Ali Khan, Nabeel, Mohammadi, Mokhtar, Ghafoor, Mubeen and Tariq, Syed Ali (2022) Convolutional Neural Networks Based Time-Frequency Image Enhancement For the Analysis of EEG Signals. Multidimensional Systems and Signal Processing, 33 . pp. 863-877. ISSN 863–877
Full content URL: https://doi.org/10.1007/s11045-022-00822-2
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Item Type: | Article |
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Item Status: | Live Archive |
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.
Keywords: | Time-frequency analysis, Convolutional neural network, EEG signals, High resolution time-frequency distributions |
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Subjects: | G Mathematical and Computer Sciences > G700 Artificial Intelligence G Mathematical and Computer Sciences > G400 Computer Science G Mathematical and Computer Sciences > G760 Machine Learning G Mathematical and Computer Sciences > G730 Neural Computing G Mathematical and Computer Sciences > G740 Computer Vision |
Divisions: | College of Science > School of Computer Science |
Related URLs: | |
ID Code: | 52277 |
Deposited On: | 20 Dec 2022 14:38 |
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