Su Yun, Zhang Zhixuan, Li Xuan, Zhang Bingtao, Ma Huifang
School of Computer Science and Engineering, Northwest Normal University, Lanzhou, China.
School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China.
Front Neurosci. 2022 Aug 15;16:872311. doi: 10.3389/fnins.2022.872311. eCollection 2022.
Emotion recognition based on EEG (electroencephalogram) has become a research hotspot in the field of brain-computer interfaces (BCI). Compared with traditional machine learning, the convolutional neural network model has substantial advantages in automatic feature extraction in EEG-based emotion recognition. Motivated by the studies that multiple smaller scale kernels could increase non-linear expression than a larger scale, we propose a 3D convolutional neural network model with multiscale convolutional kernels to recognize emotional states based on EEG signals. We select more suitable time window data to carry out the emotion recognition of four classes (low valence vs. low arousal, low valence vs. high arousal, high valence vs. low arousal, and high valence vs. high arousal). The results using EEG signals in the DEAP and SEED-IV datasets show accuracies for our proposed emotion recognition network model (ERN) of 95.67 and 89.55%, respectively. The experimental results demonstrate that the proposed approach is potentially useful for enhancing emotional experience in BCI.
基于脑电图（EEG）的情感识别已成为脑机接口（BCI）领域的研究热点。与传统机器学习相比，卷积神经网络模型在基于EEG的情感识别自动特征提取方面具有显著优势。受多个较小尺度内核比单个较大尺度内核能增加非线性表达能力的研究启发，我们提出一种具有多尺度卷积内核的三维卷积神经网络模型，用于基于EEG信号识别情绪状态。我们选择更合适的时间窗口数据来进行四类（低效价与低唤醒、低效价与高唤醒、高效价与低唤醒、高效价与高唤醒）的情感识别。使用DEAP和SEED-IV数据集中的EEG信号得到的结果表明，我们提出的情感识别网络模型（ERN）的准确率分别为95.67%和89.55%。实验结果表明，该方法对于增强BCI中的情感体验具有潜在的应用价值。
