Stressful events, such as those imposed by the COVID-19 pandemic, are associated with depression risk, raising questions about processes that make some people more susceptible to effects of stress on mental health than others. Emotion regulation may be a key process, but methods for objectively measuring emotion abilities in youth limited. We leveraged event-related potential (ERP) measures and longitudinal study adolescents oversampled risk examine difficulties prospective predictors...

It is well-established that aging impairs memory for associations more than it does single items. Aging also impacts processes involved in online language comprehension, including the ability to form integrated, message-level representations. These changes comprehension could impact older adults' associative performance, perhaps by reducing or altering effectiveness of encoding strategies encourage semantic integration. The present study examined age differences use a strategy termed...

Features extracted from the wavelet transform coefficient matrix are widely used in design of machine learning models to classify event-related potential (ERP) and electroencephalography (EEG) signals a wide range brain activity research clinical studies. This novel study is aimed at dramatically improving performance such wavelet-based classifiers by exploiting information offered cone influence (COI) continuous (CWT). The COI boundary that superimposed on scalogram delineate coefficients...

Threat-related attention bias is thought to contribute the development and maintenance of anxiety disorders. Dot-probe studies using event-related potentials (ERPs) have indicated that several early ERP components are modulated by threatening emotional stimuli in anxious populations, suggesting enhanced allocation threat emotion at earlier stages processing. However, selected for examination analysis these vary widely remain inconsistent. The present study used temporospatial principal...

Two multimodal classification models aimed at enhancing object through the integration of semantically congruent unimodal stimuli are introduced. The feature-integrating model, inspired by multisensory in subcortical superior colliculus, combines features which subsequently classified a classifier. decision-integrating primary cortical areas, classifies independently using classifiers and combined decisions classifier implemented multilayer perceptrons multivariate statistical classifiers....

This paper introduces two multisensory object classification models inspired by integration in the brain. The "feature-integrating" model emulates sub-cortical superior colliculus combining unisensory features which are subsequently classified a classifier. In "decision-integrating" model, primary cortical areas, stimuli first independently classifiers and results combined implemented using multilayer perceptron classifiers. Through several sets of experiments involving auditory visual...

Two convolution neural network (CNN) models are introduced to accurately classify event-related potentials (ERPs) by fusing frequency, time, and spatial domain information acquired from the continuous wavelet transform (CWT) of ERPs recorded multiple spatially distributed channels. The multidomain fuse multichannel Z-scalograms V-scalograms, which generated standard CWT scalogram zeroing-out discarding inaccurate artifact coefficients that outside cone influence (COI), respectively. In first...

Alterations in attention to cues signaling the need for inhibitory control play a significant role wide range of psychopathology. However, degree which motivational and attentional factors shape neurocomputations proactive remains poorly understood. The present study investigated how variation monetary incentive valence stake modulate neurocomputational signatures control. Adults ( N = 46) completed Stop-Signal Task (SST) with concurrent EEG recording under four conditions associated stop...

The brain uses contextual information to uniquely resolve the interpretation of ambiguous stimuli. This paper introduces a deep learning neural network classification model that emulates this ability by integrating weighted bidirectional context into process. model, referred as CINET, is implemented using convolution (CNN), which shown be ideal for combining target and stimuli extracting coupled target-context features. CINET parameters can manipulated simulate congruent incongruent...

The brain uses contextual information to uniquely resolve the interpretation of ambiguous stimuli. An interdisciplinary effort which combines expertise in machine learning and neuroscience is used formulate a generalized signal classification model that has ability integrate weighted bidirectional temporal or spatial context effectively formulation quite general; consequently, it not restricted stimuli any particular sensory modality nor type classifier. Furthermore, parameters can be...