EEG Signal Enhancement using Wavelet based Soft-thresholding Approach
Dipali Nilesh Dhake, Yogesh Suresh Angal · 2022 3rd International Conference for Emerging Technology (INCET) · 2022
In recent year, Electroencephalogram (EEG) signals has attracted researchers’ attention for various Brain-Computer Interface (BCI) applications. The EEG signals provide information regarding an individual’s emotional, mental, psychological, behavioral, awakeners, alertness, health, and mental activities. However, EEG signals are often tainted by various artifacts generated due to external electromagnetic interference and body movement. It is essential to minimize these noises and artifacts without a loss of actual data to maintain the quality of EEG signals. In this paper, we present Wavelet Packet Decomposition (WPD) to minimize various artifacts in single-channel EEG signals. The WPD helps to minimize or remove the artifacts without a loss of the actual content of the signal. The results of the anticipated technique are estimated based on Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Mean Absolute Error (MAE), and Cross-Correlation (CCR). The simulation outcomes of the anticipated method are compared with the traditional state of arts and it is observed that the anticipated approach gives significant improvement over traditional techniques.