发表机构
Faculty of Information Technology and Communication Sciences, Tampere University（坦佩雷大学信息与通信科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用便携式EEG头环采集数据，基于熵特征与随机森林分类器实现心流状态检测，在随机抽样下准确率达97%，留一受试者验证下达75%，证明了跨受试者泛化能力。
AI 中文摘要
心流状态以在挑战性活动中深度投入和沉浸为特征，是一种有价值的精神状态，对学习、表现和康复结果具有重要意义。虽然心流已在行为层面得到广泛研究，但适用于现实世界部署的客观神经生理检测方法仍然有限。脑电图（EEG）因其可及性、便携性和优越的时间分辨率，为心流检测提供了一条有前景的途径；然而，消费级EEG设备在稳健且受试者独立的心流分类中的效用尚未得到充分探索。本研究使用两款可穿戴EEG头环Muse-S和Emotiv Insight，在45名参与者进行自适应俄罗斯方块游戏过程中，验证了基于熵的生物标志物用于心流状态检测。在去噪后，我们应用离散小波变换（DWT）将信号分解为多个频率子带。从每个子带中提取基于熵的特征，结合通道级度量（斜率熵、分布熵、谱熵）与跨通道描述符（交叉分布熵、交叉谱熵）。这些特征随后用作随机森林分类器（RF）的输入，并通过两种验证方案进行评估：随机抽样（RS）和留一受试者（LOSO）。在随机抽样交叉验证下，随机森林分类器实现了97%的平均准确率；在更严格的留一受试者（LOSO）方案下，平均准确率达到75%，展示了真正的跨受试者泛化能力。全面的多分类器验证（SVM-RBF、GentleBoost、k-NN、拟合判别分析、朴素贝叶斯）证实了熵生物标志物在多种建模框架下具有稳健性。
英文摘要
Flow state, characterized by deep engagement and immersion during challenging activities, represents a valuable mental state with significant implications for learning, performance, and rehabilitation outcomes. While flow has been extensively studied behaviorally, objective neurophysiological detection methods suitable for real-world deployment remain limited. Electroencephalography (EEG) offers a promising avenue for flow detection due to its accessibility, portability, and superior temporal resolution; however, the utility of consumer-grade EEG devices for robust, and subject independent flow classification has been insufficiently explored. This study validates entropy-based biomarkers for flow state detection using two wearable EEG headsets, Muse-S and Emotiv Insight, across 45 participants performing adaptive Tetris gameplay. After denoising, we applied the Discrete Wavelet Transform (DWT) to decompose the signals into multiple frequency sub-bands. From each sub-band, entropy-based features, combining channel-wise measures (Slope Entropy, Distribution Entropy, Spectral Entropy) with cross-channel descriptors (Cross Distribution Entropy, Cross Spectral Entropy) were extracted. These features were then used as input to a Random Forest classifier (RF), evaluated with two validation schemes: Random Sampling (RS) and Leave-One-Subject-Out (LOSO). Under random sampling cross-validation, Random Forest classifiers achieved 97% mean accuracy; under the more rigorous leave-one-subject-out (LOSO) scheme, average accuracy reached 75%, demonstrating genuine cross-subject generalizability. Comprehensive multi-classifier validation (SVM-RBF, GentleBoost, k-NN, Fitted Discriminant, Naive Bayes) confirmed that entropy biomarkers are robust across diverse modeling frameworks.
DOI：10.1016/j.bspc.2026.111399
