EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM

By Yongqiang Yin, Xiangwei Zheng, Bin Hu, Yuang Zhang, Xinchun Cui. Applied Soft Computing, 2021

416 citations recorded by the source corpus.

Abstract In recent years, graph convolutional neural networks have become research focus and inspired new ideas for emotion recognition based on EEG. Deep learning has been widely used in emotion recognition, but it is still challenging to construct models and algorithms in practical applications. In this paper, we propose a novel emotion recognition method based on a novel deep learning model (ERDL). Firstly, EEG data is calibrated by 3s baseline data and divided into segments with 6s time window, and then differential entropy is extracted from each segment to construct feature cube. Secondly, the feature cube of each segment serves as input of the novel deep learning model which fuses graph convolutional neural network (GCNN) and long-short term memories neural networks (LSTM). In the fusion model, multiple GCNNs are applied to extract graph domain features while LSTM cells are used to memorize the change of the relationship between two channels within a specific time and extract temporal features, and Dense layer is used to attain the emotion classification results. At last, we conducted extensive experiments on DEAP dataset and experimental results demonstrate that the proposed method has better classification results than the state-of-the-art methods. We attained the average classification accuracy of 90.45% and 90.60% for valence and arousal in subject-dependent experiments while 84.81% and 85.27% in subject-independent experiments.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Urban Studies

View Paper: https://www.semanticscholar.org/paper/e5c4602c4d0e8880240616618596ef72bde773a9

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. Bin Hu
2. Prayag Tiwari
3. Yazhou Zhang
4. Meihong Yang
5. Xiang Li
6. Zhigang Zhao
7. Pekka Marttinen
8. Neeraj Kumar

In Experimental and Cognitive Psychology

1. Bin Hu
2. Prayag Tiwari
3. Yazhou Zhang
4. Meihong Yang
5. Xiang Li
6. Zhigang Zhao
7. Pekka Marttinen
8. Neeraj Kumar

Citations in the corpus

• EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “[163] DEAP a GCNN-LSTM hybrid model, named ECGGCNN 2 classes, subject-dependent modeling (Valence: 90.”