用脑电图分析大脑连接性，识别学习风格
Beyond Local Power: Functional Connectivity Analysis for Subject-Independent Learning Style Recognition
- 基于脑电相位锁定值分析全脑功能连接
- 视觉-语言维度识别准确率达70.00%
- 发现个体神经模式与群体规律相反的反向现象
识别个体学习风格可提升教学效果。传统问卷结构化，行为追踪需长时间数据积累。为克服时间限制，本文提出一种基于脑电图（EEG）的客观方法，通过相位锁定值（PLV）分析在主动-反思（AR）和言语-视觉（VV）Felder-Silverman维度上的功能连接，对比局部特征。28名参与者在完成瑞文高级渐进矩阵任务时采集脑电信号。支持向量机分类采用留一被试交叉验证（LOSO-CV）及70:30的组内划分。VV维度达到70.00%的被试级准确率，由明显的额-枕极化驱动。而AR维度跨被试泛化能力较差（55.56%），源于重叠的执行网络及“系统性神经反转”现象：稳定的个体连接特征与全局边界呈相反方向，投票差异达20-0。结果表明，固定模板分类器受限于生物多样性，未来需发展自适应特征转换技术以弥合跨被试泛化差距。
原文摘要 · Abstract (English)
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.
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