Prediction of Movements by Online Analysis of Electroencephalogram with Dataflow Accelerators

Hendrik Wöhrle, Johannes Teiwes, Marc Tabie, Anett Seeland, Elsa Andrea Kirchner, Frank Kirchner

2014

Abstract

Brain Computer Interfaces (BCIs) allow to use psychophysiological data for a large range of innovative applications. One interesting application for rehabilitation robotics is to modulate exoskeleton controls by predicting movements of a human user before they are actually performed. However, usually BCIs are used mainly in artificial and stationary experimental setups. Reasons for this are, among others, the immobility of the utilized hardware for data acquisition, but also the size of the computing devices that are required for the analysis ofthe human electroencephalogram. Therefore, mobile processing devices need to be developed. A problem is often the limited processing power of these devices, especially if there are firm time constraints as in thecase of movement prediction. Field programmable gate array (FPGA)-based application-specific dataflow accelerators are a possible solution here. In this paper we present the first FPGA-based processing system that is able to predict upcoming movements by analyzing the human electroencephalogram. We evaluate the system regarding computation time and classification performance and show that it can compete with a standard desktop computer.

References

  1. Ahmadian, P., Cagnoni, S., and Ascari, L. (2013). How capable is non-invasive EEG data of predicting the next movement? A mini review. Frontiers in Human Neuroscience.
  2. Bai, O., Rathi, V., Lin, P., Huang, D., Battapady, H., Fei, D.- Y., Schneider, L., Houdayer, E., Chen, X., and Hallett, M. (2011). Prediction of human voluntary movement before it occurs. Clinical Neurophysiology.
  3. Chi, Y. M., Wang, Y.-T., Wang, Y., Maier, C., Jung, T.-P., and Cauwenberghs, G. (2012). Dry and noncontact EEG sensors for mobile brain-computer interfaces. Neural Systems and Rehabilitation Engineering, IEEE Transactions on.
  4. Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., and Singer, Y. (2006). Online passive-aggressive algorithms. J. Mach. Learn. Res.
  5. Folgheraiter, M., Jordan, M., Straube, S., Seeland, A., Kim, S. K., and Kirchner, E. A. (2012). Measuring the improvement of the interaction comfort of a wearable exoskeleton. International Journal of Social Robotics.
  6. Folgheraiter, M., Kirchner, E. A., Seeland, A., Kim, S. K., M., J., Woehrle, H., Bongardt, B., Schmidt, S., Albiez, J., and Kirchner, F. (2011). A multimodal brain-arm interface for operation of complex robotic systems and upper limb motor recovery. In Proceedings of the 4th International Conference on Biomedical Electronics and Devices (BIODEVICES-11).
  7. Jones, E., Oliphant, T., and Peterson, P. (2001). SciPy: Open source scientific tools for Python.
  8. Khurana, K., Gupta, P., Panicker, R. C., and Kumar, A. (2012). Development of an FPGA-based real-time P300 speller. In 22nd International Conference on Field Programmable Logic and Applications (FPL).
  9. Kirchner, E. A., Albiez, J., Seeland, A., Jordan, M., and Kirchner, F. (2013a). Towards assistive robotics for home rehabilitation. In Proceedings of the 6th International Conference on Biomedical Electronics and Devices (BIODEVICES-13), Barcelona.
  10. Kirchner, E. A., Kim, S. K., Straube, S., Seeland, A., Woehrle, H., Krell, M. M., Tabie, M., and Fahle, M. (2013b). On the applicability of brain reading for predictive human-machine interfaces in robotics. PLoS ONE.
  11. Kirchner, E. A., Tabie, M., and Seeland, A. (2013c). Multimodal movement prediction - towards an individual assistance of patients. PLoS ONE.
  12. Krell, M. M., Straube, S., Seeland, A., Woehrle, H., Teiwes, J., Metzen, J. H., Kirchner, E. A., and Kirchner, F. (2013). pySPACE - A Signal Processing and Classification Environment in Python. Frontiers in Neuroinformatics.
  13. Lew, E., Chavarriaga, R., Silvoni, S., and Millán, J. D. R. (2012). Detection of self-paced reaching movement intention from EEG signals. Frontiers in Neuroengineering, 5:13.
  14. Linaro (2013). Open source software for ARM SoCs. Technical report.
  15. OpenMP (2014).
  16. Pfurtscheller, G. (2000). Brain oscillations control hand orthosis in a tetraplegic. Neuroscience Letters, 292(3):211-214.
  17. Rivet, B., Souloumiac, A., Attina, V., and Gibert, G. (2009). xDAWN algorithm to enhance evoked potentials: Application to brain computer interface. Biomedical Engineering, IEEE Transactions on.
  18. Seeland, A., Woehrle, H., Straube, S., and Kirchner, E. A. (2013). Online movement prediction in a robotic application scenario. In 6th International IEEE/EMBS Conference on Neural Engineering (NER).
  19. Shyu, K.-K., Chiu, Y.-J., Lee, P.-L., Lee, M.-H., Sie, J.-J., Wu, C.-H., Wu, Y.-T., and Tung, P.-C. (2013). Total design of an FPGA-based brain-computer interface control hospital bed nursing system. Industrial Electronics, IEEE Transactions on.
  20. Wang, Y.-T., Wang, Y., Cheng, C.-K., and Jung, T.-P. (2013). Developing stimulus presentation on mobile devices for a truly portable SSVEP-based BCI. In Engineering in Medicine and Biology Society (EMBC), 35th Annual International Conference of the IEEE.
  21. Webb, J., Xiao, Z.-G., Aschenbrenner, K. P., Herrnstadt, G., and Menon, C. (2012). Towards a portable assistive arm exoskeleton for stroke patient rehabilitation controlled through a brain computer interface. In Biomedical Robotics and Biomechatronics (BioRob), 4th IEEE RAS EMBS International Conference on, pages 1299-1304.
  22. Xilinx Corporation (2014). UG479: 7 Series DSP48E1 Slice User Guide.
Download


Paper Citation


in Harvard Style

Wöhrle H., Teiwes J., Tabie M., Seeland A., Kirchner E. and Kirchner F. (2014). Prediction of Movements by Online Analysis of Electroencephalogram with Dataflow Accelerators . In Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX, ISBN 978-989-758-056-7, pages 31-37. DOI: 10.5220/0005139400310037


in Bibtex Style

@conference{neurotechnix14,
author={Hendrik Wöhrle and Johannes Teiwes and Marc Tabie and Anett Seeland and Elsa Andrea Kirchner and Frank Kirchner},
title={Prediction of Movements by Online Analysis of Electroencephalogram with Dataflow Accelerators},
booktitle={Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,},
year={2014},
pages={31-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005139400310037},
isbn={978-989-758-056-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,
TI - Prediction of Movements by Online Analysis of Electroencephalogram with Dataflow Accelerators
SN - 978-989-758-056-7
AU - Wöhrle H.
AU - Teiwes J.
AU - Tabie M.
AU - Seeland A.
AU - Kirchner E.
AU - Kirchner F.
PY - 2014
SP - 31
EP - 37
DO - 10.5220/0005139400310037