用一种新型小波变换提升脑电解码准确率，尤其对困难样本有效。
Investigating the Impact of Rational Dilated Wavelet Transform on Motor Imagery EEG Decoding with Deep Learning Models
- 将有理数离散小波变换作为预处理，嵌入深度学习模型前
- 在三个数据集上显著提升解码精度，最高增益达4.44个百分点
- 特别适合处理非平稳、噪声大的脑电记录，适合临床应用
本研究系统评估了有理数离散小波变换（RDWT）作为预处理步骤对运动想象脑电信号解码的影响。在四个先进深度学习模型（EEGNet、ShallowConvNet、MBEEG_SENet、EEGTCNet）上，基于三个基准数据集（BCI-IV-2a、BCI-IV-2b、HGD）进行有无RDWT的配对比较。结果显示：在BCI-IV-2a上，EEGTCNet平均提升4.44个百分点（kappa+0.059），MBEEG_SENet提升2.23个百分点（+0.030）；在BCI-IV-2b上，各模型均有小幅但一致提升；在HGD上，MBEEG_SENet提升1.65个百分点（+0.022）。个体分析表明，对非平稳记录效果更显著，可抑制局部噪声并增强节律信息。结论：RDWT是低开销、模型感知的预处理方法，能有效提升解码准确率与一致性。
原文摘要 · Abstract (English)
The present study investigates the impact of the Rational Discrete Wavelet Transform (RDWT), used as a plug-in preprocessing step for motor imagery electroencephalographic (EEG) decoding prior to applying deep learning classifiers. A systematic paired evaluation (with/without RDWT) is conducted on four state-of-the-art deep learning architectures: EEGNet, ShallowConvNet, MBEEG\_SENet, and EEGTCNet. This evaluation was carried out across three benchmark datasets: High Gamma, BCI-IV-2a, and BCI-IV-2b. The performance of the RDWT is reported with subject-wise averages using accuracy and Cohen's kappa, complemented by subject-level analyses to identify when RDWT is beneficial. On BCI-IV-2a, RDWT yields clear average gains for EEGTCNet (+4.44 percentage points, pp; kappa +0.059) and MBEEG\_SENet (+2.23 pp; +0.030), with smaller improvements for EEGNet (+2.08 pp; +0.027) and ShallowConvNet (+0.58 pp; +0.008). On BCI-IV-2b, the enhancements observed are modest yet consistent for EEGNet (+0.21 pp; +0.044) and EEGTCNet (+0.28 pp; +0.077). On HGD, average effects are modest to positive, with the most significant gain observed for MBEEG\_SENet (+1.65 pp; +0.022), followed by EEGNet (+0.76 pp; +0.010) and EEGTCNet (+0.54 pp; +0.008). Inspection of the subject material reveals significant enhancements in challenging recordings (e.g., non-stationary sessions), indicating that RDWT can mitigate localized noise and enhance rhythm-specific information. In conclusion, RDWT is shown to be a low-overhead, architecture-aware preprocessing technique that can yield tangible gains in accuracy and agreement for deep model families and challenging subjects.
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