Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition

By Jinpeng Li, Zhaoxiang Zhang, Huiguang He. Cognitive Computation, 2017

304 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a74f7801370dcf1aee1ef688f5e61da233cf14bd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Yi Ding
2. Qiuhao Zeng
3. Su Zhang
4. Neethu Robinson
5. Bin Hu
6. Amir Hussain
7. Mufti Mahmud
8. Cuntai Guan

In Electrical and Electronic Engineering

1. Amir Hussain
2. Mufti Mahmud
3. Cuntai Guan
4. Chunyan Miao
5. Chenglin Liu
6. M. S. Kaiser
7. Stefano Vassanelli
8. Dawei Song

Citations in the corpus

• TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition (2021, methodology): “Different from the methods using manually extracted features [21], [22], [25], [33], [34], EEG signals are fed into TSception directly, which makes it an end-to-end deep learning method that needs less domain knowledge about the features.”
• EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (2019, background): “In contrast, topology-aware classifiers such as CNN [9], [36], [37] and GNN [12] consider the inter-channel topological relations and learn EEG representations for each channel by aggregating features from nearby channels using convolutional operations either in the euclidean space or in the…”
• Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “explored DNN (with Softmax activator and Dropout) and CNN [198] (with Tan Hyperbolic, Max Pooling, Dropout, and Softplus) for emotion classification from the DEAP dataset using EEG signals and response face video [175].”
• Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition (2020, background): “The individual differences of EEG make it difficult to acquire general models that are applicable across persons [8]–[10].”
• EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “[61] organized the differential entropy features from different channels as 2D sparse graphs, which maintains information of the electrode spatial topology, and finally used for CNN training and inferring.”