To address the challenges of physiological signal analysis, we propose a wavelet-based approach and, for the first time, introduce a large-scale pre-trained model specialized for EMG and ECG, achieving performance that surpasses existing methods. Furthermore, by integrating EEG models, we build a multimodal framework where each modality is guided through dedicated branches and fused through learnable weighted fusion. This demonstrates potential for contributions to a wide range of biomedical fields, including wearable health monitoring and clinical diagnostics.