Traditional brain-computer systems are complex and expensive, and emotion classification algorithms lack repre-sentations of the intrinsic relationships between different channels of electroencephalogram (EEG) signals. There is still room for improvement in accuracy. To lower the research barrier for EEG and harness the rich information embedded in multi-channel EEG, we propose and implement a simple and user-friendly brain-computer system for classifying four emotions: happiness, sorrow, sadness, and tranquility. This system utilizes the fusion of convolutional attention mechanisms and fully pre-activated residual blocks, termed Attention-Convolution-based Pre-Activated Residual Network (ACPA-ResNet).In the hardware acquisition and preprocessing phase, we employ the ADS1299 integrated chip as the analog front-end and utilize the ESP32 microcontroller for initial EEG signal processing. Data is wirelessly transmitted to a PC through UDP protocol for further preprocessing. In the emotion analysis phase, ACPA-ResNet is designed to automatically extract and learn features from EEG signals, thereby enabling accurate classification of emotional states by learning time-frequency domain characteristics. ACPA-ResNet introduces an attention mechanism on the foundation of residual networks, adaptively assigning different weights to each channel. This allows it to focus on more meaningful EEG signals in both spatial and channel dimensions while avoiding the problems of gradient dispersion and explosion associated with deep network architectures.Through testing on 16 subjects, our system demonstrates stable EEG signal acquisition and transmission. The novel network significantly enhances emotion recognition accuracy, achieving an average emotion classification accuracy of 95.1%.

翻译：传统脑机系统复杂昂贵，且情感分类算法缺乏对脑电信号多通道间内在关联的表征，准确率仍有提升空间。为降低脑电研究门槛并充分利用多通道脑电的丰富信息，我们设计并实现了一种简易友好、可分类喜悦、愤怒、悲伤、平静四种情感的脑机系统。该系统采用卷积注意力机制与全预激活残差块的融合架构，称为基于注意力卷积的预激活残差网络。在硬件采集与预处理阶段，我们采用ADS1299集成芯片作为模拟前端，利用ESP32微控制器进行脑电信号初步处理，数据通过UDP协议无线传输至PC端进行深度预处理。在情感分析阶段，ACPA-ResNet通过自动提取和学习脑电信号的时频域特征，实现情感状态的精准分类。该网络在残差网络基础上引入注意力机制，自适应地为各通道分配不同权重，使其能在空间与通道维度聚焦于更具意义的脑电信号，同时规避深度网络架构的梯度消散与爆炸问题。通过对16名受试者的测试，本系统展现出稳定的脑电信号采集与传输性能，所提网络显著提升了情感识别准确率，平均情感分类准确率达95.1%。