School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, 621010, China.
School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, 621010, China; MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
Comput Biol Med. 2022 Oct;149:106002. doi: 10.1016/j.compbiomed.2022.106002. Epub 2022 Aug 17.
In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) field. The neuroscience researches indicate that the left and right brain hemispheres demonstrate activity differences under different emotional activities, which could be an important principle for designing deep learning (DL) model for emotion recognition. Besides, owing to the nonstationarity of EEG signals, using convolution kernels of a single size may not sufficiently extract the abundant features for EEG classification tasks. Based on these two angles, we proposed a model termed Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM) based on convolutional neural network (CNN) structure. Evaluated on the public DEAP and DREAMER datasets, MSBAM achieved over 99% accuracy for the two-class classification of low-level and high-level states in each of four emotional dimensions, i.e., arousal, valence, dominance and liking, respectively. This study further demonstrated the promising potential to design the DL model from the multi-scale characteristics of the EEG data and the neural mechanisms of the emotion cognition.
近年来，基于脑电图（EEG）的情绪识别在脑机接口（BCI）领域受到越来越多的关注。神经科学研究表明，左右大脑半球在不同的情绪活动下表现出活动差异，这可能是设计深度学习（DL）模型进行情绪识别的重要原则。此外，由于 EEG 信号的非平稳性，使用单一大小的卷积核可能无法充分提取 EEG 分类任务的丰富特征。基于这两个角度，我们提出了一种基于卷积神经网络（CNN）结构的多尺度双半球不对称模型（MSBAM）。在公共的 DEAP 和 DREAMER 数据集上进行评估，MSBAM 在四个情绪维度（即唤醒度、效价、主导度和喜欢度）的每个维度中，对低水平和高水平状态的两类分类均达到了 99%以上的准确率。这项研究进一步证明了从 EEG 数据的多尺度特征和情绪认知的神经机制出发设计 DL 模型的有前途的潜力。
