The Removal of EOG Artifacts from EEG Signals using Multivariate Empirical Mode Decomposition
نویسنده
چکیده
The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this project, the multivariate empirical mode decomposition (MEMD)method will be proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. Firstly, the EEG signals will be decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components will then be extracted by reconstructing the MIMFs corresponding to EOAs. This method is used to eliminate EOG signals from the contaminated EEG signals. This method will be simulated using MATLAB. The improvement of this method will be based on two parameters, signal-to-noise ratio (SNR) and mean square error (MSE) after removing ocular artifacts. The results will be compared with any other existing techniques like empirical mode decomposition (EMD).
منابع مشابه
A Time-Frequency approach for EEG signal segmentation
The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...
متن کاملEEG Artifact Removal System for Depression Using a Hybrid Denoising Approach
Introduction: Clinicians use several computer-aided diagnostic systems for depression to authorize their diagnosis. An electroencephalogram (EEG) may be used as an objective tool for early diagnosis of depression and controlling it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a no...
متن کاملCommon Methodology for Cardiac and Ocular Artifact Suppression from EEG Recordings by Combining Ensemble Empirical Mode Decomposition with Regression Approach
Electroencephalography (EEG) is a non-invasive way of recording brain activities, making it useful for diagnosing various neurological disorders. However, artifact signals associated with eye blinks or the heart spread across the scalp, contaminating EEG recordings and making EEG data analysis difficult. To solve this problem, we implement a common methodology to suppress both cardiac and ocula...
متن کاملRemoval of Ocular Artifacts in the EEG through Wavelet Transform without using an EOG Reference Channel
This paper presents a statistical method for removing ocular artifacts in the electroencephalogram (EEG) records. Artifacts in EEG signals are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). The removal of ocular artifact from scalp EEGs is of considerable importance for both the automated and visual analysis of underlying brainwave activi...
متن کاملA review on EEG based brain computer interface systems feature extraction methods
The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017