发表机构
University of Colombo; The University of Queensland（科伦坡大学; 昆士兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对面孔、数学公式两个认知领域，测试15种ML/DL模型的EEG熟悉度预测性能，发现试次独立验证可避免高估泛化能力，确定Gamma、Beta振荡为关键生物标志物，为教育技术建立了相关基准。
AI 中文摘要
学习的客观评估仍是教育领域的核心挑战。脑电信号（EEG）为知识获取的神经关联（包括认知熟悉度）提供了直接、无创的观测窗口。本研究针对两个认知领域（面孔：事实性知识；数学公式：概念性知识），对15种机器学习（ML）和深度学习（DL）模型进行基于EEG的熟悉度预测基准测试。使用23名参与者的连续EEG数据，提取六个频带的频谱特征（功率谱密度）。研究显示，由于相邻时间片段的时间泄露，标准分层交叉验证会产生人为偏高的分类性能（使用卷积神经网络CNN的F1分数最高达0.9853）；而严格的试次独立验证（Group K-Fold）将峰值性能降至0.6038 F1分数（仍使用CNN），且该性能在统计上显著高于25%的随机水平。这凸显了试次独立评估对避免高估模型泛化能力的关键必要性。此外，特征重要性与SHAP分析表明，颞叶和额叶的伽马（Gamma）、贝塔（Beta）振荡是熟悉度最关键的生物标志物。本研究为教育技术中的基于EEG的认知监测建立了现实可行的基准。
英文摘要
Objective assessment of learning remains a fundamental challenge in education. Electroencephalography (EEG) provides a direct, non-invasive window into the neural correlates of knowledge acquisition, including cognitive familiarity. This study benchmarks fifteen machine learning (ML) and deep learning (DL) models for EEG-based familiarity prediction across two cognitive domains: faces (factual knowledge) and mathematical equations (conceptual knowledge). Using continuous EEG data from 23 participants, we extract spectral features (Power Spectral Density) across six frequency bands. We show that while standard stratified cross-validation yields artificially high classification performance (up to 0.9853 F1-score using CNN) due to temporal leakage across neighboring epochs, a rigorous trial-independent validation (Group K-Fold) drops the peak performance to 0.6038 F1-score (using CNN), which is still statistically significant above the 25% chance level. This highlights the critical necessity of trial-independent evaluation to avoid overestimating model generalizability. Furthermore, feature importance and SHAP analysis reveal that temporal and frontal Gamma and Beta oscillations are the most critical biomarkers for familiarity. This work establishes a realistic benchmark for EEG-based cognitive monitoring in educational technologies.
