Brain Imaging and Machine Learning for Brain-Computer Interface

Human-computer interfaces are in continuous development, from keyboard, mouse, touch screen, to voice dictation, gesture recognition, etc. The aim is to facilitate the interaction between the human brain and the resources offered by a machine or a computer. Recently, a wider interest has emerged in directly interfacing the brain and the computer. The development of methods that combine the nervous system with artificial devices is attracting a growing interest from clinical research, because the interaction between brain and machines may lead to novel prosthetic devices or to a more efficient use of computer resources by breaking the barriers imposed at present by the classical human-machine interfaces. Individuals with impaired motor control may be disabled in the performance of their daily activities. Their motor performance, however, can be supported by artificial motor control systems. Such motor support systems may also assist healthy individuals in performing their tasks. One can also imagine interacting with different systems in parallel, or developing newer software tools without the need to physically typing the code.

[1]  Gary E. Birch,et al.  A wavelet-based approach for the extraction of event related potentials from EEG , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  J.C. Mosher,et al.  Recursive MUSIC: A framework for EEG and MEG source localization , 1998, IEEE Transactions on Biomedical Engineering.

[3]  Jean-Francois Mangin,et al.  A multiresolution framework to MEG/EEG source imaging , 2001, IEEE Trans. Biomed. Eng..

[4]  Klaus-Robert Müller,et al.  Toward noninvasive brain-computer interfaces , 2006, IEEE Signal Process. Mag..

[5]  S. Jackson,et al.  The selection and suppression of action: ERP correlates of executive control in humans. , 1999, Neuroreport.

[6]  H. Poizner,et al.  Predicting Reaching Targets from Human EEG , 2008, IEEE Signal Processing Magazine.

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

[8]  Brendan Z. Allison,et al.  Brain-Computer Interfaces , 2010 .

[9]  Lauri Parkkonen,et al.  A 122-channel whole-cortex SQUID system for measuring the brain's magnetic fields , 1993 .

[10]  Chafic Mokbel,et al.  BRAIN IMAGING AND SUPPORT VECTOR MACHINES FOR BRAIN COMPUTER INTERFACE , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  J. Rothwell.  Principles of Neural Science , 1982 .

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  Steven Kay,et al.  Modern Spectral Estimation: Theory and Application , 1988 .

[14]  T. Lagerlund,et al.  Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. , 1997, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[15]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[16]  F C van der Helm,et al.  Biomechatronics – Assisting the Impaired Motor System , 2001, Archives of physiology and biochemistry.

[17]  J. Engel.  Seizures and Epilepsy , 2012 .

[18]  Tzyy-Ping Jung,et al.  Imaging brain dynamics using independent component analysis , 2001, Proc. IEEE.

[19]  G Pfurtscheller,et al.  Frequency component selection for an EEG-based brain to computer interface. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[20]  Touradj Ebrahimi,et al.  Brain-computer interface in multimedia communication , 2003, IEEE Signal Process. Mag..

[21]  A. Al-Ani,et al.  Brain-Computer Interface Analysis using Continuous Wavelet Transform and Adaptive Neuro-Fuzzy Classifier , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  M. Murray,et al.  EEG source imaging , 2004, Clinical Neurophysiology.

[23]  John P. Cunningham,et al.  Increasing the Performance of Cortically-Controlled Prostheses , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  P. Sajda,et al.  A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[25]  Ralph Otto Schmidt,et al.  A signal subspace approach to multiple emitter location and spectral estimation , 1981 .

[26]  Aiguo Song,et al.  Algorithm of Imagined Left-Right Hand Movement Classification Based on Wavelet Transform and AR Parameter Model , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[27]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[28]  E. A. Stolz,et al.  Multivariate Autoregressive Models for Classification of Spontaneous Electroencephalogram During Mental Tasks1 , 1998 .

[29]  Richard M. Leahy,et al.  Source localization using recursively applied and projected (RAP) MUSIC , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[30]  J.C. Mosher,et al.  Multiple dipole modeling and localization from spatio-temporal MEG data , 1992, IEEE Transactions on Biomedical Engineering.

[31]  Jagath C. Rajapakse,et al.  Bayesian approach to segmentation of statistical parametric maps , 2001, IEEE Transactions on Biomedical Engineering.

[32]  G. Pfurtscheller,et al.  The BCI competition III: validating alternative approaches to actual BCI problems , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  C.W. Anderson,et al.  Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks , 1998, IEEE Transactions on Biomedical Engineering.

[34]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[35]  Touradj Ebrahimi,et al.  Support vector EEG classification in the Fourier and time-frequency correlation domains , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[36]  G. Flint,et al.  Seizures and epilepsy. , 1988, British journal of neurosurgery.

[37]  Anders M. Dale,et al.  Improved Localization of Cortical Activity By Combining EEG and MEG with MRI Cortical Surface Reconstruction , 2002 .

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

[39]  Andrzej Cichocki,et al.  Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis , 2002, Biological Cybernetics.