.png){kind=icon}
返回
Variational Instance-Adaptive Graph for EEG Emotion Recognition
.png){kind=icon}
PRE
AI
DOI：10.1109/TAFFC.2021.3064940.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The individual differences and the dynamic uncertain relationships among different electroencephalogram (EEG) regions are essential factors that limit EEG emotion recognition. To address these issues, in this article, we propose a variational instance-adaptive graph method (V-IAG) that simultaneously captures the individual dependencies among different EEG electrodes and estimates the underlying uncertain information. Specifically, we employ two branches, i.e., instance-adaptive branch and variational branch, to construct the graph. Inspired by the attention mechanism, the instance-adaptive branch generates the graph based on the input so as to characterize the individual dependencies among EEG channels. The variational branch generates the probabilistic graph, which quantifies the uncertainties. We combine these two types of graphs to extract more discriminative features. To present more precise graph representation, we propose a new operation named the multi-level and multi-graph convolution operation, which aggregates the features of EEG channels from different frequencies with different graphs. Furthermore, we design the graph coarsening and employ the sparse constraint to obtain more robust features. We conduct extensive experiments on three widely-used EEG emotion recognition databases, i.e., SJTU emotion EEG dataset (SEED), multi-modal physiological emotion recognition dataset (MPED) and DREAMER. The results demonstrate that the proposed model achieves the-state-of-the-art performance.
Keyword:
Electroencephalography
Emotion recognition
Feature extraction
Brain modeling
Time-frequency analysis
Electrodes
Uncertainty
EEG emotion recognition
graph neural network
variational inference
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.8
论文数:
1.4K
被引数:
9.1K
机构
引用论文
.png){kind=icon}
分享收藏
Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism基于注意力机制的CNN遮挡感知面部表情识别
PREAI
分享收藏DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices梦想家: 通过来自无线低成本现成设备的EEG和ECG信号进行情感识别的数据库
OAAI
OAAI分享收藏
查看更多内容