An Overview on SSVEP Based Brain Computer Interfaces
Abstract
BCI is a system for communication between brain and computer, in this process the person or subject is need not to be do actual muscular activities for interaction as sending or receiving messages or commands to the computer. Electroencephalography (EEG) along the scalp is the recording of electrical activity. The system is using EEG signals for the interface with brain to the computer. One out of all visual responses Steady-state visually evoked potentials are visually evoked potentials by an external stimulus flickering at fixed frequency. Research focused on Steady State Visual Evoked Potentials (SSVEP) base BCI widely used because of the excellent signal-to-noise ratio (SNR) and relative immunity to artifacts we can use SSVEP. According to various papers the accuracy of the SSVEP signals is very high i.e. 90 ± 8. The goal of this paper is to overview of SSVEP generated frameworks and their processing. The system needs a headset which can capture EEG signals. Usually for generation of SSVEP signals we need at least 10 channels of the headset. There is need of very little training for use of higher ITR and high accuracy for living environment.
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Xin-an Fan, Ke Jie, Teng Teng, Hongsheng Ding, and Yili Liu, “Using a Head-up Display-Based Steady-State Visually Evoked Potential Brain–Computer Interface to Control a Simulated Vehicle,” IEEE Trans. Human Mach. Syst., vol. 43, no. 2, pp. 161–176VOL. 15, NO. 3, JUNE 2014
Muller, S.M.T.” Using a SSVEP-BCI to command a robotic wheelchair” ; Comput. Eng. Dept., Fed. Univ. of Espirito Santo (UFES), São Mateus, Brazil, Industrial Electronics (ISIE), 2011 IEEE International Symposium on 27-30 June 2011
Jayesh Malik, Priyanka Gupta, Sakshi Bansal, Yajuvendra Rathore:”Steady State Visual Evoked Potential Based Thought Translation Device”
Nikolay Chumerin, Nikolay V. Manyakov, Marijn van Vliet, Arne Robben, Adrien Combaz, and Marc M. Van Hulle” Steady-State Visual Evoked Potential-Based Computer Gaming on a Consumer-Grade EEG Device ” IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, VOL. 5, NO. 2, JUNE 2013
J. Muñoz, O. Henao, J. F. Lopez ,” BCI Games With Motion Capture and its Possibilities in Rehabilitation” Proceedings of the Fifth International Brain-Computer Interface Meeting 2013 DOI:10.3217/978-3-85125-260-6-55
Yijun Wang, Zhiguang Zhang, Xiaorong Gao, Shangkai Gao,” Lead selection for SSVEP-based brain-computer interface”, Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco, CA, USA • September 1-5, 2004
Hartwig Holzapfel, Kai Nickel, Rainer Stiefelhagen,” Implementation and Evaluation of a ConstraintBased Multimodal Fusion System for Speech and 3D Pointing Gestures” Interactive Systems Laboratories Universität Karlsruhe (TH) Germany, German Research Foundation,2004
Alexandre Barachant, St´ephane Bonnet, “Channel Selection Procedure using Riemannian distance for BCI Applications” MINATEC Campus December 17, 2010.
Shangkai Gao, Yijun Wang, Xiaorong Gao, Bo Hong Members, IEEE” Visual and Auditory Brain-Computer Interfaces” September 23, 2013
Pierre Gergondet, Abderrahmane Kheddar, Christoph Hintermuller, Christoph Guger, and Mel Slate “Multitask Humanoid Control with a Brain-Computer Interface: user experiment with HRP-2” European Union FP7 Integrated Project VERE
Bakkama Srinath Reddy, “Evidential Reasoning for Multimodal Fusion in Human Computer Interaction” thesis Master of Applied Science Waterloo, Ontario, Canada, 2007
Sylvain Le Groux, Jonatas Manzolli, Paul F.M.J Verschure, “Disembodied and Collaborative Musical Interaction in the Multimodal Brain Orchestra” SPECS, Universitat Pompeu Fabra, Barcelona, Spain
Nataliya Kos’myna, Franck Tarpin-Bernard,” Evaluation and Comparison of a Multimodal Combination of BCI Paradigms with Consumer-Grade Hardware and Eye Tracking” CHI’13, April 27 – May 2, 2013, Paris, France.
Luis Fernando Nicolas-Alonsoand Jaime Gomez-Gil “Brain Computer Interfaces, a Review” Sensors 2012, 12, 1211-1279; doi:10.3390/s120201211
Danhua Zhu,Jordi Bieger, Gary GarciaMolina, and RonaldM. Aarts” A Survey of StimulationMethods Used in SSVEP-Based BCIs” Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2010, Article ID 702357, 12 pages
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, Article ID 187024,
Rajesh Singla and Haseena B. A.,” Comparison of SSVEP Signal Classification Techniques Using SVM and ANN Models for BCI Applications” International Journal of Information and Electronics Engineering, Vol. 4, No. 1, January 2014
Quan Liu, Kun Chen, Qingsong Ai, Sheng Quan Xie “Review: Recent Development of Signal Processing Algorithms for SSVEP-based Brain Computer Interfaces” Journal of Medical and Biological Engineering, 34(4): 299-309 12 Aug 2013; doi: 10.5405/jmbe.1522
Josef Faller Gernot Mu¨ ller-Putz, Dieter Schmalstieg, Gert Pfurtscheller,” An Application Framework for Controlling an Avatar in a Desktop-Based Virtual Environment via a Software SSVEP Brain–Computer Interface” Presence, Vol. 19, No. 1, February 2010, 25–34
M. Byczuk, P. Poryzała, and A. Materka,” SSVEP-Based Brain-Computer Interface: On the Effect of Stimulus Parameters on VEPs Spectral Characteristics” Springer-Verlag Berlin Heidelberg 2012
Pablo Martinez, Hovagim Bakardjian, and Andrzej Cichocki,” Research Article Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm” Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2007, Article ID 94561
Robert Prueckl, Christoph Guger,” A Brain-Computer Interface Based on Steady State Visual Evoked Potentials for Controlling a Robot” g.tec, Guger Technologies OEG, Sierningstr. 14, 4521 Schiedlberg, Austria
Seyed Navid Resalat, Seyed Kamaledin Setarehdan, ”An Improved SSVEP Based BCI System Using Frequency Domain Feature Classification” American Journal of Biomedical Engineering 2013, 3(1): 1-8 DOI: 10.5923/j.ajbe.20130301.01
http://en.wikipedia.org/wiki/Linear_discriminant_analysis
http://en.wikipedia.org/wiki/Support_vector_machine
http://en.wikipedia.org/wiki/Artificial_neural_network
Alessandro L. Stamatto Ferreira, Leonardo Cunha de Miranda, Erica E. Cunha de Miranda, Sarah Gomes Sakamoto, “A Survey of Interactive Systems based on Brain-Computer Interfaces” SBC Journal on 3D Interactive Systems, volume 4, number 1, 2013
Po-Lei Lee, Chia-Lung Yeh, John Yung-Sung Cheng, Chia-Yen Yang, and Gong-Yau Lan,” An SSVEP Based BCI Using High Duty-Cycle Visual Flicker” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 58, NO. 12, DECEMBER 2011
Luzheng Bi, Xin-An Fan, and Yili Liu,” EEG-Based Brain-Controlled Mobile Robots: A Survey” IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, VOL. 43, NO. 2, MARCH 2013
Yosuke Kimura, Toshihisa Tanaka, Hiroshi Higashi, Naoki Morikawa,” SSVEP-Based Brain–Computer Interfaces Using FSK-Modulated Visual Stimuli” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 10, OCTOBER 2013
Fang-Cheng Lin, John K. Zao, Kuan-Chung Tu, Yijun Wang, Yi-Pai Huang, Che-Wei Chuang, Hen-Yuan Kuo, Yu-Yi Chien, Ching-Chi Chou, Tzyy-Ping Jung,” An SNR Analysis of High-Frequency Steady-State Visual Evoked Potentials from the Foveal and Extrafoveal Regions of Human Retina” 34th Annual International IEEE EMBS Conference. Received April 1, 2012.
Alvaro Rodrigo Fuentes Cabrera,” Feature Extraction and Classification for Brain-Computer Interfaces”, Ph.D. Thesis, Department of Health Science and Technology Aalborg University, Denmark, August, 2009.
Turk, M. Multimodal interaction: A review. Pattern Recognition Lett. (2013), http://dx.doi.org/10.1016/ j.patrec.2013.07.003
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