Linking Multi-Layer Dynamical GCN With Style-Based Recalibration CNN for EEG-Based Emotion Recognition

We performed an updated quantitative meta-analysis of 162 neuroimaging studies of emotion using a novel multi-level kernel-based approach, focusing on locating brain regions consistently activated in emotional tasks and their functional organization into distributed functional groups, independent of semantically defined emotion category labels (e.g., "anger," "fear"). Such brain-based analyses are critical if our ways of labeling emotions are to be evaluated and revised based on consistency with brain data. Consistent activations were limited to specific cortical sub-regions, including multiple functional areas within medial, orbital, and inferior lateral frontal cortices. Consistent with a wealth of animal literature, multiple subcortical activations were identified, including amygdala, ventral striatum, thalamus, hypothalamus, and periaqueductal gray. We used multivariate parcellation and clustering techniques to identify groups of co-activated brain regions across studies. These analyses identified six distributed functional groups, including medial and lateral frontal groups, two posterior cortical groups, and paralimbic and core limbic/brainstem groups. These functional groups provide information on potential organization of brain regions into large-scale networks. Specific follow-up analyses focused on amygdala, periaqueductal gray (PAG), and hypothalamic (Hy) activations, and identified frontal cortical areas co-activated with these core limbic structures. While multiple areas of frontal cortex co-activated with amygdala sub-regions, a specific region of dorsomedial prefrontal cortex (dmPFC, Brodmann's Area 9/32) was the only area co-activated with both PAG and Hy. Subsequent mediation analyses were consistent with a pathway from dmPFC through PAG to Hy. These results suggest that medial frontal areas are more closely associated with core limbic activation than their lateral counterparts, and that dmPFC may play a particularly important role in the cognitive generation of emotional states.

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin. Published as a conference paper at ICLR 2017