Abstract

This paper describes a sensory system for implementing a human–computer interface based on bio-potential signal acquisition. The designed acquisition system captures electro oculogram, EEG and transmits them via the RF protocol to a nearby robot. The human machine interface developed will assist a partially paralyzed patients suffering from stoke, etc by helping them to lift the objects in the vicinity of the patient by using the bio-potentials ,thereby controlling the movement of the designed robot. The data acquired are analyzed in real time using a FPGA-based platform. By exploiting the qualities of MULTISIM functionality, design complexity, the design time of these signal circuits can be significantly reduced. Also a comparative analysis is done to prove the design of the hardware developed is accurate to an extent of 90%. The paper aims at the verification and analysis of the developed/designed smart sensors with the design employed using MULTISIM package developed by NI-lab VIEW.

References

• S. Venkataramanan, P. Prabhat, S. R. Choudhury, H. B. Nemade, and J. S. Sahambi, “Biomedical instrumentation based on Electrooculogram (EOG) signal processing and application to a hospital alarm system,” in Proceedings of the 2nd IEEE International Conference on Intelligent Sensing and Information Processing (ICISIP '05), pp. 535–540, Chennai, India, January 2005.
• R. Barae, L. Boquete, and M. Mazo, “System for assisted mobility using eye movements based on electrooculography,” IEEE Transaction on Neural Systems and Rehabilitation Engineering, vol. 10, no. 4, pp. 209–218, 2002.
• Y. Kim, N. L. Doh, Y. Youm, and W. K. Chung, “Robust discrimination method of the electrooculogram signals for human-computer interaction controlling mobile robot,” Intelligent Automation and Soft Computing, vol. 13, no. 3, pp. 319–336, 2007.
• Z. Lv, X. Wu, M. Li, and C. Zhang, “Implementation of the EOG-based human computer interface system,” in Proceedings of the 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '08), pp. 2188–2191, Shanghai, China, May 2008.
• C. K. Young and M. Sasaki, “Mobile robot control by neural network EOG gesture recognition,” in Proceedings of the 8th International Conference on Neural Information Processing, vol. 1, pp. 322–328, 2001.
• Y. Chen and W. S. Newman, “A human-robot interface based on electrooculography,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '04), pp. 243–248, New Orleans, La, USA, April 2004.
• D. Kumar and E. Poole, “Classification of EOG for human computer interface,” in Proceedings of the 2nd Joint IEEE Annual International Conference of the Engineering in Medicine and Biology Jointly with the 24th Annual Conference of the Biomedical Engineering Society (BMES/EMBS '02), vol. 1, pp. 64–67, Houston, Tex, USA, October 2002.
• F. Cincotti, D. Mattia, F. Aloise, et al., “Non-invasive brain-computer interface system: towards its application as assistive technology,” Brain Research Bulletin, vol. 75, no. 6, pp. 796–803, 2008.
• A. B. Usakli and S. Gurkan, “Design of a novel efficient human computer interface: an electrooculagram based virtual keyboard,” submitted to IEEE Transactions on Instrumentation and Measurement.
• F. De Vico Fallani, L. Astolfi, F. Cincotti, et al., “Cortical Functional Connectivity Networks In Normal And Spinal Cord Injured Patients: Evaluation by Graph Analysis,” Human Brain Mapping, vol. 28, no. 12, pp. 1334–1346, 2007.
• L. Astolfi, F. De Vico Fallani, F. Cincotti, et al., “Imaging Functional Brain Connectivity Patterns From High-Resolution EEG And fMRI Via Graph Theory,” Psychophysology, vol. 44, no. 6, pp. 880–893, 2007.
• L. Astolfi, F. Cincotti, D. Mattia, et al., “Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators,” IEEE Trans on Biomedical Engineering, vol. 55, no. 3, pp. 902–913, 2008.
• M. Oliveri, C. Babiloni, M. M. Filippi, et al., “Influence of the supplementary motor area on primary motor cortex excitability during movements triggered by neutral or emotionally unpleasant visual cues,” Exp Brain Res, vol. 149, no. 2, pp. 214–221, 2003.
• C. Babiloni, F. Babiloni, F. Carducci, et al., “Mapping of early and late human somatosensory evoked brain potentials to phasic galvanic painful stimulation,” Human Brain Mapping, vol. 12, no. 3, pp. 168–179, 2001.
• A. Urbano, C. Babiloni, F. Carducci, L. Fattorini, P. Onorati, and F. Babiloni, “Dynamic functional coupling of high resolution EEG potentials related to unilateral internally triggered one-digit movements,” Electroencephalography and clinical Neurophysiol, vol. 106, pp. 477–487, 1998.