The locations of active brain areas can be estimated from the magnetic field the neural current sources produce. In this work we study a visualization method of magnetoencephalographic data that is based on minimum[symbol: see text] (1)-norm estimates. The method can represent several local or distributed sources and does not need explicit a priori information. We evaluated the performance of the method using simulation studies.
In the last decade, large arrays of sensors for magnetoencephalography (MEG) (and electroencephalography (EEG)) have become more commonplace, allowing new opportunities for the application of beamforming techniques to the joint problems of signal estimation and noise reduction. We introduce a new approach to noise cancellation, the generalized sidelobe canceller (GSC), itself an alternative to the linearly constrained minimum variance (LCMV) algorithm. The GSC framework naturally fits within the other noise reduction techniques that employ real or virtual reference arrays.
In this paper we describe the instrumentation for biomagnetic measurements available in our laboratory. The focus is on our 24-channel planar gradiometer system. In addition, a 122-channel system under construction will be discussed.
We recorded middle-latency auditory evoked magnetic fields from 9 healthy subjects with a 122-channel whole-head SQUID gradiometer. The stimuli were click triplets, 2.5 msec in total duration, delivered alternately to the two ears once every 333 msec. Contralateral clicks elicited P30m responses in 16 and P50m responses in 12 out of 18 hemispheres studied; ipsilateral clicks did so in 7 and 13 hemispheres, respectively.
Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitude and location of the neural sources will be ill-conditioned because of the underdetermined nature of the problem.
Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion.
Whole-cortex magnetometers represent a significant methodological breakthrough in noninvasive studies of the brain's electrical activity. This paper describes our 122-channel instrument with planar gradiometers, methods to interpret its data, and gives a few examples of neuromagnetic studies.
Recording of the magnetic fields of the brain, magnetoencephalography (MEG), has proved to be a valuable method in neurophysiological research. In order to study the feasibility of MEG recording during anaesthesia we recorded magnetoecephalographic burst suppression in a dog during enflurane and propofol anaesthesia. The observed signal distribution implies a complex current distribution underlying the burst activity. This experiment also proves that an essentially artefact-free MEG recording can be obtained during respirator-assisted anaesthesia.
Magnetoencephalography (MEG) is an important non-invasive method for studying activity within the human brain. Source localization methods can be used to estimate spatiotemporal activity from MEG measurements with high temporal resolution, but the spatial resolution of these estimates is poor due to the ill-posed nature of the MEG inverse problem. Recent developments in source localization methodology have emphasized temporal as well as spatial constraints to improve source localization accuracy, but these methods can be computationally intense.
Multichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data. Recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli.
