A Dataflow-based Mobile Brain Reading System on Chip with Supervised Online Calibration - For Usage without Acquisition of Training Data

Hendrik Woehrle, Johannes Teiwes, Mario Michael Krell, Elsa Andrea Kirchner, Frank Kirchner

Abstract

Brain activity is more and more used for innovative applications like Brain Computer Interfaces (BCIs). However, in order to be able to use the brain activity, the related psychophysiological data has to be processed and analyzed with sophisticated signal processing and machine learning methods. Usually these methods have to be calibrated with subject-specific data before they can be used. Since future systems that implement these methods need to be portable to be applied more flexible tight constraints regarding size, power consumption and computing time have to be met. Field Programmable Gate Arrays (FPGAs) are a promising solution, which are able to meet all the constraints at the same time. Here, we present an FPGA-based mobile system for signal processing and classification. In addition to other systems, it is able to be calibrated and adapt at runtime, which makes the acquisition of training data unnecessary.

References

  1. Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., and Singer, Y. (2006). Online passive-aggressive algorithms. The Journal of Machine Learning Research, 7:551-585.
  2. Farwell, L. A. and Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol., 70(6):510-23.
  3. Isreal, J., Chesney, G., Wickens, C., and Donchin, E. (1980). P300 and tracking difficulty: Evidence for multiple resources in dual-task performance. Psychophysiology, 17(3):259-73.
  4. Khurana, K., Gupta, P., Panicker, R., and Kumar, A. (2012). Development of an FPGA-based real-time p300 speller. In 2012 22nd International Conference on Field Programmable Logic and Applications (FPL), pages 551 -554.
  5. Kirchner, E. A. and Kim, S. K. (2012). EEG in Dual-Task Human-Machine Interaction: Target Recognition and Prospective Memory. In Proceedings of the 18th Annual Meeting of the Organization for Human Brain Mapping.
  6. Kirchner, E. A., W öhrle, H., Bergatt, C., Kim, S. K., Metzen, J. H., Feess, D., and Kirchner, F. (2010). Towards operator monitoring via brain reading - an EEG-based approach for space applications. In Proc. 10th Int. Symp. Artificial Intelligence, Robotics and Automation in Space, pages 448-455, Sapporo.
  7. Lin, C., Ko, L., Chang, M., Duann, J., Chen, J., Su, T., and Jung, T. (2009). Review of wireless and wearable electroencephalogram systems and braincomputer InterfacesA mini-review. Gerontology.
  8. Linaro (2013). Open source software for arm socs. [Online; accessed 11-April-2013].
  9. Meyer-Baese, U. (2004). Digital signal processing with field programmable gate arrays. Springer Verlag.
  10. Polich, J. (2007). Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol, 118(10):2128-48.
  11. Rivet, B., Souloumiac, A., Attina, V., and Gibert, G. (2009). xDAWN algorithm to enhance evoked potentials: application to braincomputer interface. Biomedical Engineering, IEEE Transactions on, 56(8):20352043.
  12. Shenoy, P., Krauledat, M., Blankertz, B., Rao, R. P. N., and Mller, K.-R. (2006). Towards adaptive classification for bci. Journal of Neural Engineering, 3(1):R13.
  13. Shyu, K. K., Lee, P. L., Lee, M. H., Lin, M. H., Lai, R. J., and Chiu, Y. J. (2010). Development of a low-cost FPGA-based SSVEP BCI multimedia control system. Biomedical Circuits and Systems, IEEE Transactions on, 4(2):125132.
  14. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., and Vaughan, T. M. (2002). Braincomputer interfaces for communication and control. Clin. Neurophysiol., 113(6):767-91.
  15. Woods, R., McAllister, J., Yi, Y., and Lightbody, G. (2008). FPGA-based Implementation of Signal Processing Systems. Wiley.
Download


Paper Citation


in Harvard Style

Woehrle H., Teiwes J., Krell M., Kirchner E. and Kirchner F. (2013). A Dataflow-based Mobile Brain Reading System on Chip with Supervised Online Calibration - For Usage without Acquisition of Training Data . In Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX, ISBN 978-989-8565-80-8, pages 46-53. DOI: 10.5220/0004637800460053


in Bibtex Style

@conference{neurotechnix13,
author={Hendrik Woehrle and Johannes Teiwes and Mario Michael Krell and Elsa Andrea Kirchner and Frank Kirchner},
title={A Dataflow-based Mobile Brain Reading System on Chip with Supervised Online Calibration - For Usage without Acquisition of Training Data},
booktitle={Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,},
year={2013},
pages={46-53},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004637800460053},
isbn={978-989-8565-80-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,
TI - A Dataflow-based Mobile Brain Reading System on Chip with Supervised Online Calibration - For Usage without Acquisition of Training Data
SN - 978-989-8565-80-8
AU - Woehrle H.
AU - Teiwes J.
AU - Krell M.
AU - Kirchner E.
AU - Kirchner F.
PY - 2013
SP - 46
EP - 53
DO - 10.5220/0004637800460053