Brain-computer interfaces for communication and control.

The brain's electrical signals enable people without muscle control to physically interact with the world.

Brain-computer interfaces for communication and control

http://www.ece.uvic.ca/~bctill/research/bci/chmaprefs/McFarland1997.pdf

Spatial filter selection for EEG-based communication

In MRI, the main magnetic field polarizes the electron cloud of a molecule, generating a chemical shift for observer protons within the molecule and a magnetic susceptibility inhomogeneity field for observer protons outside the molecule. The number of water protons surrounding a molecule for detecting its magnetic susceptibility is vastly greater than the number of protons within the molecule for detecting its chemical shift. However, the study of tissue magnetic susceptibility has been hindered by poor molecular specificities of hitherto used methods based on MRI signal phase and T2* contrast, which depend convolutedly on surrounding susceptibility sources. Deconvolution of the MRI signal phase can determine tissue susceptibility but is challenged by the lack of MRI signal in the background and by the zeroes in the dipole kernel. Recently, physically meaningful regularizations, including the Bayesian approach, have been developed to enable accurate quantitative susceptibility mapping (QSM) for studying iron distribution, metabolic oxygen consumption, blood degradation, calcification, demyelination, and other pathophysiological susceptibility changes, as well as contrast agent biodistribution in MRI. This paper attempts to summarize the basic physical concepts and essential algorithmic steps in QSM, to describe clinical and technical issues under active development, and to provide references, codes, and testing data for readers interested in QSM. Magn Reson Med 73:82–101, 2015. © 2014 The Authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance. This is an open access article under the terms of the Creative commons Attribution License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited.

Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker.

This paper proposes a novel and simple local neural classifier for the recognition of mental tasks from on-line spontaneous EEG signals The proposed neural classifier recognizes three mental tasks from on-line spontaneous EEG signals Correct recognition is around 70% This modest rate is largely compensated by two properties, namely low percentage of wrong decisions (below 5%) and rapid responses (every 1/2 s) Interestingly, the neural classifier achieves this performance with a few units, normally just one per mental task Also, since the subject and his/her personal interface learn simultaneously from each other, subjects master it rapidly (in a few days of moderate training) Finally, analysis of learned EEG patterns confirms that for a subject to operate satisfactorily a brain interface, the latter must fit the individual features of the former

/pdf/a-local-neural-classifier-for-the-recognition-of-eeg-2rvsoh28i1.pdf

Pei-bai Zhou

Papers

Numerical Analysis of Electromagnetic Fields