EEG feature comparison and classification of simple and compound limb motor imagery
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Hongzhi Qi | Lixin Zhang | B. Wan | Dong Ming | Shuang Qiu | Weibo Yi | Dong Ming
[1] H. Jasper,et al. Electrocorticograms in man: Effect of voluntary movement upon the electrical activity of the precentral gyrus , 1949 .
[2] T. Inouye,et al. Quantification of EEG irregularity by use of the entropy of the power spectrum. , 1991, Electroencephalography and clinical neurophysiology.
[3] Dennis J. McFarland,et al. Design and operation of an EEG-based brain-computer interface with digital signal processing technology , 1997 .
[4] G. Pfurtscheller,et al. Motor imagery activates primary sensorimotor area in humans , 1997, Neuroscience Letters.
[5] G Pfurtscheller,et al. Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[6] F. L. D. Silva,et al. Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.
[7] M J Stokes,et al. EEG-based communication: a pattern recognition approach. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[8] Anil K. Jain,et al. Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[9] E Donchin,et al. Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[10] G. R. Muller,et al. Brain oscillations control hand orthosis in a tetraplegic , 2000, Neuroscience Letters.
[11] G. Pfurtscheller,et al. Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[12] J. Wolpaw,et al. Brain-computer communication: unlocking the locked in. , 2001, Psychological bulletin.
[13] Gert Pfurtscheller,et al. Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.
[14] J. Mourino,et al. Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[15] William Z Rymer,et al. Guest Editorial Brain–Computer Interface Technology: A Review of the Second International Meeting , 2001 .
[16] Klaus-Robert Müller,et al. Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms , 2004, IEEE Transactions on Biomedical Engineering.
[17] D A Steyn-Ross,et al. Cortical entropy changes with general anaesthesia: theory and experiment. , 2004, Physiological measurement.
[18] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[19] Arnaud Delorme,et al. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.
[20] Yijun Wang,et al. Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.
[21] W. Penfield,et al. Electrocorticograms in man: Effect of voluntary movement upon the electrical activity of the precentral gyrus , 2005, Archiv für Psychiatrie und Nervenkrankheiten.
[22] G. Pfurtscheller,et al. Graz brain-computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns , 1996, Medical and Biological Engineering and Computing.
[23] Clemens Brunner,et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.
[24] Clemens Brunner,et al. Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis , 2007, Pattern Recognit. Lett..
[25] K.-R. Muller,et al. Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.
[26] Hongzhi Qi,et al. A novel technique for phase synchrony measurement from the complex motor imaginary potential of combined body and limb action , 2010, Journal of neural engineering.
[27] G. Pfurtscheller,et al. Self-Paced Operation of an SSVEP-Based Orthosis With and Without an Imagery-Based “Brain Switch:” A Feasibility Study Towards a Hybrid BCI , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[28] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[29] G. Pfurtscheller,et al. An SSVEP BCI to Control a Hand Orthosis for Persons With Tetraplegia , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[30] Dennis J. McFarland,et al. Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.
[31] A. Doud,et al. Continuous Three-Dimensional Control of a Virtual Helicopter Using a Motor Imagery Based Brain-Computer Interface , 2011, PloS one.
[32] Cuntai Guan,et al. Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.
[33] S. Baron-Cohen,et al. Atypical EEG complexity in autism spectrum conditions: A multiscale entropy analysis , 2011, Clinical Neurophysiology.
[34] Motoaki Kawanabe,et al. Stationary common spatial patterns for brain–computer interfacing , 2012, Journal of neural engineering.