Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition

Abstract

Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (EEG) signals can record the neural activities across the human brain, providing us a reliable way to recognize the emotional states. Most of existing EEG-based emotion recognition studies directly concatenated features extracted from all EEG frequency bands for emotion classification. This way assumes that all frequency bands share the same importance by default; however, it cannot always obtain the optimal performance. In this paper, we present a novel multi-scale frequency bands ensemble learning (MSFBEL) method to perform emotion recognition from EEG signals. Concretely, we first re-organize all frequency bands into several local scales and one global scale. Then we train a base classifier on each scale. Finally we fuse the results of all scales by designing an adaptive weight learning method which automatically assigns larger weights to more important scales to further improve the performance. The proposed method is validated on two public data sets. For the “SEED IV” data set, MSFBEL achieves average accuracies of 82.75%, 87.87%, and 78.27% on the three sessions under the within-session experimental paradigm. For the “DEAP” data set, it obtains average accuracy of 74.22% for four-category classification under 5-fold cross validation. The experimental results demonstrate that the scale of frequency bands influences the emotion recognition rate, while the global scale that directly concatenating all frequency bands cannot always guarantee to obtain the best emotion recognition performance. Different scales provide complementary information to each other, and the proposed adaptive weight learning method can effectively fuse them to further enhance the performance.

Multi-Frequent Band Collaborative EEG Emotion Classification Method Based on Optimal Projection and Shared Dictionary Learning

“…Similar to the feature extraction strategy in subsection "Experiments on the SEED Dataset, " PSD features are used in the DEAP dataset. Following (Shen et al, 2021), four frequency bands (θ, α, β, γ) are used in the experiment.…”

“…Different frequency band EEG signals reflect the different states of brain state. Table 1 briefly describes the information of five frequent bands of EEG signals ( Gu et al, 2021a ; Shen et al, 2021 ). Many scholars have studied EEG signals in different frequency bands.…”

A Class-Incremental Learning Method Based on Preserving the Learned Feature Space for EEG-Based Emotion Recognition

“…Furthermore, EEG is a non-invasive device, easy to use, and has a low cost [4,13]. Thus, EEG has been widely used in emotion recognition systems in the last years [3,8,[13][14][15][16][17][18].…”

“…The collected EEG signals are usually analyzed in three categories to extract discriminative features: time domain (e.g., statistics of signal), frequency domain (e.g., differential entropy), and time-frequency domain (e.g., Fourier transform). In this direction, many methods have been proposed via machine learning to leverage the features extracted from EEG signals [17][18][19]. Recently, several methods are gradually moving towards the deep learning-based approaches, becoming dominant in EEG-based emotion recognition [3,8,[13][14][15][16]20,21].…”

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder

“…In the DEAP dataset, this algorithm can achieve 74, 78, 80, and 86.27% in recognition accuracy of four states of arousal, valence, dominance, and preference, respectively. In Shen et al (2021) , applied multi-scale frequency bands ensemble learning to identify emotional state and achieved average recognition accuracy of 74.22% in the DEAP dataset.…”