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Convolutional Neural Networks Based Time-Frequency Image Enhancement For the Analysis of EEG Signals
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Abstract
- Quadratic time-frequency (TF) methods are commonly used for the analysis, modeling, and classification of time-varying non-stationary electroencephalogram (EEG) signals. Commonly employed TF methods suffer from an inherent tradeoff between cross-term suppression and preservation of auto-terms. In this paper, we propose a new convolutional neural network (CNN) based approach to enhancing TF images. The proposed method trains a CNN using the Wigner-Ville distribution as the input image and the ideal time-frequency distribution with the total concentration of signal energy along the IF curves as the output image. The results show significant improvement compared to the other state-of-the-art TF enhancement methods. The codes for reproducing the results can be accessed on the GitHub via https://github.com/nabeelalikhan1/CNN-based-TF-image-enhancement.
Details
- Database :
- OAIster
- Notes :
- 10.1007/s11045-022-00822-2
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1356437880
- Document Type :
- Electronic Resource