Exploring EEG Features in Cross-Subject Emotion Recognition

By Xiang Li, Dawei Song, Peng Zhang, Yazhou Zhang, Yuexian Hou, Bin Hu. Frontiers in Neuroscience, 2018

347 citations recorded by the source corpus.

Recognizing cross-subject emotions based on brain imaging data, e.g., EEG, has always been difficult due to the poor generalizability of features across subjects. Thus, systematically exploring the ability of different EEG features to identify emotional information across subjects is crucial. Prior related work has explored this question based only on one or two kinds of features, and different findings and conclusions have been presented. In this work, we aim at a more comprehensive investigation on this question with a wider range of feature types, including 18 kinds of linear and non-linear EEG features. The effectiveness of these features was examined on two publicly accessible datasets, namely, the dataset for emotion analysis using physiological signals (DEAP) and the SJTU emotion EEG dataset (SEED). We adopted the support vector machine (SVM) approach and the “leave-one-subject-out” verification strategy to evaluate recognition performance. Using automatic feature selection methods, the highest mean recognition accuracy of 59.06% (AUC = 0.605) on the DEAP dataset and of 83.33% (AUC = 0.904) on the SEED dataset were reached. Furthermore, using manually operated feature selection on the SEED dataset, we explored the importance of different EEG features in cross-subject emotion recognition from multiple perspectives, including different channels, brain regions, rhythms, and feature types. For example, we found that the Hjorth parameter of mobility in the beta rhythm achieved the best mean recognition accuracy compared to the other features. Through a pilot correlation analysis, we further examined the highly correlated features, for a better understanding of the implications hidden in those features that allow for differentiating cross-subject emotions. Various remarkable observations have been made. The results of this paper validate the possibility of exploring robust EEG features in cross-subject emotion recognition.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/fc1c1c3e263449ae5b3ed807160af0fd225e792f

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. Dongrui Wu
2. Yi Ding
3. Qiuhao Zeng
4. Su Zhang
5. Neethu Robinson
6. Bin Hu
7. Zhen Cui
8. Cuntai Guan

In Experimental and Cognitive Psychology

1. Dongrui Wu
2. Bin Hu
3. Zhen Cui
4. Yuan Zong
5. Baoliang Lu
6. Prayag Tiwari
7. Yazhou Zhang
8. Meihong Yang

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.”
• A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition (2021, methodology): “Li et al. [16] proposed another method for cross-subject EEG emotion recognition.”
• Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, positive): “Li et al. [91] extracted nine types of time-frequency domain features (the peak-to-peak mean, mean square, variance, Hjorth activity, Hjorth mobility, Hjorth complexity, maximum power spectral frequency, maximum power spectral density, power sum) and nine types of non-linear dynamical system…”
• EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “[62] extracted 18 widely used EEG features and studied the contribution of each feature in cross-subject emotion recognition according to the results derived from multiple feature selection methods.”