EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations

By Peiyang Li, Huan Liu, Yajing Si, Cunbo Li, Fali Li, Xuyang Zhu, Xiaoye Huang, Ying Zeng, Dezhong Yao, Yangsong Zhang and 1 more. IEEE Transactions on Biomedical Engineering, 2019

333 citations recorded by the source corpus.

Objective: Spectral power analysis plays a predominant role in electroencephalogram-based emotional recognition. It can reflect activity differences among multiple brain regions. In addition to activation difference, different emotions also involve different large-scale network during related information processing. In this paper, both information propagation patterns and activation difference in the brain were fused to improve the performance of emotional recognition. Methods: We constructed emotion-related brain networks with phase locking value and adopted a multiple feature fusion approach to combine the compensative activation and connection information for emotion recognition. Results: Recognition results on three public emotional databases demonstrated that the combined features are superior to either single feature based on power distribution or network character. Furthermore, the conducted feature fusion analysis revealed the common characters between activation and connection patterns involved in the positive, neutral, and negative emotions for information processing. Significance: The proposed feasible combination of both information propagation patterns and activation difference in the brain is meaningful for developing the effective human–computer interaction systems by adapting to human emotions in the real world applications.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/5ebef85aa9c97c0cbf60e095e82fcd15ba00a81d

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. Chunyan Miao
2. Di Wang
3. Peixiang Zhong
4. Yi Ding
5. Qiuhao Zeng
6. Su Zhang
7. Neethu Robinson
8. Rencheng Song

In Experimental and Cognitive Psychology

1. Chunyan Miao
2. Di Wang
3. Peixiang Zhong
4. Rencheng Song
5. Tengfei Song
6. Feng Wan
7. Zhen Cui
8. Yuan Zong

Citations in the corpus

• TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition (2021, background): “…extreme learning machine to investigate stable patterns over time from the differential entropy (DE) features of emotional EEG. Li et al. [14] utilized phase-locking value to construct emotion-related brain networks with multiple feature fusion to detect emotions from EEG. Recently, deep…”
• A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition (2019, background): “Li et al. [11] fused the information propagation patterns and activation difference in the brain to improve the performance of emotional recognition.”
• Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, positive): “The functional connection network was integrated with local activation by the authors in [46] to depict the activity of local parts of the brain that reacts to emotions and reflects the interactions between critical brain areas.”
• EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (2019, methodology): “…multiple channels to capture the inter-channel relations, e.g., the asymmetry of PSD between two hemispheres [7] and functional connectivity [29], [30], where common indices such as correlation, coherence and phase synchronization were used estimate brain functional connectivity between channels.”
• EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention (2023, methodology): “…(PLV) and adopted a multiple feature fusion approach to combine the compensative activation and connection information for emotion recognition [55], Wang et al. combined brain directed connectivity (BDC) and DE features which are in different frequency bands among brain areas to extract…”