Convolutional Neural Network - Wavelet Transform Fusion for EEG Signal Processing: Explorations and Perspectives

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This paper systematically explores the research progress and application potential of the integration of Convolutional Neural Networks (CNNs) and Wavelet Transform (WT) techniques in electroencephalogram (EEG) signal processing. By combining the time-frequency localization analysis of wavelet transforms with the deep feature learning capabilities of CNNs, this approach effectively addresses the traditional challenges of EEG signal processing, such as non-stationarity and subtle signal characteristics. The study identifies core challenges, including insufficient real-time performance due to model complexity, variability in cross-subject generalization, and subjective biases in annotated data. Future directions focus on lightweight dynamic fusion architectures, multimodal data collaborative learning, and enhanced clinical interpretability techniques. Through algorithmic innovations and interdisciplinary collaboration between engineering and medicine, breakthroughs in fields such as brain-machine interfaces and precise diagnosis of neurological diseases are expected.