In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is to use a graph to model the multichannel EEG features and then perform EEG emotion clas...
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networksdoi:10.1109/taffc.2018.2817622Tengfei SongWenming ZhengPeng SongZhen CuiIEEE
Compared with the Koopman models previously used in biophysics and fluid dynamics, the introduction of graph convolutional neural networks enables parameter sharing between the atoms and an encoding of local environments that is invariant to permutation, rotation, and reflection. This symmetry facilitates ...
For example, in 2D or 3D, one can use convolutional networks42, as in image recognition; for sparse networks, graph neural networks are efficient43. Based on the VAN representation, we evaluate Eq. (2) by applying the operator \({e}^{\delta t{{\mathbb{W}}}_{s}}\) sequentially at...
In particular, we use these features to train Convolutional Neural Networks (CNNs). The resulting causal inference mechanism outperforms state-of-the-art counterparts w.r.t. sample-complexity. The trained CNNs generalize well over structurally distinct networks (dense or sparse) and noise-level ...
Mogonet inte- grates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nature Communications, 12(1):1–13, 2021. 1, 2, 5, 8 [63] Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes. On deep ...
Deng, A. & Hooi, B. Graph neural network-based anomaly detection in multivariate time series. In:Proc.Thirty-Fifth AAAI Conference on Artificial Intelligence, 4027–4035 (AAAI Press, 2021).https://ojs.aaai.org/index.php/AAAI/article/view/16523. ...
Unifying Graph Convolutional Neural Networks and Label Propagation, Wang and Leskovec (2020) Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation Scellier and Bengio (2017) Expectation Propagation for Approximate Bayesian Inference, Minka (2001) Propagation Networks: A ...
Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switchin
We compare our proposed algorithm against the widely used baselines including the graph deviation network (GDN), vanilla autoencoder (AE), support vector data description (SVDD), Gaussian process model (GP) and a data-driven battery fault detection algorithm (variation evaluation, VE) on the relea...