Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns
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Danilo P. Mandic | Youngjoo Kim | Ko Keun Kim | Cheolsoo Park | Clive Cheong Took | Jiwoo Ryu | D. Mandic | C. C. Took | Cheolsoo Park | K. Kim | J. Ryu | Youngjoo Kim
[1] G. Pfurtscheller,et al. Visualization of sensorimotor areas involved in preparation for hand movement based on classification of μ and central β rhythms in single EEG trials in man , 1994, Neuroscience Letters.
[2] Eibe Frank,et al. Logistic Model Trees , 2003, Machine Learning.
[3] D. Mandic,et al. Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models , 2009 .
[4] F. L. D. Silva,et al. Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.
[5] G. Pfurtscheller. Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest. , 1992, Electroencephalography and clinical neurophysiology.
[6] Cheolsoo Park,et al. Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[7] Abdul Hamid Adom,et al. Motor Imagery Signal Classification for a Four State Brain Machine Interface , 2007 .
[8] Mike W Oram,et al. Emotional Information Processing in Major Depression Remission and Partial Remission: Faces Come First , 2013, Applied neuropsychology. Adult.
[9] J. Wolpaw,et al. Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements , 2004, Brain Topography.
[10] Aida Khorshidtalab,et al. Motor Imagery Task Classification Using a Signal-Dependent Orthogonal Transform Based Feature Extraction , 2015, ICONIP.
[11] Dezhong Yao,et al. L1 Norm based common spatial patterns decomposition for scalp EEG BCI , 2013, BioMedical Engineering OnLine.
[12] Cheolsoo Park,et al. Strong Uncorrelated Transform Applied to Spatially Distant Channel EEG Data , 2015 .
[13] Bin He,et al. A novel channel selection method for optimal classification in different motor imagery BCI paradigms , 2015, BioMedical Engineering OnLine.
[14] J. Binder,et al. Functional magnetic resonance imaging of complex human movements , 1993, Neurology.
[15] Bin He,et al. Motor imagery task classification for brain computer interface applications using spatiotemporal principle component analysis , 2004, Neurological research.
[16] David W. Aha,et al. Instance-Based Learning Algorithms , 1991, Machine Learning.
[17] Xiaopei Wu,et al. Motor Imagery EEG Classification Based on Dynamic ICA Mixing Matrix , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.
[18] Danilo P. Mandic,et al. Filter Bank Property of Multivariate Empirical Mode Decomposition , 2011, IEEE Transactions on Signal Processing.
[19] B. Hofmann-Wellenhof,et al. Introduction to spectral analysis , 1986 .
[20] G Pfurtscheller,et al. Graphical display and statistical evaluation of event-related desynchronization (ERD). , 1977, Electroencephalography and clinical neurophysiology.
[21] Gert Pfurtscheller,et al. Characterization of four-class motor imagery EEG data for the BCI-competition 2005 , 2005, Journal of neural engineering.
[22] Nidal Kamel,et al. Mental task motor imagery classifications for noninvasive brain computer interface , 2014, 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS).
[23] G. Pfurtscheller,et al. 15 years of BCI research at graz university of technology: current projects , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[24] D. Kibler,et al. Instance-based learning algorithms , 2004, Machine Learning.
[25] Keum-Shik Hong,et al. Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain–computer interface , 2013, Neuroscience Letters.
[26] D M Durand,et al. Suppression of axonal conduction by sinusoidal stimulation in rat hippocampus in vitro , 2007, Journal of neural engineering.
[27] Yoon Gi Chung,et al. Analysis of correlated EEG activity during motor imagery for brain-computer interfaces , 2011, 2011 11th International Conference on Control, Automation and Systems.
[28] Minkyu Ahn,et al. Journal of Neuroscience Methods , 2015 .
[29] G. Pfurtscheller,et al. Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.
[30] Jeffrey M. Hausdorff,et al. Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .
[31] Xiaojun Yang,et al. Estimating landscape imperviousness index from satellite imagery , 2006, IEEE Geosci. Remote. Sens. Lett..
[32] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[33] N. Huang,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[34] Kenneth P. Camilleri,et al. Complex-valued spatial filters for task discrimination , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.
[35] G. Pfurtscheller,et al. Motor imagery activates primary sensorimotor area in humans , 1997, Neuroscience Letters.
[36] Yong Wang,et al. Using Model Trees for Classification , 1998, Machine Learning.
[37] Gernot R. Müller-Putz,et al. Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier , 2016, Biomedizinische Technik. Biomedical engineering.
[38] Claude Frasson,et al. Predicting Learner Answers Correctness through Brainwaves Assesment and Emotional Dimensions , 2009, AIED.
[39] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[40] Gert Pfurtscheller,et al. Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.
[41] Cheolsoo Park,et al. Time-Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Birth , 2015 .
[42] Brendan Z. Allison,et al. Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction , 2013 .
[43] Salim Lahmiri,et al. A weighted bio-signal denoising approach using empirical mode decomposition , 2015, Biomedical Engineering Letters.
[44] Brendan Z. Allison,et al. Brain-Computer Interfaces , 2010 .
[45] Danilo P. Mandic,et al. Augmented Complex Common Spatial Patterns for Classification of Noncircular EEG From Motor Imagery Tasks , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[46] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.
[47] I. Toni,et al. Distinct roles for alpha- and beta-band oscillations during mental simulation of goal-directed actions. , 2014, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[48] Xinman Zhang,et al. Human Face Recognition using Multi-Class Projection Extreme Learning Machine , 2013 .
[49] Ram Bilas Pachori,et al. Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals , 2013 .
[50] Bin He,et al. Cortical Imaging of Event-Related (de)Synchronization During Online Control of Brain-Computer Interface Using Minimum-Norm Estimates in Frequency Domain , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[51] Klaus-Robert Müller,et al. The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.
[52] N. Birbaumer,et al. BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.
[53] Ana Loboda,et al. Discrimination of EEG-Based Motor Imagery Tasks by Means of a Simple Phase Information Method , 2014 .
[54] G. Pfurtscheller,et al. EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.
[55] Min Han,et al. A fully automatic ocular artifact removal from EEG based on fourth-order tensor method , 2014 .
[56] Wei Liu,et al. Analysis and Online Realization of the CCA Approach for Blind Source Separation , 2007, IEEE Transactions on Neural Networks.
[57] Jesús Navarro-Moreno,et al. Estimation of Improper Complex-Valued Random Signals in Colored Noise by Using the Hilbert Space Theory , 2009, IEEE Transactions on Information Theory.
[58] D. P. Mandic,et al. Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[59] J. Mazziotta,et al. Mapping motor representations with positron emission tomography , 1994, Nature.
[60] Vanessa Su Lee Goh,et al. Complex Valued Adaptive Filters , 2009 .
[61] Clemens Brunner,et al. Better than random? A closer look on BCI results , 2008 .
[62] Po-Lei Lee,et al. Recognition of Motor Imagery Electroencephalography Using Independent Component Analysis and Machine Classifiers , 2005, Annals of Biomedical Engineering.