比较了脑半球分类中机器学习与深度学习的性能，发现两者在不同场景下各有优势。
Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Classical Machine Learning and Deep Learning Optimization Techniques with Neurofeedback
- 用三种神经网络和多个优化器分析脑电频段数据
- 经典方法训练快50-100倍且准确率完美，深度模型实时反馈调节率44.7%
- 适合离线分析用传统方法，实时神经反馈推荐深度模型
本研究探究了在不同脑电频率带中分类器对左右半球状态的预测性能，对比了多种优化器的效果。采用TensorFlow和PyTorch框架实现三种神经网络结构：深度密集网络、浅层三层网络和卷积神经网络（CNN）。Adagrad和RMSprop优化器在各频段表现最优，其中Adagrad在β波段突出，RMSprop在γ波段领先。传统机器学习方法（线性SVM与随机森林）实现了完美分类，且训练速度比深度学习模型快50至100倍。但在具有实时性要求的神经反馈模拟中，深度神经网络表现出更优的反馈信号生成能力，调节率达44.7%，而传统方法为0%。SHAP分析揭示了各脑电频段对模型决策的细微贡献。总体表明，应根据任务需求选择模型：传统方法适用于高效离线分类，深度学习则更适合自适应实时神经反馈应用。
原文摘要 · Abstract (English)
This study investigates the performance of classifiers across EEG frequency bands, evaluating efficient class prediction for the left and right hemispheres using various optimisers. Three neural network architectures a deep dense network, a shallow three-layer network, and a convolutional neural network (CNN) are implemented and compared using the TensorFlow and PyTorch frameworks. Adagrad and RMSprop optimisers consistently outperformed others across frequency bands, with Adagrad excelling in the beta band and RMSprop achieving superior performance in the gamma band. Classical machine learning methods (Linear SVM and Random Forest) achieved perfect classification with 50--100 times faster training times than deep learning models. However, in neurofeedback simulations with real-time performance requirements, the deep neural network demonstrated superior feedback-signal generation (a 44.7% regulation rate versus 0% for classical methods). SHAP analysis reveals the nuanced contributions of EEG frequency bands to model decisions. Overall, the study highlights the importance of selecting a model dependent on the task: classical methods for efficient offline classification and deep learning for adaptive, real-time neurofeedback applications.
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