Online Adaptive Filters to Classify Left and Right Hand Motor Imagery

Kais Belwafi, Ridha Djemal, Fakhreddine Ghaffari, Olivier Romain, Bouraoui Ouni, Sofien Gannouni

2016

Abstract

Sensorimotor rhythms (SMRs) caused by motor imagery are key issues for subject with severe disabilities when controlling home devices. However, the development of such EEG-based control system requires a great effort to reach a high accuracy in real-time. Furthermore, BCIs have to confront with inter-individual variability, imposing to the parameters of the methods to be adapted to each subjects. In this paper, we propose a novel EEG-based solution to classify right and left hands(RH and LH) thoughts. Our approach integrates adaptive filtering techniques customized for each subject during the training phase to increase the accuracy of the proposed system. The validation of the proposed architecture is conducted using existing data sets provided by BCI-competition and then using our own on-line validation platform experienced with four subjects. Common Spatial Pattern (CSP) is used for feature extraction to extract features vector from µ and β bands. These features are classified by the Linear Discriminant Analysis (LDA) algorithm. Our prototype integrates the Open-BCI acquisition system with 8 channels connected to Matlab environment in which we integrated all EEG signal processing including the adaptive filtering. The proposed system achieves 80.5% of classification accuracy, which makes approach a promising method to control an external devices based on the thought of LH and RH movement.

References

  1. Belwafi, K., Djemal, R., Ghaffari, F., and Romain, O. (2014). An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery. In Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2014 IEEE Symposium on, pages 121-126.
  2. Cai, B., Xiao, S., Jiang, L., Wang, Y., and Zheng, X. (2013). A rapid face recognition bci system using single-trial erp. In Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on, pages 89-92.
  3. Duan, S., Xu, T., Zhuang, W., and Mao, D. (2014). The feature extraction of erd/ers signals based on the wavelet package and ICA. In Intelligent Control and Automation (WCICA), 2014 11th World Congress on, pages 5621-5625.
  4. Hajimani, E., Ruano, C., Graca Ruano, M., and Ruano, A. (2013). A software tool for intelligent cva diagnosis by cerebral computerized tomography. In Intelligent Signal Processing (WISP), 2013 IEEE 8th International Symposium on, pages 103-108.
  5. Kais, B., Ghaffari, F., Romain, O., and Djemal, R. (2014). An embedded implementation of home devices control system based on brain computer interface. In Microelectronics (ICM), 2014 26th International Conference on, pages 140-143.
  6. Koo, B., Nam, Y., and Choi, S. (2014). A hybrid eogp300 Bci with dual monitors. In Brain-Computer Interface (BCI), 2014 International Winter Workshop on, pages 1-4.
  7. Lotte, F., Congedo, M., Lcuyer, A., Lamarche, F., and Arnaldi, B. (2007). A review of classification algorithms for eeg-based braincomputer interfaces. Journal of Neural Engineering, 4(2):R1.
  8. Lotte, F. and Guan, C. (2011). Regularizing common spatial patterns to improve bci designs: Unified theory and new algorithms. Biomedical Engineering, IEEE Transactions on, 58(2):355-362.
  9. McFarland, D. J., Sarnacki, W. A., and Wolpaw, J. R. (2003). Braincomputer interface (bci) operation: optimizing information transfer rates. Biological Psychology, 63(3):237 - 251.
  10. Onishi, A. and Natsume, K. (2013). A new online event related potential based brain-computer interfaces using an ensemble classifier. In Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on, pages 1033-1036.
  11. Robinson, N., Vinod, A., Guan, C., Ang, K. K., and Peng, T. K. (2011). A wavelet-csp method to classify hand movement directions in eeg based bci system. In Information, Communications and Signal Processing (ICICS) 2011 8th International Conference on, pages 1-5.
  12. Sannelli, C., Vidaurre, C., Muller, K., and Blankertz, B. (2010). Common spatial pattern patches - an optimized filter ensemble for adaptive brain-computer interfaces. In Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, pages 4351-4354.
  13. Yu, T., Xiao, J., Wang, F., Zhang, R., Gu, Z., Cichocki, A., and Li, Y. (2015). Enhanced motor imagery training using a hybrid bci with feedback. Biomedical Engineering, IEEE Transactions on, 62(7):1706-1717.
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Paper Citation


in Harvard Style

Belwafi K., Djemal R., Ghaffari F., Romain O., Ouni B. and Gannouni S. (2016). Online Adaptive Filters to Classify Left and Right Hand Motor Imagery . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: Smart-BIODEV, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 335-339. DOI: 10.5220/0005846503350339


in Bibtex Style

@conference{smart-biodev16,
author={Kais Belwafi and Ridha Djemal and Fakhreddine Ghaffari and Olivier Romain and Bouraoui Ouni and Sofien Gannouni},
title={Online Adaptive Filters to Classify Left and Right Hand Motor Imagery},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: Smart-BIODEV, (BIOSTEC 2016)},
year={2016},
pages={335-339},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005846503350339},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: Smart-BIODEV, (BIOSTEC 2016)
TI - Online Adaptive Filters to Classify Left and Right Hand Motor Imagery
SN - 978-989-758-170-0
AU - Belwafi K.
AU - Djemal R.
AU - Ghaffari F.
AU - Romain O.
AU - Ouni B.
AU - Gannouni S.
PY - 2016
SP - 335
EP - 339
DO - 10.5220/0005846503350339