超越局部能力：用于被试独立学习风格识别的功能连接分析
Beyond Local Power: Functional Connectivity Analysis for Subject-Independent Learning Style Recognition
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中文总结 AI 辅助
该研究提出基于EEG和PLV连接性的方法，识别被试独立学习风格，在VV维度获70.00%准确率，AR维度仅55.56%，指出需自适应特征变换缩小跨被试泛化差距。
中文摘要 AI 辅助
识别个体学习风格可优化教学效果。传统问卷虽结构化，但行为追踪方法需积累长期交互日志。为克服这些时间限制，本文提出一种客观的脑电图（EEG）方法，评估主动-反思（AR）和言语-视觉（VV）Felder-Silverman维度下的锁相值（PLV）连接性，而非局部特征。研究记录了28名被试在瑞文高级渐进矩阵任务中的EEG信号，采用留一被试交叉验证（LOSO-CV）及70:30的被试内划分进行支持向量机分类。VV维度因独特的额-枕极化，达到70.00%的被试级准确率；而AR维度因执行网络重叠及“系统神经反转”现象（稳定的个体连接特征与全局边界完全相反，投票差距达20-0），跨被试泛化能力较低，仅为55.56%。最终结果表明，僵化的“一刀切”分类器受限于生物多样性，强调未来需自适应特征变换技术以缩小跨被试泛化差距。
英文摘要
Identifying individual learning styles optimizes pedagogical efficacy. While traditional questionnaires are structured, behavioral tracking methods require prolonged interaction log accumulation. To overcome these temporal constraints, this paper proposes an objective Electroencephalography (EEG) approach evaluating Phase Locking Value (PLV) connectivity against localized features across the Active-Reflective (AR) and Verbal-Visual (VV) Felder-Silverman dimensions. EEG signals were recorded from 28 participants during Raven's Advanced Progressive Matrices tasks. Support Vector Machine classification used Leave-One-Subject-Out Cross-Validation (LOSO-CV) alongside a 70:30 intra-subject split. The VV dimension achieved 70.00% subject-level accuracy driven by distinct fronto-occipital polarization. Conversely, the AR dimension yielded lower cross-subject generalizability (55.56%) due to overlapping executive networks and a "Systematic Neural Inversion" phenomenon, where stable individual connectivity signatures operated diametrically opposed to global boundaries (up to 20-0 voting margins). Ultimately, these outcomes demonstrate that rigid "one-size-fits-all" classifiers are bounded by biological diversity, emphasizing the need for future adaptive feature transformation techniques to bridge the cross-subject generalization gap.
发表机构
- Universitas Gadjah Mada（加查马达大学）
机构由 AI 辅助整理，请以论文原文为准。
