Novel Features for Brain-Computer Interfaces
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[1] W. Penny,et al. Temporal and Spatial Complexity Measures for Eeg-based Brain- Computer Interfacing , 1998 .
[2] Fusheng Yang,et al. Mu rhythm-based cursor control: an offline analysis , 2004, Clinical Neurophysiology.
[3] Milan Paluš,et al. Is nonlinearity relevant for detecting changes in EEG , 1999 .
[4] David Barber,et al. EEG classification using generative independent component analysis , 2006, Neurocomputing.
[5] Steven Lemm,et al. BCI competition 2003-data set III: probabilistic modeling of sensorimotor /spl mu/ rhythms for classification of imaginary hand movements , 2004, IEEE Transactions on Biomedical Engineering.
[6] S. J. Roberts,et al. Temporal and spatial complexity measures for electroencephalogram based brain-computer interfacing , 2006, Medical & Biological Engineering & Computing.
[7] Dennis J. McFarland,et al. Design and operation of an EEG-based brain-computer interface with digital signal processing technology , 1997 .
[8] G. Curio,et al. Probabilistic Modeling of Sensorimotor μ-Rhythms for Classification of Imaginary Hand Movements ( BCI Competition 2003-Data Set III ) , 2004 .
[9] C. Stam,et al. Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.
[10] Le Song,et al. Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features , 2006, ICML.
[11] Xiao-mei Pei,et al. Multi-channel linear descriptors for event-related EEG collected in brain computer interface , 2006, Journal of neural engineering.
[12] I. Rezek,et al. Stochastic complexity measures for physiological signal analysis , 1998, IEEE Transactions on Biomedical Engineering.
[13] Klaus-Robert Müller,et al. The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials , 2004, IEEE Transactions on Biomedical Engineering.
[14] T. Schreiber. Interdisciplinary application of nonlinear time series methods , 1998, chao-dyn/9807001.
[15] Christopher J. James,et al. Extracting multisource brain activity from a single electromagnetic channel , 2003, Artif. Intell. Medicine.
[16] Yuanqing Li,et al. ICA and Committee Machine-Based Algorithm for Cursor Control in a BCI System , 2005, ISNN.
[17] Clemens Brunner,et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.
[18] C. Elger,et al. CAN EPILEPTIC SEIZURES BE PREDICTED? EVIDENCE FROM NONLINEAR TIME SERIES ANALYSIS OF BRAIN ELECTRICAL ACTIVITY , 1998 .
[19] Gilles Blanchard,et al. BCI competition 2003-data set IIa: spatial patterns of self-controlled brain rhythm modulations , 2004, IEEE Transactions on Biomedical Engineering.
[20] Rajesh P. N. Rao,et al. Towards adaptive classification for BCI , 2006, Journal of neural engineering.
[21] John R. Terry,et al. NONLINEAR INTERDEPENDENCE IN NEURAL SYSTEMS: MOTIVATION, THEORY, AND RELEVANCE , 2002, The International journal of neuroscience.
[22] 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.
[23] C. Stam,et al. Decrease of non-linear structure in the EEG of Alzheimer patients compared to healthy controls , 1999, Clinical Neurophysiology.
[24] Barak A. Pearlmutter,et al. Nonlinear time series analysis of human alpha rhythm , 2002 .
[25] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[26] F. L. D. Silva,et al. Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.
[27] Andrzej Cichocki,et al. Nonnegative Matrix Factorization for Motor Imagery EEG Classification , 2006, ICANN.
[28] Lucas C. Parra,et al. Recipes for the linear analysis of EEG , 2005, NeuroImage.
[29] Matcheri S. Keshavan,et al. Decreased nonlinear complexity and chaos during sleep in first episode schizophrenia: a preliminary report , 2004, Schizophrenia Research.