Abstract

The Brain Computer Interface (BCI) systems enable unblessed people to operate devices and applications through their mental activities. It is believed that the BCI technology should be a blessing for the unblessed persons who may be suffering from severe neuromuscular disorders. So in this paper, we present a review on the progress of research efforts and then we analyze the challenges in BCI research and development for unblessed people. Here, a general Electro-Encephalogram (EEG) based BCI system is discussed which can assist the paralyzed or physically or mentally challenged people in performing their various routine tasks or applications.

References

• F. Lotte, Y. Renard, A. Lécuyer, “Self-paced Brain-Computer Interaction with Virtual Worlds: a Quantitative and Qualitative Study “Out of the Lab”, in 4th International Brain-Computer Interface Workshop and Training Course, pp. 373-378, Austria, 2008.
• Li Yi?Fan Yingle, Tong Qinye, “EEG Feature Detection and Classification Algorithm in Brain-Computation Interface”, 3rd IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1403 – 1407, Singapore, June 3-5, 2008.
• Gary N. Garcia, Touradj Ebrahimi and Jean-Marc Vesin, “Support Vector EEG Classification in the Fourier and Time-Frequency Correlation Domains”, Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering, pp. 591-594, Italy, March 20-22, 2003.
• E. Yom-Tov, G. F. Inbar, “Selection of Relevant Features for Classification of Movements from Single Movement-Related Potentials Using a Genetic Algorithm”, Proceedings of the 23rd Annual EMBS International Conference, pp. 1364 – 1366, Turkey, October 25-28, 2001.
• Li Ming-Ai; Wang Rui; Hao Dong-Mei; Yang Jin-Fu, “Feature Extraction and Classification of Mental EEG for Motor Imagery”, Proceedings of the 2009 Fifth International Conference on Natural Computation, pp. 139 – 143, Tianjin, Aug 14-16,2009 .
• Hubert Cecotti, Axel Graeser, “Convolutional Neural Network with embedded Fourier Transform for EEG Classification”, 19th International Conference on Pattern Recognition, ICPR, pp. 1-4, Tampa, FL,Dec 8-11,2008.
• Gabriel Pires, Urbano Nunes and Miguel Castelo-Branco, “Single-Trial EEG Classification of Movement Related Potential”, Proceedings of 10th IEEE International Conference on Rehabilitation Robotics, pp. 569 – 574, Noordwijk, June 13-15, 2007.
• Febo Cincotti, et al., “Non-Invasive Brain-Computer Interface System to Operate Assistive Devices”, Proceedings of 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2532 - 2535, Lyon, France, Aug 23-26, 2007.
• S. R. Liyanage, J. -X. Xu, C. Guan, K. K. Ang, C. S. Zhang and T. H. Lee, “Classification of Self-paced Finger Movements with EEG Signals Using Neural Network and Evolutionary Approaches”, IEEE International Conference on Control and Automation, pp. 1807 – 1812, Christchurch, New Zealand, Dec 9-11, 2009
• Boqiang Liu, Mingshi Wang, Tonglei Li, Zhongguo Liu, “Identification and Classification for finger movement based on EEG”, Proceedings of 27th Annual Conference of the IEEE Engineering in Medicine and Biology, pp. 5408 – 5411, Shanghai, China, Sept. 1-4, 2005
• I. Navarro, F. Sepulveda, B. Hubais, “A Comparison of Time, Frequency and ICA Based Features and Five Classifiers for Wrist Movement Classification in EEG Signals”, Proceedings of 27th Annual Conference of the IEEE Engineering in Medicine and Biology, pp. 2118 – 2121, Shanghai, China, Sept. 1-4, 2005
• D.A. Craig, H.T. Nguyen, H.A. Burchey, “Two Channel EEG Thought Pattern Classifier”, Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1291 – 1294, New York City, USA, Aug 30-Sept 3, 2006.
• Yuan-Pin Lin, et al., “Multilayer Perceptron for EEG Signal Classification during Listening to Emotional Music”, IEEE Region 10 Conference, TENCON,pp. 1 - 3 ,Taipei, Oct. 30 2007-Nov.2 2007
• Jussi Virkkala, et al., “Automatic Sleep Stage Classification Using Two Facial Electrodes”, 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1643 – 1646, Vancouver, British Columbia, Canada, August 20-24, 2008
• Zheng Yang Chin, Kai Keng Ang, Cuntai Guan, “Multiclass Voluntary Facial Expression Classification based on Filter Bank Common Spatial Pattern”, 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1005 - 1008, Vancouver, British Columbia, Canada, August 20-24, 2008
• Mu Li and Bao-Liang Lu, “Emotion Classification Based on Gamma-band EEG”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1223- 1226, Minneapolis, Sept. 3-6, 2009.
• Dong Ming, et al., “Study on EEG-Based Mouse System by Using Brain-Computer Interface”, International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems, pp. 236 – 239, Hong Kong, China May 11-13, 2009.
• Hiroshi Higashi, Toshihisa Tanaka, and Arao Funase, “Classification of Single Trial EEG during Imagined Hand Movement by Rhythmic Component Extraction”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2482 - 2485, Minneapolis, Sept. 3-6, 2009.
• Adrien Combaz, et al., “Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling”, International Conference on Machine Learning and Applications, pp. 386 – 391, Miami Beach, FL , Dec 13-15, 2009.
• Mohammad Ahmadi and Abbas Erfanian, “An On-Line BCI System for Hand Movement Control Using Real-Time Recurrent Probabilistic Neural Network”, Proceedings of the 4th International IEEE Conference on Neural Engineering, pp. 367 – 370, Antalya, Turkey, April 29 - May 2, 2009
• T. Hinterberger, et al., “A device for the detection of cognitive brain functions in completely paralyzed or unresponsive patients”, IEEE Transactions on Biomedical Engineering, vol. 52, no.2, pp. 211 -220, Feb 2005.
• D Pérez-Marcos, J A Buitrago, F D Giraldo, Velásquez, “Writing through a robot: a proof of concept for a brain-machine interface”, Med Eng Phys, vol. 33, no. 10, pp.1314-7, Dec 2011.
• G.G. Gentiletti, et al., “Command of a simulated wheelchair on a virtual environment using a brain-computer interface”, IRBM, vol 30, no. 5, pp. 218-225, 2009.
• Y. P. A. Yong, N. J. Hurley, and G. C. M. Silvestre, “Single-trial EEG classification for brain-computer interface using wavelet decomposition”, Mendeley Brain, vol. 3, no. 2, pp. 194-197, 2005.
• Manoj Thulasidas, Cuntai Guan, and Jiankang Wu, “Robust Classification of EEG Signal for Brain–Computer Interface”, IEEE Transactions on Neural Systems And Rehabilitation Engineering, vol. 14, no. 1,pp. 24-29, March 2006
• A. Cichocki and S. Sanei, “EEG/MEG Signal Processing”, Journal of Computational Intelligence and Neuroscience, vol. 2007, Article ID 97026, 2007.
• Matthew Middendorf, Grant McMillan, Gloria Calhoun, and Keith S. Jones, “Brain–Computer Interfaces Based on the Steady-State Visual-Evoked Response”, IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2,pp. 211 – 214, JUNE 2000.
• Wanpracha Art Chaovalitwongse, Ya-Ju Fan, Rajesh C. Sachdeo, “On the Time Series K-Nearest Neighbor Classification of Abnormal Brain Activity”, IEEE Transactions on Systems, Man, And Cybernetics—Part A: Systems And Humans, vol. 37, no. 6, pp. 1005 – 1016, November 2007.
• A Ferreira et al., “Human-Machine Interface Based on Muscular and Brain Signals Applied to a Robotic Wheelchair”, Journal of Physics, vol.90, 2007.
• ZHANG Zhen, FAN Hong-liang, “Prosthetic Controlled System Based on Signal Pattern Recognition of Electroencephalogram”, International Seminar on Future BioMedical Information Engineering, pp. 287 – 290, Dec 18, 2008.
• Syed M. Saddique and Laraib Hassan Siddiqui, “EEG Based Brain Computer Interface”, Journal of Software, vol. 4, no 6, pp 550-554, Aug 2009.
• Md. R. Ahsan, Md. R. Ahsan, Md. R. Ahsan, “EMG Signal Classification for Human Computer Interaction: A Review”, European Journal of Scientific Research, vol.33, no.3, pp.480-501, 2009.
• J.R., Wolpaw, et.al.,“Brain–Computer Interface Technology: A Review of the First International Meeting”, IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 164 – 173, June 2000
• Torsten Felzer, “On the Possibility of Developing a Brain-Computer Interface (BCI)”, Technical Report, Technical University of Darmstadt, Department of Computer Science, Alexanderstr. 10, D-64283 Darmstadt, Germany, 2001.
• Jorge Baztarrica Ochoa, “EEG Signal Classification for Brain Computer Interface Applications”, Ph.D Dissertation, ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE, March 28th, 2002.
• Dr. Andrzej Cichocki, “Human / Brain Computer Interfaces: Challenging puzzles to solve” , http://www.brain.riken.jp/bsi-news/bsinews22/no22/networke.html, Nov 2003
• Scott Makeig, “PCA vs ICA”, http://sccn.ucsd.edu/pipermail/eeglablist/2003/000145.html, Wed Oct 8