解码想象言语：一种严格主题无关的脑电图方法
Decoding Imagined Speech: A Strictly Subject-Independent Approach Using EEG
- Technical University of Denmark（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究在严格受试者无关框架下，比较时域与频域特征对EEG想象言语解码的性能，发现频谱带功率方法显著更优，为跨受试者泛化提供基线。
中文摘要 AI 辅助
从脑电图（EEG）中解码想象言语，作为严重运动障碍个体的一种潜在沟通途径，已受到越来越多的关注，然而所报告的性能往往依赖于不能清晰反映跨受试者泛化能力的评估协议。本研究在严格的受试者无关评估框架下，对多类想象言语EEG数据集进行了透明的基线调查。比较了两种预处理和特征提取流程：时域统计特征方法和频域频谱带功率方法，使用受试者级交叉验证和随机森林分类器的试验级多数投票进行评估。在粗粒度跨受试者分类中，频谱流程的平均试验级准确率显著高于统计流程（49.03 ± 4.18% 对比 37.97 ± 3.79%）。前向特征选择进一步表明，有限的频带子集捕获了大部分判别信息。总体而言，这项工作为未来针对提高基于EEG的想象言语解码中跨受试者泛化能力的脑机接口研究提供了坚实基础。
英文摘要
Imagined speech decoding from electroencephalography (EEG) has gained increasing attention as a potential communication pathway for individuals with severe motor impairments, yet reported performance often relies on evaluation protocols that do not clearly reflect cross-subject generalization. This study presents a transparent baseline investigation of a multi-class imagined speech EEG dataset under a strictly subject-independent evaluation framework. Two preprocessing and feature extraction pipelines were compared: a time-domain statistical feature approach and a frequency-domain spectral bandpower approach, evaluated using subject-wise cross-validation and trial-level majority voting with a random forest classifier. The spectral pipeline achieved a significantly higher mean trial-wise accuracy than the statistical pipeline (49.03 $\pm$ 4.18% vs. 37.97 $\pm$ 3.79%) for coarse-level classification across subjects. Forward feature selection further indicated that a limited subset of frequency bands captured most of the discriminative information. Overall, this work provides a strong basis for future brain-computer interface studies targeting improved cross-subject generalization in EEG-based imagined speech decoding.
