Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs
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Romis de Faissol Attux | Diogo C. Soriano | Luis Coradine | Luisa F. S. Uribe | Glauco F. G. Yared | Sarah N. Carvalho | Thiago B. S. Costa | R. Attux | S. N. Carvalho | T. B. S. Costa | D. Soriano | L. F. S. Uribe | L. Coradine | G. Yared
[1] Dianhui Wang,et al. Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..
[2] C. Herrmann. Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena , 2001, Experimental Brain Research.
[3] D. Regan. Human brain electrophysiology: Evoked potentials and evoked magnetic fields in science and medicine , 1989 .
[4] Ahmed H. Tewfik,et al. Adapting subject specific motor imagery EEG patterns in space-time-frequency for a brain computer interface , 2009, Biomed. Signal Process. Control..
[5] Gary Garcia-Molina,et al. Optimal spatial filtering for the steady state visual evoked potential: BCI application , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.
[6] Ivan Volosyak,et al. Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.
[7] Cuntai Guan,et al. Improving session-to-session transfer performance of motor imagery-based BCI using adaptive extreme learning machine , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[8] B. Allison,et al. BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI? , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[9] L.J. Trejo,et al. 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 , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[10] Christopher J. C. Burges,et al. A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.
[11] Desney S. Tan,et al. Brain-Computer Interfacing for Intelligent Systems , 2008, IEEE Intelligent Systems.
[12] Kwang Suk Park,et al. Frequency recognition methods for dual-frequency SSVEP based brain-computer interface , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[13] K. Jellinger. Toward Brain-Computer Interfacing , 2009 .
[14] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[15] P. Welch. The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .
[16] C. Neuper,et al. Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges , 2010, Front. Neurosci..
[17] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[18] Zhen Yang,et al. A Voting Optimized Strategy Based on ELM for Improving Classification of Motor Imagery BCI Data , 2014, Cognitive Computation.
[19] Andrzej Cichocki,et al. Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm , 2007, Comput. Intell. Neurosci..
[20] Yijun Wang,et al. VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier] , 2009, IEEE Computational Intelligence Magazine.
[21] G. Pfurtscheller,et al. Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.
[22] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[23] Ross Cunnington,et al. Stimulus specificity of a steady-state visual-evoked potential-based brain–computer interface , 2012, Journal of neural engineering.
[24] Dong Ming,et al. Time-locked and phase-locked features of P300 event-related potentials (ERPs) for brain-computer interface speller , 2010, Biomed. Signal Process. Control..
[25] Chang-Hwan Im,et al. Evaluation of feature extraction methods for EEG-based brain–computer interfaces in terms of robustness to slight changes in electrode locations , 2012, Medical & Biological Engineering & Computing.
[26] S. Haykin,et al. Adaptive Filter Theory , 1986 .
[27] Brendan Z. Allison,et al. Could Anyone Use a BCI? , 2010, Brain-Computer Interfaces.
[28] Brendan Z. Allison,et al. Brain-Computer Interfaces: A Gentle Introduction , 2009 .
[29] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[30] G. Berns,et al. BAD TO WORSE , 1975, The Lancet.
[31] Yijun Wang,et al. Brain-Computer Interfaces Based on Visual Evoked Potentials , 2008, IEEE Engineering in Medicine and Biology Magazine.
[32] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[33] Donald W. Bouldin,et al. A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[34] Gert Pfurtscheller,et al. Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.