Real‐time fMRI using brain‐state classification
暂无分享,去创建一个
[1] Stephen C. Strother,et al. Support vector machines for temporal classification of block design fMRI data , 2005, NeuroImage.
[2] S. Ogawa,et al. Oxygenation‐sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields , 1990, Magnetic resonance in medicine.
[3] D. Tank,et al. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 1990, Proceedings of the National Academy of Sciences of the United States of America.
[4] Gary H. Glover,et al. Learned regulation of spatially localized brain activation using real-time fMRI , 2004, NeuroImage.
[5] Seung-Schik Yoo,et al. Functional MRI for neurofeedback: feasibility studyon a hand motor task , 2002, Neuroreport.
[6] Luiz Pessoa,et al. Quantitative prediction of perceptual decisions during near-threshold fear detection. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[7] F. Tong,et al. Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.
[8] Jason Weston,et al. Trading convexity for scalability , 2006, ICML.
[9] P. Boesiger,et al. Dynamic characteristics of oxygenation-sensitive MRI signal in different temporal protocols for imaging human brain activity , 2000, Neuroradiology.
[10] Alice J. O'Toole,et al. Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex , 2005, Journal of Cognitive Neuroscience.
[11] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[12] G. Rees,et al. Predicting the Stream of Consciousness from Activity in Human Visual Cortex , 2005, Current Biology.
[13] Stephen C. Strother,et al. Penalized Discriminant Analysis of [15O]-water PET Brain Images with Prediction Error Selection of Smoothness and Regularization , 2001, IEEE Trans. Medical Imaging.
[14] D.M. Taylor,et al. Information conveyed through brain-control: cursor versus robot , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[15] David D. Cox,et al. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex , 2003, NeuroImage.
[16] Stephen José Hanson,et al. Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area? , 2004, NeuroImage.
[17] Dinggang Shen,et al. Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection , 2005, NeuroImage.
[18] John D E Gabrieli,et al. Control over brain activation and pain learned by using real-time functional MRI. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[19] K. Uğurbil,et al. Spatial and temporal differentiation of fMRI BOLD response in primary visual cortex of human brain during sustained visual simulation , 1998, Magnetic resonance in medicine.
[20] A. Ishai,et al. Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.
[21] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[22] Gary F. Egan,et al. Evaluating subject specific preprocessing choices in multisubject fMRI data sets using data-driven performance metrics , 2003, NeuroImage.
[23] R W Cox,et al. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.
[24] S. Ogawa. Brain magnetic resonance imaging with contrast-dependent oxygenation , 1990 .
[25] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[26] E Yacoub,et al. Detection of the early negative response in fMRI at 1.5 Tesla , 1999, Magnetic resonance in medicine.
[27] S. C. Strother,et al. The Quantitative Evaluation of Functional Neuroimaging Experiments: Mutual Information Learning Curves , 2002, NeuroImage.
[28] Rainer Goebel,et al. Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[29] Tom M. Mitchell,et al. Learning to Decode Cognitive States from Brain Images , 2004, Machine Learning.
[30] Lars Kai Hansen,et al. Optimizing the fMRI data-processing pipeline using prediction and reproducibility performance metrics: I. A preliminary group analysis , 2004, NeuroImage.
[31] Leslie G. Ungerleider,et al. Distributed representation of objects in the human ventral visual pathway. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[32] Manel Martínez-Ramón,et al. fMRI pattern classification using neuroanatomically constrained boosting , 2006, NeuroImage.
[33] Robert A. Lordo,et al. Learning from Data: Concepts, Theory, and Methods , 2001, Technometrics.
[34] S. Strother,et al. Penalized discriminant analysis of [/sup 15/O]-water PET brain images with prediction error selection of smoothness and regularization hyperparameters , 2001, IEEE Transactions on Medical Imaging.
[35] 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.
[36] Vaidehi S. Natu,et al. Category-Specific Cortical Activity Precedes Retrieval During Memory Search , 2005, Science.
[37] L. K. Hansen,et al. The Quantitative Evaluation of Functional Neuroimaging Experiments: The NPAIRS Data Analysis Framework , 2000, NeuroImage.
[38] Essa Yacoub,et al. The Evaluation of Preprocessing Choices in Single-Subject BOLD fMRI Using NPAIRS Performance Metrics , 2003, NeuroImage.
[39] S. Ogawa,et al. An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[40] N. Kanwisher,et al. The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception , 1997, The Journal of Neuroscience.
[41] Janaina Mourão Miranda,et al. Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data , 2005, NeuroImage.
[42] Soo-Young Lee,et al. Brain–computer interface using fMRI: spatial navigation by thoughts , 2004, Neuroreport.
[43] Frank Schneider,et al. Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness , 2003, NeuroImage.
[44] N. Kanwisher,et al. Testing cognitive models of visual attention with fMRI and MEG , 2001, Neuropsychologia.
[45] Michael Erb,et al. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data , 2003, NeuroImage.
[46] L. K. Hansen,et al. Plurality and Resemblance in fMRI Data Analysis , 1999, NeuroImage.