A Real-Time Functional Magnetic Resonance Imaging (fMRI) Neurofeedback System

Abstract In order to implement brain-computer interfaces (BCI's), the individual must be able to volitionally control brain function: a skill that can only be achieved through specialized training. Currently, this training is time-consuming and produces unreliable results. It is necessary to first understand the underlying neural mechanisms associated with this skill to develop effective, dependable training techniques. A real-time functional Magnetic Resonance Imaging (rtfMRI) system has been developed to research volitional control over neural activity and interpret the neural changes associated with increases in this skill. The developed system enables additional research such as the study of neuroplasticity and neural networks.

[1]  Sven Haller,et al.  Real-time fMRI feedback training may improve chronic tinnitus , 2010, European Radiology.

[2]  W. K. Simmons,et al.  Self-Regulation of Amygdala Activation Using Real-Time fMRI Neurofeedback , 2011, PloS one.

[3]  N. Birbaumer,et al.  The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Ole Winther,et al.  Analysis of functional neuroimages using ICA with adaptive binary sources , 2002, Neurocomputing.

[5]  Tomas Ros,et al.  Optimizing microsurgical skills with EEG neurofeedback , 2009, BMC Neuroscience.

[6]  T. Egner,et al.  Foundation and Practice of Neurofeedback for the Treatment of Epilepsy , 2006, Applied psychophysiology and biofeedback.

[7]  J J Vidal,et al.  Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.

[8]  N. Birbaumer,et al.  The thought translation device: a neurophysiological approach to communication in total motor paralysis , 1999, Experimental Brain Research.

[9]  Niels Birbaumer,et al.  fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment , 2007, Comput. Intell. Neurosci..

[10]  Wolfgang Grodd,et al.  Regulation of anterior insular cortex activity using real-time fMRI , 2007, NeuroImage.

[11]  W. Kirchner.  Age differences in short-term retention of rapidly changing information. , 1958, Journal of experimental psychology.

[12]  Wolfgang Grodd,et al.  Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) , 2004, IEEE Transactions on Biomedical Engineering.

[13]  Shirley M Coyle,et al.  Brain–computer interface using a simplified functional near-infrared spectroscopy system , 2007, Journal of neural engineering.

[14]  Rainer Goebel,et al.  Neurofeedback: A promising tool for the self-regulation of emotion networks , 2010, NeuroImage.

[15]  R. Deichmann,et al.  Real-time functional magnetic resonance imaging: methods and applications. , 2007, Magnetic resonance imaging.

[16]  Jong-Hwan Lee,et al.  Neurofeedback fMRI-mediated learning and consolidation of regional brain activation during motor imagery , 2008 .

[17]  Melody Moore Jackson,et al.  Applications for Brain-Computer Interfaces , 2010, Brain-Computer Interfaces.

[18]  Gary H. Glover,et al.  Learned regulation of spatially localized brain activation using real-time fMRI , 2004, NeuroImage.

[19]  Gaoyan Zhang,et al.  Functional Alteration of the DMN by Learned Regulation of the PCC Using Real-Time fMRI , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  Nikolaus Weiskopf,et al.  Real-time fMRI and its application to neurofeedback , 2012, NeuroImage.

[21]  M. Doppelmayr,et al.  An Attempt to Increase Cognitive Performance after Stroke with Neurofeedback , 2007 .

[22]  Paolo Massimo Buscema,et al.  The Implicit Function as Squashing Time Model: A Novel Parallel Nonlinear EEG Analysis Technique Distinguishing Mild Cognitive Impairment and Alzheimer's Disease Subjects with High Degree of Accuracy , 2007, Comput. Intell. Neurosci..

[23]  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.

[24]  J.J. Vidal,et al.  Real-time detection of brain events in EEG , 1977, Proceedings of the IEEE.

[25]  R. Veit,et al.  Self‐regulation of regional cortical activity using real‐time fMRI: The right inferior frontal gyrus and linguistic processing , 2009, Human brain mapping.

[26]  J. Lubar,et al.  Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in T.O.V.A. scores, behavioral ratings, and WISC-R performance , 1995, Biofeedback and self-regulation.

[27]  Seung-Schik Yoo,et al.  Functional MRI for neurofeedback: feasibility studyon a hand motor task , 2002, Neuroreport.

[28]  Girijesh Prasad,et al.  Using motor imagery based brain-computer interface for post-stroke rehabilitation , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[29]  Thilo Hinterberger,et al.  The Thought Translation Device: structure of a multimodal brain-computer communication system , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..