Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, an...
Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, t...
Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC beca...
Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-blinks, saccades, and cardiac activity are three of the ...
In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words, which lack a physical referent. Hebbian formalism and optimization are basic principles of b...
Between subject variability in the spatial and spectral structure of oscillatory networks can be highly informative but poses a considerable analytic challenge. Here, we describe a data-driven modal decomposition of a multivariate autoregressive mode...
We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned to single and multiple time point MEG data, and can estimate varying numbers of dipole s...
OBJECTIVE: One barrier hindering high frequency brain signals (HFBS, >80 Hz) from wide clinical applications is that the brain generates both pathological and physiological HFBS. This study was to find specific biomarkers for localizing epileptogenic...
While vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understoo...
Auditory event-related fields (ERFs) measured with magnetoencephalography (MEG) are useful for studying the neuronal underpinnings of auditory cognition in human cortex. They have a highly subject-specific morphology, albeit certain characteristic de...
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