EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features
Tài liệu tham khảo
Burke, 2005, A parametric feature extraction and classification strategy for brain–computer interfacing, IEEE Trans Neural Syst Rehabil Eng, 13, 12, 10.1109/TNSRE.2004.841881
Coyle, 2004, Estimating the predictability of EEG recorded over the motor cortex using information theoretic functionals, 43
Coyle, 2005, A time-series prediction approach for feature extraction in a brain–computer interface, IEEE Trans Neural Syst Rehabil Eng, 13, 461, 10.1109/TNSRE.2005.857690
Daubechies, 1988, Orthonormal bases of compactly supported wavelets, Commun Pure Appl Math, 41, 909, 10.1002/cpa.3160410705
Graz Data Sets and description for the BCI 2003 competition. [Online]. Available: http://ida.first.fraunhofer.de/projects/bci/competition/.
Graz Data Sets and description for the BCI 2005 competition. [Online]. Available: http://ida.first.fraunhofer.de/projects/bci/competition_iii/.
Guger, 2003, How many people are able to operate an EEG-based brain–computer interface (BCI)?, IEEE Trans Neural Syst Rehabil Eng, 11, 145, 10.1109/TNSRE.2003.814481
Guger, 2001, Rapid prototyping of an EEG-based brain–computer interface (BCI), IEEE Trans Rehabil Eng, 9, 49, 10.1109/7333.918276
Hanley, 1982, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 29, 10.1148/radiology.143.1.7063747
Haselsteiner, 2000, Using time-dependent NNs for EEG classification, IEEE Trans Rehabil Eng, 8, 457, 10.1109/86.895948
Henderson, 2006, Development and assessment of methods for detecting dementia using the human electroencephalogram, IEEE Trans Biomed Eng, 53, 1557, 10.1109/TBME.2006.878067
Hsu, 2007, Wavelet-based fractal features with active segment selection: application to single-trial EEG data, J Neurosci Methods, 163, 145, 10.1016/j.jneumeth.2007.02.004
Hsu, 2008, Automatic seamless mosaicing of microscopic images: enhancing appearance with colour degradation compensation and wavelet-based blending, J Microsc, 231, 408, 10.1111/j.1365-2818.2008.02052.x
Hsu, 2009, EEG-based motor imagery analysis using weighted wavelet transform features, J Neurosci Methods, 176, 310, 10.1016/j.jneumeth.2008.09.014
Hu, 2007, Time series prediction with a weighted bidirectional multi-stream extended Kalman filter, Neurocomputing, 70, 2392, 10.1016/j.neucom.2005.12.135
Jang Roger, 1993, ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans SMC, 23, 665
Jasper, 1958, Report of committee on methods of clinical exam in EEG, Electroencephalogr Clin Neurophysiol, 10, 370, 10.1016/0013-4694(58)90053-1
Kelly, 2002, Parametric models and spectral analysis for classification in brain–computer interfaces, 307
Lee, 2003, Ultrasonic liver tissues classification by fractal feature vector based on M-band wavelet transform, IEEE Trans Med Imaging, 22, 382, 10.1109/TMI.2003.809593
Leeb, 2007, Brain–computer communication: motivation, aim, and impact of exploring a virtual apartment, IEEE Trans Neural Syst Rehabil Eng, 15, 473, 10.1109/TNSRE.2007.906956
Lian, 2006, Performance enhancement for T–S fuzzy control using neural networks, IEEE Trans Fuzzy Syst, 14, 619, 10.1109/TFUZZ.2006.876728
Mandelbrot, 1982
Müller-Putz, 2008, Comparison of DFT and lock-in amplifier features and search for optimal electrode positions in SSVEP-based BCI, J Neurosci Methods, 168, 174, 10.1016/j.jneumeth.2007.09.024
Nijboer, 2008, An auditory brain–computer interface (BCI), J Neurosci Methods, 167, 43, 10.1016/j.jneumeth.2007.02.009
Obermaier, 2001, Information transfer rate in a five-classes brain–computer interface, IEEE Trans Neural Syst Rehabil Eng, 9, 283, 10.1109/7333.948456
Paramanathan, 2008, Application of fractal theory in analysis of human electroencephalographic signals, Comput Biol Med, 38, 372, 10.1016/j.compbiomed.2007.12.004
Parra, 2002, Linear spatial integration for single trial detection in encephalography, NeuroImage, 7, 223, 10.1006/nimg.2002.1212
Pfurtscheller, 1999, Event-related EEG/MEG synchronization and desynchronization: basic principles, Clin Neurophysiol, 110, 1842, 10.1016/S1388-2457(99)00141-8
Pfurtscheller, 2006, 15 years of BCI research at Graz University of Technology: current projects, IEEE Trans Neural Syst Rehabil Eng, 14, 205, 10.1109/TNSRE.2006.875528
Rankine, 2007, A nonstationary model of newborn EEG, IEEE Trans Biomed Eng, 54, 19, 10.1109/TBME.2006.886667
Rong, 2006, Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction, Fuzzy Sets Syst, 157, 1260, 10.1016/j.fss.2005.12.011
Schalk, 2008, Brain–computer interfaces (BCIs): detection instead of classification, J Neurosci Methods, 167, 51, 10.1016/j.jneumeth.2007.08.010
Schlogl, 2002, Estimating the mutual information of an EEG-based brain–computer interface, Biomed Tech, 47, 3, 10.1515/bmte.2002.47.1-2.3
Schlogl, 2005, Characterization of four-class motor imagery EEG data for the BCI-competition 2005, J Neural Eng, 2, L14, 10.1088/1741-2560/2/4/L02
Shumway, 2000
Stamatis, 1999, Forecasting chaotic cardiovascular time series with an adaptive slope multilayer perceptron neural network, IEEE Trans Biomed Eng, 46, 1441, 10.1109/10.804572
Townsend, 2004, Continuous EEG classification during motor imagery-simulation of an asynchronous BCI, IEEE Trans Neural Syst Rehabil Eng, 12, 258, 10.1109/TNSRE.2004.827220
Vaughan, 2006, The wadsworth BCI research and development program: at home with BCI, IEEE Trans Neural Syst Rehabil Eng, 14, 229, 10.1109/TNSRE.2006.875577
Wolpaw, 2000, Brain–computer interface technology: a review of the first international meeting, IEEE Trans Rehabil Eng, 8, 164, 10.1109/TRE.2000.847807
Wolpaw, 2002, Brain–computer interfaces for communication and control, Clin Neurophysiol, 113, 767, 10.1016/S1388-2457(02)00057-3