EEG-based motor imagery analysis using weighted wavelet transform features

[1]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[2]  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.

[3]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[4]  V. Samar,et al.  Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial , 1999, Brain and Language.

[5]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[6]  V. Samar,et al.  Time–Frequency Analysis of Single-Sweep Event-Related Potentials by Means of Fast Wavelet Transform , 1999, Brain and Language.

[7]  G Pfurtscheller,et al.  Current trends in Graz Brain-Computer Interface (BCI) research. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[8]  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.

[9]  G Pfurtscheller,et al.  Using time-dependent neural networks for EEG classification. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[10]  B. Allison,et al.  The effects of self-movement, observation, and imagination on mu rhythms and readiness potentials (RP's): toward a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[11]  G. Pfurtscheller,et al.  Rapid prototyping of an EEG-based brain-computer interface (BCI) , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[13]  G Pfurtscheller,et al.  Estimating the Mutual Information of an EEG-based Brain-Computer Interface , 2002, Biomedizinische Technik. Biomedical engineering.

[14]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[15]  Barak A. Pearlmutter,et al.  Linear Spatial Integration for Single-Trial Detection in Encephalography , 2002, NeuroImage.

[16]  W. A. Sarnacki,et al.  Brain–computer interface (BCI) operation: optimizing information transfer rates , 2003, Biological Psychology.

[17]  Christa Neuper,et al.  An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate , 2004, IEEE Transactions on Biomedical Engineering.

[18]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.

[19]  Bin He,et al.  INSTITUTE OF PHYSICS PUBLISHING JOURNAL OF NEURAL ENGINEERING , 2003 .

[20]  Bernhard Graimann,et al.  Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis , 2004, IEEE Transactions on Biomedical Engineering.

[21]  Bin He,et al.  Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns , 2004, Clinical Neurophysiology.

[22]  B. Kotchoubey,et al.  Recognition of affective prosody: continuous wavelet measures of event-related brain potentials to emotional exclamations. , 2004, Psychophysiology.

[23]  Vladimir Bostanov,et al.  BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram , 2004, IEEE Transactions on Biomedical Engineering.

[24]  Bin He,et al.  A wavelet-based time–frequency analysis approach for classification of motor imagery for brain–computer interface applications , 2005, Journal of neural engineering.

[25]  Zhongming Liu,et al.  An enhanced time-frequency-spatial approach for motor imagery classification , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[26]  Wei-Yen Hsu,et al.  Wavelet-based fractal features with active segment selection: Application to single-trial EEG data , 2007, Journal of Neuroscience Methods.

[27]  J. R. Wolpaw,et al.  Brain–computer interfaces (BCIs): Detection instead of classification , 2008, Journal of Neuroscience Methods.

[28]  D. McFarland,et al.  An auditory brain–computer interface (BCI) , 2008, Journal of Neuroscience Methods.

[29]  Gernot R. Müller-Putz,et al.  Comparison of DFT and lock-in amplifier features and search for optimal electrode positions in SSVEP-based BCI , 2008, Journal of Neuroscience Methods.

[30]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.