Prediction underlying comprehension of human motion: an analysis of Deaf signer and non-signer EEG in response to visual stimuli.

Introduction: Sensory inference and top-down predictive processing, reflected in human neural activity, play a critical role in higher-order cognitive processes, such as language comprehension. However, the neurobiological bases of predictive processing in higher-order cognitive processes are not well-understood. Methods: This study used electroencephalography (EEG) to track participants' cortical dynamics in response to Austrian Sign Language and reversed sign language videos, measuring neural coherence to optical flow in the visual signal. We then used machine learning to assess entropy-based relevance of specific frequencies and regions of interest to brain state classification accuracy. Results: EEG features highly relevant for classification were distributed across language processing-related regions in Deaf signers (frontal cortex and left hemisphere), while in non-signers such features were concentrated in visual and spatial processing regions. Discussion: The results highlight functional significance of predictive processing time windows for sign language comprehension and biological motion processing, and the role of long-term experience (learning) in minimizing prediction error.

Classification of visual comprehension based on EEG data using sparse optimal scoring.

Objective.Understanding and differentiating brain states is an important task in the field of cognitive neuroscience with applications in health diagnostics, such as detecting neurotypical development vs. autism spectrum or coma/vegetative state vs. locked-in state. Electroencephalography (EEG) analysis is a particularly useful tool for this task as EEG data can detect millisecond-level changes in brain activity across a range of frequencies in a non-invasive and relatively inexpensive fashion. The goal of this study is to apply machine learning methods to EEG data in order to classify visual language comprehension across multiple participants.Approach.26-channel EEG was recorded for 24 Deaf participants while they watched videos of sign language sentences played in time-direct and time-reverse formats to simulate interpretable vs. uninterpretable sign language, respectively. Sparse optimal scoring (SOS) was applied to EEG data in order to classify which type of video a participant was watching, time-direct or time-reversed. The use of SOS also served to reduce the dimensionality of the features to improve model interpretability.Main results.The analysis of frequency-domain EEG data resulted in an average out-of-sample classification accuracy of 98.89%, which was far superior to the time-domain analysis. This high classification accuracy suggests this model can accurately identify common neural responses to visual linguistic stimuli.Significance.The significance of this work is in determining necessary and sufficient neural features for classifying the high-level neural process of visual language comprehension across multiple participants.

Engineered nonlinear materials using gold nanoantenna array.

Gold dipole nanoantennas embedded in an organic molecular film provide strong local electromagnetic fields to enhance both the nonlinear refractive index (n2) and two-photon absorption (2PA) of the molecules. An enhancement of 53× for 2PA and 140× for nonlinear refraction is observed for BDPAS (4,4'-bis(diphenylamino)stilbene) at 600 nm with only 3.7% of gold volume fraction. The complex value of the third-order susceptibility enhancement results in a sign change of n2 for the effective composite material relative to the pure BDPAS film. This complex nature of the enhancement and the tunability of the nanoantenna resonance allow for engineering the effective nonlinear response of the composite film.

Information Transfer Capacity of Articulators in American Sign Language.

The ability to convey information is a fundamental property of communicative signals. For sign languages, which are overtly produced with multiple, completely visible articulators, the question arises as to how the various channels co-ordinate and interact with each other. We analyze motion capture data of American Sign Language (ASL) narratives, and show that the capacity of information throughput, mathematically defined, is highest on the dominant hand (DH). We further demonstrate that information transfer capacity is also significant for the non-dominant hand (NDH), and the head channel too, as compared to control channels (ankles). We discuss both redundancy and independence in articulator motion in sign language, and argue that the NDH and the head articulators contribute to the overall information transfer capacity, indicating that they are neither completely redundant to, nor completely independent of, the DH.

Numerical modeling of plasmonic nanoantennas with realistic 3D roughness and distortion.

Nanostructured plasmonic metamaterials, including optical nanoantenna arrays, are important for advanced optical sensing and imaging applications including surface-enhanced fluorescence, chemiluminescence, and Raman scattering. Although designs typically use ideally smooth geometries, realistic nanoantennas have nonzero roughness, which typically results in a modified enhancement factor that should be involved in their design. Herein we aim to treat roughness by introducing a realistic roughened geometry into the finite element (FE) model. Even if the roughness does not result in significant loss, it does result in a spectral shift and inhomogeneous broadening of the resonance, which could be critical when fitting the FE simulations of plasmonic nanoantennas to experiments. Moreover, the proposed approach could be applied to any model, whether mechanical, acoustic, electromagnetic, thermal, etc, in order to simulate a given roughness-generated physical phenomenon.

Drude relaxation rate in grained gold nanoantennas.

The effect of grain boundaries on the electron relaxation rate is significant even for large area noble metal films and more so for plasmonic nanostructures. Optical spectroscopy and X-ray diffraction show a substantial improvement in plasmon resonance quality for square-particle nanoantennas after annealing due to an enlarged grain size from 22 to 40 nm and improved grain boundaries described by the electron reflection coefficient. The electron relaxation rate due to the grains is shown to decrease by a factor of 3.2.