Comparative Evaluation of Time–Frequency Transformations and Pretrained CNN Models for EEG-Based Parkinson’s Disease Detection

Background: Parkinson’s disease is a progressive neurodegenerative disorder. Early PD detection plays a key role in effective therapy. Electroencephalography is a neuroimaging technique used to analyze brain abnormalities, such as those seen in patients with PD. However, the complex nature of EEG data requires advanced signal processing and classification methods. Methods: This study systematically evaluates three time-frequency (TF) representation techniques, namely discrete wavelet transform (DWT), continuous wavelet transform (CWT), and synchrosqueezing transform (SST), along with four pretrained convolutional neural network architectures for EEG-based PD detection. The experiments were performed using the San Diego dataset. Image-wise and subject-wise 5-fold cross-validation were employed to assess performance and generalization capability. Results: CWT and SST consistently outperform DWT across all evaluated architectures in image-wise CV evaluation. At the image-wise level, the CWT-EfficientNet-B0 model achieved 97.28% accuracy for HC vs. PD-OFF classification, while SST-EfficientNet-B0 reached 97.26% accuracy for HC vs. PD-ON classification. In subject-wise evaluation, acceptable accuracies of up to 84% were achieved, indicating the ability of the framework in learning PD patterns for unseen subjects. Conclusions: These findings demonstrate that the choice of TF representation has a strong impact on classification performance and that lightweight CNN architectures can achieve high image-wise accuracy with reduced computational cost.