairs, vol. 33, no. 2, pp. 191–193, 2014. View at Publisher · View at Google Scholar · View at Scopus
  46. R. Kwok, “Personal technology: Phoning in data,” Nature, vol. 458, no. 7241, pp. 959–961, 2009. View at Publisher · View at Google Scholar · View at Scopus
  47. J. Cartwright, “Smarthphone science: researchers are learning how to convert devices into global laboratories,” Nature, vol. 531, pp. 669–671, 2016. View at Google Scholar
  48. L. Piwek, D. A. Ellis, S. Andrews, and A. Joinson, “The Rise of Consumer Health Wearables: Promises and Barriers,” PLoS Medicine, vol. 13, no. 2, Article ID e1001953, 2016. View at Publisher · View at Google Scholar · View at Scopus