Spatial Artifact Detection for Multi-channel EMG-based Speech Recognition

Till Heistermann, Matthias Janke, Michael Wand, Tanja Schultz

2014

Abstract

We introduce a spatial artifact detection method for a surface electromyography (EMG) based speech recognition system. The EMG signals are recorded using grid-shaped electrode arrays affixed to the speakers face. Continuous speech recognition is performed on the basis of these signals. As the EMG data are highdimensional, Independent Component Analysis (ICA) can be applied to separate artifact components from the content-bearing signal. The proposed artifact detection method classifies the ICA components by their spatial shape, which is analyzed using the spectra of the spatial patterns of the independent components. Components identified as artifacts can then be removed. Our artifact detection method reduces the word error rates (WER) of the recognizer significantly. We observe a slight advantage in terms of WER over the temporal signal based artifact detection method by (Wand et al., 2013a).

References

  1. Bell, A. J. and Sejnowski, T. I. (1995). An InformationMaximization Approach to Blind Separation and Blind Deconvolution. Neural Computation, 7:1129 - 1159.
  2. Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., and Müller, K. (2008). Optimizing Spatial Filters for Robust EEG Single-Trial Analysis. Signal Processing Magazine, IEEE, 25(1):41-56.
  3. Cardoso, J.-F. (1998). Blind Signal Separation: Statistical Principles. Proc. IEEE, 9(10):2009 - 2025.
  4. Cavanagh, P. and Komi, P. (1979). Electromechanical Ddelay in Human Skeletal Muscle Under Concentric and Eccentric Contractions. European journal of applied physiology and occupational physiology, 42(3):159- 163.
  5. Delorme, A. and Makeig, S. (2004). EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics including Independent Component Analysis. Journal of Neuroscience Methods, 134(1):9-21.
  6. Denby, B., Schultz, T., Honda, K., Hueber, T., and Gilbert, J. (2010). Silent Speech Interfaces. Speech Communication, 52(4):270 - 287.
  7. Deng, Y., Colby, G., Heaton, J. T., and Meltzner, G. S. (2012). Signal Processing Advances for the MUTE sEMG-Based Silent Speech Recognition System. In Military Communication Conference, MILCOM 2012, pages 1-6. IEEE.
  8. Hyvärinen, A. and Oja, E. (2000). Independent Component Analysis: Algorithms and Applications. Neural Networks, 13:411 - 430.
  9. Jorgensen, C. and Dusan, S. (2010). Speech Interfaces based upon Surface Electromyography. Speech Communication, 52:354 - 366.
  10. Jorgensen, C., Lee, D., and Agabon, S. (2003). Sub Auditory Speech Recognition Based on EMG/EPG Signals. In Proceedings of International Joint Conference on Neural Networks (IJCNN), pages 3128 - 3133.
  11. Jou, S.-C., Schultz, T., Walliczek, M., Kraft, F., and Waibel, A. (2006). Towards Continuous Speech Recognition using Surface Electromyography. In Proc. Interspeech, pages 573 - 576.
  12. Jou, S.-C. S., Schultz, T., and Waibel, A. (2007). Continuous Electromyographic Speech Recognition with a Multi-Stream Decoding Architecture. In Proc. ICASSP, pages IV-401 - IV-404.
  13. Jung, T.-P., Makeig, S., Humphries, C., Lee, T.-W., Mckeown, M. J., Iragui, V., and Sejnowski, T. J. (2000). Removing Electroencephalographic Artifacts by Blind Source Separation. Psychophysiology, 37(2):163- 178.
  14. Kubo, T., Yoshida, M., Hattori, T., and Ikeda, K. (2013). Shift Invariant Feature Extraction for sEMG-Based Speech Recognition With Electrode Grid. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pages 5797-5800. IEEE.
  15. Lee, K.-F. (1989). Automatic Speech Recognition: The Development of the SPHINX System. Kluwer Academic Publishers.
  16. Metze, F. and Waibel, A. (2002). A Flexible Stream Architecture for ASR Using Articulatory Features. In Proc. ICSLP, pages 2133 - 2136.
  17. Nakamura, H., Yoshida, M., Kotani, M., Akazawa, K., and Moritani, T. (2004). The Application of Independent Component Analysis to the Multi-Channel Surface Electromyographic Signals for Separation of Motor Unit Action Potential Trains: Part II-Modelling Interpretation. Journal of Electromyography and Kinesiology, 14(4):433-441.
  18. Qiao, Z., Zhou, L., and Huang, J. Z. (2009). Sparse Linear Discriminant Analysis with Applications to High Dimensional Low Sample Size Data. International Journal of Applied Mathematics, 39:48 - 60.
  19. Ren, X., Hu, X., Wang, Z., and Yan, Z. (2006). MUAP Extraction and Classification Based on Wavelet Transform and ICA for EMG Decomposition. Medical and Biological Engineering and Computing, 44(5):371- 382.
  20. Schultz, T. and Wand, M. (2010). Modeling Coarticulation in Large Vocabulary EMG-based Speech Recognition. Speech Communication, 52(4):341 - 353.
  21. Viola, F., Thorne, J., Edmonds, B., Schneider, T., Eichele, T., Debener, S., et al. (2009). Semi-Automatic Identification of Independent Components Representing EEG Artifact. Clinical Neurophysiology, 120(5):868- 877.
  22. Wand, M., Himmelsbach, A., Heistermann, T., Janke, M., and Schultz, T. (2013a). Artifact Removal Algorithm for an EMG-based Silent Speech Interface. In Proc. of the 2013 IEEE Engineering in Medicine and Biology 35th Annual Conference.
  23. Wand, M., Schulte, C., Janke, M., and Schultz, T. (2013b). Array-based Electromyographic Silent Speech Interface. In Proc. Biosignals.
  24. Wand, M. and Schultz, T. (2011). Session-independent EMG-based Speech Recognition. In Proc. Biosignals, pages 295 - 300.
  25. Zhao, H. and Xu, G. (2011). The Research on Surface Electromyography Signal Effective Feature Extraction. In Proc. of the 6th International Forum on Strategic Technology.
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Paper Citation


in Harvard Style

Heistermann T., Janke M., Wand M. and Schultz T. (2014). Spatial Artifact Detection for Multi-channel EMG-based Speech Recognition . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014) ISBN 978-989-758-011-6, pages 189-196. DOI: 10.5220/0004793901890196


in Bibtex Style

@conference{biosignals14,
author={Till Heistermann and Matthias Janke and Michael Wand and Tanja Schultz},
title={Spatial Artifact Detection for Multi-channel EMG-based Speech Recognition},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)},
year={2014},
pages={189-196},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004793901890196},
isbn={978-989-758-011-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)
TI - Spatial Artifact Detection for Multi-channel EMG-based Speech Recognition
SN - 978-989-758-011-6
AU - Heistermann T.
AU - Janke M.
AU - Wand M.
AU - Schultz T.
PY - 2014
SP - 189
EP - 196
DO - 10.5220/0004793901890196