Toward a Semi-Self-Paced EEG Brain Computer Interface: Decoding Initiation State from Non-Initiation State in Dedicated Time Slots
暂无分享,去创建一个
Terrence J. Sejnowski | Lingling Yang | Howard Poizner | Howard Leung | David A. Peterson | T. Sejnowski | D. A. Peterson | H. Poizner | Howard Leung | Lingling Yang
[1] Horst Bischof,et al. Toward Self-Paced Brain–Computer Communication: Navigation Through Virtual Worlds , 2008, IEEE Transactions on Biomedical Engineering.
[2] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[3] G. Pfurtscheller,et al. Continuous EEG classification during motor imagery-simulation of an asynchronous BCI , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[4] 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.
[5] Mário Sarcinelli-Filho,et al. Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface. , 2013, Medical engineering & physics.
[6] Gernot R. Müller-Putz,et al. Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic , 2007, Comput. Intell. Neurosci..
[7] J. Zygierewicz,et al. Asynchronous BCI Based on Motor Imagery With Automated Calibration and Neurofeedback Training , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[8] W. A. Sarnacki,et al. Electroencephalographic (EEG) control of three-dimensional movement , 2010, Journal of neural engineering.
[9] Shangkai Gao,et al. An N200 speller integrating the spatial profile for the detection of the non-control state , 2012, Journal of neural engineering.
[10] G. Oriolo,et al. Non-invasive brain–computer interface system: Towards its application as assistive technology , 2008, Brain Research Bulletin.
[11] N. Birbaumer,et al. Brain–computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? , 2008, Clinical Neurophysiology.
[12] Dennis J. McFarland,et al. Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.
[13] R. C. Oldfield. The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.
[14] B. Dobkin. Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation , 2007, The Journal of physiology.
[15] T. Chau,et al. Towards a system-paced near-infrared spectroscopy brain–computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state , 2011, Journal of neural engineering.
[16] U. Rajendra Acharya,et al. EEG Signal Analysis: A Survey , 2010, Journal of Medical Systems.
[17] Terrence J. Sejnowski,et al. Choice modulates the neural dynamics of prediction error processing during rewarded learning , 2011, NeuroImage.
[18] V. Caggiano,et al. Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses , 2012, PloS one.
[19] J. Schoffelen,et al. Parieto‐occipital sources account for the increase in alpha activity with working memory load , 2007, Human brain mapping.
[20] R. Oostenveld,et al. Validating the boundary element method for forward and inverse EEG computations in the presence of a hole in the skull , 2002, Human brain mapping.
[21] Sebastian Bosse,et al. Toward a Direct Measure of Video Quality Perception Using EEG , 2012, IEEE Transactions on Image Processing.
[22] R. Oostenveld,et al. Theta and Gamma Oscillations Predict Encoding and Retrieval of Declarative Memory , 2006, The Journal of Neuroscience.
[23] Hubert Cecotti,et al. A Self-Paced and Calibration-Less SSVEP-Based Brain–Computer Interface Speller , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[24] Jaeseung Jeong,et al. Toward Brain-Actuated Humanoid Robots: Asynchronous Direct Control Using an EEG-Based BCI , 2012, IEEE Transactions on Robotics.
[25] Pablo F. Diez,et al. Asynchronous BCI control using high-frequency SSVEP , 2011, Journal of NeuroEngineering and Rehabilitation.
[26] Gary E. Birch,et al. A brain-controlled switch for asynchronous control applications , 2000, IEEE Trans. Biomed. Eng..
[27] G.E. Birch,et al. Brain interface research for asynchronous control applications , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[28] 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.
[29] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[30] H. Poizner,et al. Probabilistic reversal learning is impaired in Parkinson's disease , 2009, Neuroscience.
[31] Miguel A. L. Nicolelis,et al. Brain–machine interfaces to restore motor function and probe neural circuits , 2003, Nature Reviews Neuroscience.
[32] John Q. Gan,et al. Hangman BCI: An unsupervised adaptive self-paced Brain-Computer Interface for playing games , 2012, Comput. Biol. Medicine.
[33] F. L. D. Silva,et al. Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.
[34] C.W. Anderson,et al. Comparison of linear, nonlinear, and feature selection methods for EEG signal classification , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[35] P. Sajda,et al. Spatiotemporal Linear Decoding of Brain State , 2008, IEEE Signal Processing Magazine.
[36] Tzyy-Ping Jung,et al. A Collaborative Brain-Computer Interface for Improving Human Performance , 2011, PloS one.