Using Invariant Translation to Denoise Electroencephalogram Signals

Abstract

Problem statement: Because of the distance between the skull and the brain and their different resistivity's, Electroencephalogram (EEG) recordings on a machine is usually mixed with the activities generated within the area called noise. EEG signals have been used to diagnose major brain diseases such as Epilepsy, narcolepsy and dementia. The presence of these noises however can result in misdiagnosis, as such it is necessary to remove them before further analysis and processing can be done. Denoising is often done with Independent Component Analysis algorithms but of late Wavelet Transform has been utilized. Approach: In this study we utilized one of the newer Wavelet Transform methods, Translation-Invariant, to deny EEG signals. Different EEG signals were used to verify the method using the MATLAB software. Results were then compared with those of renowned ICA algorithms Fast ICA and Radical and evaluated using the performance measures Mean Square Error (MSE), Percentage Root Mean Square Difference (PRD) and Signal to Noise Ratio (SNR). Results: Experiments revealed that Translation-Invariant Wavelet Transform had the smallest MSE and PRD while having the largest SNR. Conclusion/Recommendations: This indicated that it performed superior to the ICA algorithms producing cleaner EEG signals which can influence diagnosis as well as clinical studies of the brain.