标题： 基于真实世界fNIRS的脑机接口：深度学习和经典模型在互动游戏中的基准测试 显示英文标题

标题： Real-World fNIRS-Based Brain-Computer Interfaces: Benchmarking Deep Learning and Classical Models in Interactive Gaming

摘要： 脑-计算机接口使大脑与外部系统的直接通信成为可能，功能性近红外光谱作为一种便携且非侵入性的方法，用于捕捉大脑的血流动力学正在兴起。 本研究通过使用功能性近红外光谱信号和多种机器学习方法，调查了在现实的、互动的网球模拟过程中休息状态和任务状态的分类。 我们基准测试了基于工程特征的传统分类器、在原始时间序列数据上的长短期记忆网络，以及应用于格拉姆角场变换图像的卷积神经网络。 像极端随机树和梯度提升这样的集成模型达到了超过97%的准确率，而基于残差网络的CNN达到了95.0%的准确率，其接近完美的AUC为99.2%，优于LSTM和EfficientNet架构。 采用了一种新的数据增强策略来平衡试验时长，同时保持生理完整性。 特征重要性分析显示，氧合血红蛋白和脱氧血红蛋白信号，特别是斜率和RMS指标，是分类性能的关键贡献者。 这些发现展示了基于功能性近红外光谱的BCI在动态、真实世界环境中的强大潜力，并强调了深度学习模型在解码复杂神经信号方面的优势。 显示英文摘要

摘要： Brain-Computer Interfaces enable direct communication between the brain and external systems, with functional Near-Infrared Spectroscopy emerging as a portable and non-invasive method for capturing cerebral hemodynamics. This study investigates the classification of rest and task states during a realistic, interactive tennis simulation using fNIRS signals and a range of machine learning approaches. We benchmarked traditional classifiers based on engineered features, Long Short-Term Memory networks on raw time-series data, and Convolutional Neural Networks applied to Gramian Angular Field-transformed images. Ensemble models like Extra Trees and Gradient Boosting achieved accuracies above 97 percent, while the ResNet-based CNN reached 95.0 percent accuracy with a near-perfect AUC of 99.2 percent, outperforming both LSTM and EfficientNet architectures. A novel data augmentation strategy was employed to equalize trial durations while preserving physiological integrity. Feature importance analyses revealed that both oxygenated and deoxygenated hemoglobin signals, particularly slope and RMS metrics, were key contributors to classification performance. These findings demonstrate the strong potential of fNIRS-based BCIs for deployment in dynamic, real-world environments and underscore the advantages of deep learning models in decoding complex neural signals.