Intelligent robot chair with communication aid using TEP responses and higher order spectra band features

In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.

1. A. J. Lees, N. A. Blackburn, and V. L. Campbell, ‘The nighttime problems of Parkinson’s disease.’, Clin. Neuropharmacol., vol. 11, no. 6, pp. 512–519, Dec. 1988.

2. D. Lacomis, J. Terry Petrella, and M. J. Giuliani, ‘Causes of neuromuscular weakness in the intensive care unit: A study of ninety-two patients’, Muscle and Nerve, vol. 21, no. 5, pp. 610–617, May 1998.

3. J. R. Millan, F. Renkens, J. Mouriño, and W. Gerstner, ‘Noninvasive brain-actuated control of a mobile robot by human EEG’, IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1026–1033, 2004.

4. J. Philips et al., ‘Adaptive shared control of a brain-actuated simulated wheelchair’, in 2007 IEEE 10th International Conference on Rehabilitation Robotics, 2007, pp. 408–414.

5. W. Speier, C. Arnold, J. Lu, A. Deshpande, and N. Pouratian, ‘Integrating language information with a hidden Markov model to improve communication rate in the P300 speller’, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 3, pp. 678–684, 2014.

6. H. Gürkök and A. Nijholt, ‘Brain–Computer Interfaces for Multimodal Interaction: A Survey and Principles’, Int. J. Hum. Comput. Interact., vol. 28, no. 5, pp. 292–307, Jun. 2011.

7. R. Näätänen, T. Kujala, and G. Light, Mismatch negativity: A window to the brain. Oxford University Press, 2019.

8. L. J. Trejo, R. Rosipal, and B. Matthews, ‘Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials’, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 14, no. 2, pp. 225–229, 2006.

9. P. Wang, J. Shen, and J. Shi, ‘Feature extraction of eeg for imagery left-right hands movement’, Chinese J. Sensors Actuators, vol. 9, 2010.

10. J. D. Loeser, R. G. Black, and A. Christman, ‘Relief of pain by transcutaneous stimulation’, J. Neurosurg., vol. 42, no. 3, pp. 308–314, 1975.

11. R. S. Armiger et al., ‘A real-time virtual integration environment for neuroprosthetics and rehabilitation’, Johns Hopkins APL Tech. Dig., vol. 30, no. 3, pp. 198–206, 2011.

12. S. Machado et al., ‘EEG-based brain-computer interfaces: an overview of basic concepts and clinical applications in neurorehabilitation’, Rev. Neurosci., vol. 21, no. 6, pp. 451–468, 2010.

13. M. V. M. Yeo, X. Li, K. Shen, and E. P. V Wilder-Smith, ‘Can SVM be used for automatic EEG detection of drowsiness during car driving?’, Saf. Sci., vol. 47, no. 1, pp. 115–124, 2009.

14. C.-T. Lin, F.-C. Lin, S.-A. Chen, S.-W. Lu, T.-C. Chen, and L.-W. Ko, ‘EEG-based brain-computer interface for smart living environmental auto-adjustment’, J. Med. Biol. Eng., vol. 30, no. 4, pp. 237–245, 2010.

15. T. Kaufmann, A. Herweg, and A. Kübler, ‘Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials’, J. Neuroeng. Rehabil., vol. 11, no. 1, p. 7, 2014.

16. S. K. Nataraj, M. P. Paulraj, S. Bin Yaacob, and A. H. Adom, ‘Performance comparison of TEP and VEP responses using bispectral estimation to command an intelligent robot chair with communication aid’, Indian J. Sci. Technol., vol. 8, no. 20, 2015.

17. F. H. Guenther et al., ‘A wireless brain-machine interface for real-time speech synthesis’, PLoS One, vol. 4, no. 12, p. e8218, 2009.

18. A. Porbadnigk, M. Wester, J. Calliess, and T. Schultz, ‘EEG-based speech recognition impact of temporal effects’, 2nd Int. Conf. Bio-inspired Syst. Signal Process., 2009.

19. K. Stamps and Y. Hamam, ‘Towards inexpensive BCI control for wheelchair navigation in the enabled environment–a hardware survey’, in In International Conference on Brain Informatics, 2010, pp. 336–345.

20. A. C. Lopes, G. Pires, L. Vaz, and U. Nunes, ‘Wheelchair navigation assisted by Human-Machine shared-control and a P300-based Brain Computer Interface’, in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011, pp. 2438–2444.

21. T. M. Vaughan, J. R. Wolpaw, and E. Donchin, ‘EEG-Based Communication: Prospects and Problems’, IEEE Trans. Rehabil. Eng., vol. 4, no. 4, pp. 425–430, 1996.

22. C. L. Nikias and M. R. Raghuveer, ‘Bispectrum estimation: A digital signal processing framework’, Proc. IEEE, vol. 75, no. 7, pp. 869–891, 1987.

23. C. Guger, B. Allison, and G. Edlinger, Brain-Computer Interface Research: A State-of-the-Art Summary. Springer, 2013.

24. S. K. Nataraj, S. B. Yaacob, M. P. Paulraj, and A. H. Adom, ‘EEG based Intelligent robot chair with communication aid using statistical cross correlation based features’, in 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2014, pp. 12–18.

25. A. Kübler et al., ‘The thought translation device: A neurophysiological approach to communication in total motor paralysis’, Exp. Brain Res., vol. 124, no. 2, pp. 223–232, 1999.

26. M. Teplan, ‘Fundamentals of EEG measurement’, Meas. Sci. Rev., vol. 2, no. 2, pp. 1–11, 2002.

27. D. A. Kaiser, ‘What is quantitative EEG?’, J. Neurother., vol. 10, no. 4, pp. 37–52, 2007.

28. K. Tai, S. Blain, and T. Chau, ‘A review of emerging access technologies for individuals with severe motor impairments.’, Assist. Technol., vol. 20, no. 4, pp. 204–219; quiz 220–221, 2008.

29. R. Ortner, E. Grünbacher, and C. Guger, ‘State of the art in sensors, signals and signal processing’. 2013.

30. M. R. Raghuveer and C. L. Nikias, ‘Bispectrum estimation: A parametric approach’, IEEE Trans. Acoust. Speech Signal Process., vol. 33, no. 5, pp. 1213–1230, 1985.

31. M. P. Paulraj and S. N. Sivanandam, Introduction to Artificial Neural Networks. vikas publishing House PVT LTD, 2003.

32. R. Kohavi, ‘A study of cross-validation and bootstrap for accuracy estimation and model selection’, in International Joint Conference on Arti cial Intelligence (IJCAI), 1995, vol. 14, no. 2, pp. 1137–1145.