Convolutional Neural Networks Based Time-Frequency Image Enhancement For the Analysis of EEG Signals

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

Full text not available from this repository.

Item Type:Article
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
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

Repository Staff Only: item control page