Published In : IJCAT Journal Volume 2, Issue 4

Date of Publication : April 2015

Pages : 130 - 134

Figures :01

Tables : 01

Publication Link :A Review on Signal Pre-processing Techniques in Brain Computer Interface

 

 

 

Abhijeet Mallick : Department of Computer Science & Engineering, G.H.R.A.E.T, Nagpur University, India

Deepak Kapgate : Department of Computer Science & Engineering, G.H.R.A.E.T, Nagpur University, India

 

 

 

 

 

 

 

Brain Computer Interface (BCI)

Electroencephalogram (EEG)

Brain Machine Interface (BMI)

A brain computer interface (BCI) is a communications system that enables humans to control various devices by using control signals generated from electroencephalographic (EEG) activity. The signal processing components of a BCI design: (1) signal acquisition, (2) signal pre-processing, (3) feature extraction, (4) feature classification and (5) control interface. The acquired signals may suffer from noise and artifacts because of movement of subjects. But no one give any special attention toward this signal pre-processing process. This paper mostly concentrated on the Preprocessing. Pre-processing algorithms are one of the most primary factors to decide accuracy and to obtain optimal results of BCI system. In this paper, we summarize various signal pre-processing methods and also their advantages and disadvantages.

 

 

 

 

 

 

 

 

 

[1] G. Bin, X. Gao, B. Hong, Z. Yan and S. Gao, “An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method,” Journal of Neural Engineering, vol. 6, no. 4, Article ID 046002, 2009. [2] Puneet Mishra, Sunil Kumar Singla ,”Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition”, MEASUREMENT SCIENCE REVIEW, Volume 13, No. 1, 2013 [3] Yuanqing Li, Fei Wang, Jiahui Pan and Zhuliang Yu, “A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control”, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 11, NOVEMBER 2013 [4] Erwei Yin, Zongtan Zhou, Fanglin Chen, Jun Jiang, Yadong Liu and Dewen Hu “A Speedy Hybrid BCI Spelling Approach Combining P300 and SSVEP”,IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 2, FEBRUARY 2014 473 [5] S. G. Mason and G. E. Birch, “A general framework for brain-computer interface design,” IEEE Trans. Neural Syst. Rehabil. Eng., 11: 70-85, 2003. [6] Y. J. Wang, R. P. Wang, X. R. Gao, B. Hong and S. K. Gao, “A practical VEP-based brain-computer interface,” IEEE Trans. Neural Syst. Rehabil. Eng., 14: 234-239, 2006. [7] Luis Fernando Nicolas-Alonso and Jaime Gomez-Gil,” Brain Computer Interfaces, a Review,” Sensors 2012, 12, 1211-1279; doi:10.3390/s120201211 [8] Setare Amiri, Reza Fazel-Rezai, and Vahid Asadpour.” A Review of Hybrid Brain-Computer Interface Systems”, Hindawi Publishing Corporation Advances in Human-Computer Interaction Volume 2013 [9] G. Y. Bin, X. R. Gao, Z. Yan, B. Hong and S. K. Gao, “An online multi-channel SSVEP-based braincomputer interface using a canonical correlation analysis method,” J. Neural. Eng., 6: 046002, 2009. [10] P.L. Lee, J. J. Sie, Y. J. Liu, C. H. Wu, M. H. Lee, C. H. Shu, P. H. Li, C. W. Sun and K. K. Shyu, “An SSVEP-actuated brain computer interface using phasetagged flickering sequences: a cursor system,” Ann. Biomed. Eng., 38: 2383-2397, 2010. [11] Z. D. Deng, X. Q. Li, K. H. Zheng and W. T. Yao, “A humanoid robot control system with SSVEP-based asynchronous brain-computer interface,” Jiqiren/Robot, 33: 129-135, 2011. [12] G. Garcia-Molina, D. H. Zhu and S. Abtahi, “Phase detection in a visual-evoked-potential based brain computer interface,” Proc.18thEuropean Signal Processing Conf., 949-953, 2010. [13] Zongyuan Zhao, Byeong Ho Kang Mir Md Jahangir Kabir, Shuxiang Xu, “Investigation of Multilayer Perceptron and Class Imbalance Problems for Credit Rating”, International Journal of Computer and Information Technology ,Volume 03– Issue 04, July 2014. [14] Minkyu Ahn, Mijin Lee, Jinyoung Choi and Sung Chan Jun, “A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users”, Sensors 2014. [15] Antara Bhattacharya, Dr. N. G. Bawane, S. M. Nirkhi, Brain Computer Interface Using EEG Signals, and Available at: http://www.schalklab.org/sites/default/files/misc/Abra in-computer interface using electrocorticographic signals in humans.pdf. [16] Mohammed J. Alhaddad, Common Average Reference (CAR) Improves P300 Speller, Int. J. of Engg. and Tech., Vol. 2 No. 3, 2012. [17] Thomas C Ferree and Ramesh Srinivasan, Theory and Calculation of the Scalp Surface Laplacian, Electrical Geodesics, Inc., August 24, 2000. [18] Siyi Deng, William Winter, Samuel Thorpe, Ramesh Srinivasan, EEG Surface Laplacian using realistic head geometry, Int. J. of Bioelectromagnetism, Vol. 13, No. 4, pp. 173-177, 2011. [19] C. Tandonnet, B. Burle, T. Hasbroucq, F. Vidal, Spatial Enhancement of EEG traces by Surface Laplacian Estimation: Comparison between Local and Global Methods, Clinical Neurophysiology 116 (2005) 18–24. [20] Claudio G. Carvalhaes, Patrick Suppes, A Spline Framework for Estimating the EEG Surface Laplacian Using the Euclidean Metric, Neural Computation 23, 2010. [21] Dieter Devlaminck, BartWyns, Moritz Grosse- Wentrup,Georges Otte, and Patrick Santens, Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI, Computational Intell. and Neuroscience Volume 2011. [22] Fabien Lotte and Cuntai Guan, Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms, IEEE Trans. On Biomedical Engg., Vol. 58, No. 2, 2011. [23] A Garcés Correa, E Laciar, M E Valentinuzzi, H D Patiño, Artifact removal from EEG signals using adaptive filters in cascade, 16th Argentine Bioengineering Cong. and the 5th Conf. of Clinical Engg., IOP Publishing J. of Physics: Conf. Series 90 (2007). [24] Chinmay Chandrakar, M.K. Kowar, De-noising ECG Signals Using Adaptive Filter Algorithm, Int. J. of Soft Computing and Engg. (IJSCE), Vol. 2, Issue 1, 2012.