用多尺度卷积网络提升消费级脑电情绪识别准确率
Consumer-friendly EEG-based Emotion Recognition System: A Multi-scale Convolutional Neural Network Approach
- 设计多尺度卷积结构，融合不同脑区特征提取
- 在多个指标上优于SOTA模型TSception，提升情绪预测精度
- 适合可穿戴设备实时情绪分析，落地应用前景好
脑电图（EEG）是一种非侵入、安全且低风险的脑内电生理信号记录方法。随着干电极技术、消费级EEG设备及机器学习的快速发展，EEG已成为自动情绪识别的重要资源。为实现真实场景下的深度学习情绪识别，本文提出一种新型多尺度卷积神经网络方法。通过引入多种比例系数的特征提取核，以及一种能从大脑四个独立区域学习关键信息的新类型卷积核，该模型在预测效价、唤醒度和支配度评分方面，于多个性能评估指标上持续优于当前最先进的TSception模型。
原文摘要 · Abstract (English)
EEG is a non-invasive, safe, and low-risk method to record electrophysiological signals inside the brain. Especially with recent technology developments like dry electrodes, consumer-grade EEG devices, and rapid advances in machine learning, EEG is commonly used as a resource for automatic emotion recognition. With the aim to develop a deep learning model that can perform EEG-based emotion recognition in a real-life context, we propose a novel approach to utilize multi-scale convolutional neural networks to accomplish such tasks. By implementing feature extraction kernels with many ratio coefficients as well as a new type of kernel that learns key information from four separate areas of the brain, our model consistently outperforms the state-of-the-art TSception model in predicting valence, arousal, and dominance scores across many performance evaluation metrics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。