Informed decomposition of electroencephalographic data.

BACKGROUND: Blind source separation techniques have become the de facto standard for decomposing electroencephalographic (EEG) data. These methods are poorly suited for incorporating prior information into the decomposition process. While alternative techniques to this problem, such as the use of constrained optimization techniques, have been proposed, these alternative techniques tend to only minimally satisfy the prior constraints. In addition, the experimenter must preset a number of parameters describing both this minimal limit as well as the size of the target subspaces. NEW METHOD: We propose an informed decomposition approach that builds upon the constrained optimization approaches for independent components analysis to better model and separate distinct subspaces within EEG data. We use a likelihood function to adaptively determine the optimal model size for each target subspace. RESULTS: Using our method we are able to produce ordered independent subspaces that exhibit less residual mixing than those obtained with other methods. The results show an improvement in modeling specific features of the EEG space, while also showing a simultaneous reduction in the number of components needed for each model. COMPARISON WITH EXISTING METHOD(S): We first compare our approach to common methods in the field of EEG decomposition, such as Infomax, FastICA, PCA, JADE, and SOBI for the task of modeling and removing both EOG and EMG artifacts. We then demonstrate the utility of our approach for the more complex problem of modeling neural activity. CONCLUSIONS: By working in a one-size-fits-all fashion current EEG decomposition methods do not adapt to the specifics of each data set and are not well designed to incorporate additional information about the decomposition problem. However, by adding specific information about the problem to the decomposition task, we improve the identification and separation of distinct subspaces within the original data and show better preservation of the remaining data.

MEG correlates of bimodal encoding of faces and persons' names.

Learning associations between people's faces and names is a universal cognitive function with important social implications. The goal of the present study was to examine brain activity patterns associated with cross-modal encoding of names and faces. Learning face-name pairs was compared to unimodal learning tasks using the same visual and auditory stimuli. Spatiotemporal brain activation profiles were obtained with magnetoencephalography using an automated source estimation method. Results showed activation foci in left (for names) and right (for faces) temporal lobe perisylvian cortices, predominantly right-hemisphere occipital and occipitotemporal regions (for faces), and right hemisphere dorsolateral prefrontal regions during the encoding phase for both types of stimuli presented in isolation. Paired (face-name) stimulus presentation elicited bilateral prefrontal and temporal lobe perisylvian activity for faces and enhanced visual cortex activation in response to names (compared to names in the unpaired condition). These findings indicate distinct patterns of brain activation during the formation of associations between meaningful visual and auditory stimuli.

Functional neuroimaging with MEG: normative language profiles.

The reliability of language-specific brain activation profiles was assessed using Magnetoencephalography (MEG) in five experiments involving ninety-seven normal volunteers of both genders ranging in age from seven to eighty-four years. MEG data were analyzed with a fully automated method to eliminate subjective judgments in the process of deriving the activation profiles. Across all experiments, profiles were characterized by significant bilateral activity centered in the superior temporal gyrus, and in activity lateralized to the left middle temporal gyrus. These features were invariant across age, gender, variation in task characteristics, and mode of stimulus presentation. The absolute amount of activation, however, did decline with age in the auditory tasks. Moreover, contrary to the commonly held belief that left hemisphere dominance for language is greater in men than in women, our data revealed an opposite albeit a not consistently significant trend.