2017/05/13 by Yiluan Guo, Guo, Yiluan, Hossein Nejati +3 · 1 citation
Computer Science · Neuroscience · #Advanced Graph Neural Networks #Computer Vision and Pattern Recognition (cs.CV) #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Functional Brain Connectivity Studies
paper · pdf · doi:10.48550/arxiv.1705.04828
openalex publication_date 2017/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal data often degraded by complex, non-Gaussian noise. For reliable analysis of brain imaging data, it is important to extract discriminative, low-dimensional intrinsic representation of the recorded data. This work proposes a new method to learn the low-dimensional representations from the noise-degraded measurements. In particular, our work proposes a new deep neural network design that integrates graph information such as brain connectivity with fully-connected layers. Our work leverages efficient graph filter design using Chebyshev polynomial and recent work on convolutional nets on graph-structured data. Our approach exploits graph structure as the prior side information, localized graph filter for feature extraction and neural networks for high capacity learning. Experiments on real MEG datasets show that our approach can extract more discriminative representations, leading to improved accuracy in a supervised classification task.