An objective and accurate emotion diagnostic reference is vital to
psychologists, especially when dealing with patients who are difficult to
communicate with for pathological reasons. Nevertheless, current systems based
on Electroencephalography (EEG) data utilized for sentiment discrimination have
some problems, including excessive model complexity, mediocre accuracy, and
limited interpretability. Consequently, we propose a novel and effective
feature fusion mechanism named Mutual-Cross-Attention (MCA). Combining with a
specially customized 3D Convolutional Neural Network (3D-CNN), this purely
mathematical mechanism adeptly discovers the complementary relationship between
time-domain and frequency-domain features in EEG data. Furthermore, the new
designed Channel-PSD-DE 3D feature also contributes to the high performance.
The proposed method eventually achieves 99.49% (valence) and 99.30% (arousal)
accuracy on DEAP dataset.

我们提出了一种名为 Mutual-Cross-Attention（MCA）的新颖有效的特征融合机制，结合特殊定制的 3D 卷积神经网络（3D-CNN），该方法巧妙地发现了 EEG 数据中时域和频域特征之间的互补关系，并且通过新设计的 Channel-PSD-DE 3D 特征在 DEAP 数据集上获得了 99.49%（愉悦度）和 99.30%（唤醒度）的准确率。

基于互相交叉注意机制的特征融合用于 EEG 情绪识别

Feature Fusion Based on Mutual-Cross-Attention Mechanism for EEG Emotion  Recognition

The human brain is a complex organ, still completely undiscovered, that
controls almost all the parts of the body. Apart from survival, the human brain
stimulates emotions. Recent research indicates that brain signals can be very
effective for emotion recognition. However, which parts of the brain exhibit
most of the emotions is still under-explored. In this study, we empirically
analyze the contribution of each part of the brain in exhibiting emotions. We
use the DEAP dataset to find the most optimal electrode set which eventually
leads to the effective brain part associated with emotions. We use Fast Fourier
Transformation for effective feature extraction and a 1D-CNN with residual
connection for classification. Though 32 electrodes from the DEAP dataset got
an accuracy of 97.34%, only 12 electrodes (F7, P8, O1, F8, C4, T7, PO3, Fp1,
Fp2, O2, P3, and Fz) achieve 95.81% accuracy. This study also shows that adding
more than 10 electrodes does not improve performance significantly. Moreover,
the frontal lobe is the most important for recognizing emotion.

人脑是一个复杂的器官，主要控制身体的各个部分，同时也影响情绪。最近的研究表明，脑信号对情绪识别非常有效，但大部分情绪主要集中于哪些脑部尚未被充分探索。本研究通过实证分析了各个脑部在情绪表现中的贡献，并利用 DEAP 数据集找到与情绪相关的最优电极组合。研究采用快速傅里叶变换进行有效特征提取，同时通过带有残差连接的一维卷积神经网络进行分类。实验结果表明，从 DEAP 数据集中的 32 个电极中，只有 12 个电极（F7、P8、O1、F8、C4、T7、PO3、Fp1、Fp2、O2、P3 和 Fz）可以达到 95.81% 的准确率，而添加超过 10 个电极并不能显著提高性能。此外，额叶对情绪识别最为重要。