A GUI based EEG Signal Denoising using Hilbert Huang Transform

Volume 7 Issue 1 September - November 2016

Research Paper

A GUI based EEG Signal Denoising using Hilbert Huang Transform

N. Padmaja*, M. Bharathi**, E. Sujatha***

* Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College (Autonomous), Tirupati, India.

** Assistant Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College (Autonomous), Tirupati, India.

*** Research Scholar, Department of Electronics and Communication Engineering, JNTUA, Anantapuramu, India.

Padmaja,N., Bharathi, M., and Sujatha, E. (2016). A GUI based EEG Signal Denoising using Hilbert Huang Transform. i-manager's Journal on Electronics Engineering, 7(1), 25-30. https://doi.org/10.26634/jele.7.1.8281

Abstract

The electrical activities of the brain can be recorded using EEG (Electroencephalogram). EEG is often used to diagnose coma, epilepsy, brain death, tumors, stroke, and other brain disorders. In spite of limited spatial resolution, EEG is one of the important and handy tools for research and diagnosis. The EEG records through surface electrodes that are placed onto the scalp of a patient. Unfortunately, EEG data is contaminated by artifacts due to movement of eye related muscles and eyeballs. Thus ocular artifacts make the analysis of neuronal data very complex. EEG signal amplitude is very low in the order of 20-50 μV. These artifacts lead to wrong analysis and interpretation of the disorder. The focus of this work is to develop a novel technique that can detect and remove eye blink artifacts in order to facilitate analysis of EEG recordings using Empirical Mode Decomposition (EMD), and Hilbert Huang Transform (HHT). A GUI (Graphic User Interface) model was designed and developed, so as to enable the users ease in handling data of various patients for medical diagnosis. This project is implemented with the help of GUIDE, a graphical user interface in MatLab. The algorithm developed is applied on the data taken from various patients. The results demonstrate that, HHT is one of the best approaches for analyzing and de-noising EEG Signals.