Department Publications

Neural processing along the ascending auditory pathway is often associated with a progressive reduction in characteristic processing rates. For instance, the well-known frequency-following response (FFR) of the auditory midbrain, as measured with electroencephalography (EEG), is dominated by frequencies from ∼100 Hz to several hundred Hz, phase-locking to the acoustic stimulus at those frequencies. In contrast, cortical responses, whether measured by EEG or magnetoencephalography (MEG), are typically characterized by frequencies of a few Hz to a few tens of Hz, time-locking to acoustic envelope features. In this study we investigated a crossover case, cortically generated responses time-locked to continuous speech features at FFR-like rates. Using MEG, we analyzed responses in the high gamma range of 70–200 Hz to continuous speech using neural source-localized reverse correlation and the …

Stroke patients with small central nervous system infarcts often demonstrate an acute dysexecutive syndrome characterized by difficulty with attention, concentration, and processing speed, independent of lesion size or location. We use magnetoencephalography (MEG) to show that disruption of network dynamics may be responsible. Nine patients with recent minor strokes and eight age-similar controls underwent cognitive screening using the Montreal cognitive assessment (MoCA) and MEG to evaluate differences in cerebral activation patterns. During MEG, subjects participated in a visual picture–word matching task. Task complexity was increased as testing progressed. Cluster-based permutation tests determined differences in activation patterns within the visual cortex, fusiform gyrus, and lateral temporal lobe. At visit 1, MoCA scores were significantly lower for patients than controls (median [interquartile range …

We consider the problem of determining Granger causal influences among sources that are indirectly observed through low-dimensional and noisy linear projections. Commonly used methods proceed in a two-stage fashion, by first solving an inverse problem to localize the sources, and then inferring the Granger causal influences from the estimated sources. The inferred Granger causal links thus inherit the various biases that are used in source localization techniques, in the form of spatiotemporal priors designed in favor of spatial localization. In addition, this approach does not account for the structural properties of the underlying functional networks such as sparsity of the links. We address these issues by modeling the source dynamics as sparse vector autoregressive processes and estimate the model parameters directly from the observations, with no recourse to intermediate source localization. We evaluate …

Light field saliency detection—important due to utility in many vision tasks—still lack speed and can improve in accuracy. Due to the formulation of the saliency detection problem in light fields as a segmentation task or a “memorizing” tasks, existing approaches consume unnecessarily large amounts of computational resources for (training and) testing leading to execution times is several seconds. We solve this by aggressively reducing the large light-field images to a much smaller three-channel feature map appropriate for saliency detection using an RGB image saliency detector. We achieve this by introducing a novel convolutional neural network based features extraction and encoding module. Our saliency detector takes 0.4 s to process a light field of size 9× 9× 512× 375 in a CPU and is significantly faster than existing systems, with better or comparable accuracy. Our work shows that extracting features from light fields through aggressive size reduction and the attention results in a faster and accurate light-field saliency detector.