School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China.
Academic Affairs Office, Changchun University, Changchun 130022, China.
Math Biosci Eng. 2023 Jan;20(2):2566-2587. doi: 10.3934/mbe.2023120. Epub 2022 Nov 24.
Emotion recognition is of a great significance in intelligent medical treatment and intelligent transportation. With the development of human-computer interaction technology, emotion recognition based on Electroencephalogram (EEG) signals has been widely concerned by scholars. In this study, an EEG emotion recognition framework is proposed. Firstly, variational mode decomposition (VMD) is used to decompose the nonlinear and non-stationary EEG signals to obtain intrinsic mode functions (IMFs) at different frequencies. Then sliding window tactic is used to extract the characteristics of EEG signals under different frequency. Aiming at the issue of feature redundancy, a new variable selection method is proposed to improve the adaptive elastic net (AEN) by the minimum common redundancy maximum relevance criterion. Weighted cascade forest (CF) classifier is constructed for emotion recognition. The experimental results on the public dataset DEAP show that the valence classification accuracy of the proposed method reaches 80.94%, and the classification accuracy of arousal is 74.77%. Compared with some existing methods, it effectively improves the accuracy of EEG emotion recognition.
情感识别在智能医疗和智能交通中具有重要意义。随着人机交互技术的发展，基于脑电图（EEG）信号的情感识别已经引起了学者们的广泛关注。在这项研究中，提出了一种 EEG 情感识别框架。首先，使用变分模态分解（VMD）将非线性和非平稳的 EEG 信号分解为不同频率的固有模态函数（IMFs）。然后，使用滑动窗口策略提取不同频率下 EEG 信号的特征。针对特征冗余问题，提出了一种新的变量选择方法，通过最小冗余最大相关性准则来改进自适应弹性网（AEN）。构建加权级联森林（CF）分类器进行情感识别。在公共数据集 DEAP 上的实验结果表明，所提出方法的效价分类准确率达到 80.94%，唤醒度的分类准确率为 74.77%。与一些现有的方法相比，该方法有效地提高了 EEG 情感识别的准确性。
