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Automatic ocular correction in EEG recordings using maximum likelihood estimation

, Article IEEE International Symposium on Signal Processing and Information Technology, IEEE ISSPIT 2013, Athens ; 2013 , Pages 164-169 Karimi, S ; Molaee Ardekani, B ; Shamsollahi, M. B ; Leroy, C ; Derambure, P ; Sharif University of Technology

IEEE Computer Society 2013

Abstract

The electrooculogram (EOG) artifact is one of the main contaminators of electroencephalographic recording (EEG). EOG can make serious problems in results and interpretations of EEG processing. Rejecting contaminated EEG segments result in an unacceptable data loss. Many methods were proposed to correct EOG artifact mainly based on regression and blind source separation (BSS). In this study, we proposed an automatic correction method based on maximum likelihood estimation. The proposed method was applied to our simulated data (real artifact free EEG plus controlled EOG) and results show that this method gives superior performance to Schlögl and SOBI methods

A new framework based on recurrence quantification analysis for epileptic seizure detection

, Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 3 , 2013 , Pages 572-578 ; 21682194 (ISSN) Niknazar, M ; Mousavi, S. R ; Vosoughi Vahdat, B ; Sayyah, M ; Sharif University of Technology

2013

Abstract

This study presents applying recurrence quantification analysis (RQA) on EEG recordings and their subbands: delta, theta, alpha, beta, and gamma for epileptic seizure detection. RQA is adopted since it does not require assumptions about stationarity, length of signal, and noise. The decomposition of the original EEG into its five constituent subbands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure course (Ictal). The proposed algorithm is applied to an epileptic EEG dataset provided...

Extracting single trial visual evoked potentials using iterative generalized eigen value decomposition

, Article Proceedings of the 8th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2008, 16 December 2008 through 19 December 2008, Sarajevo ; 2008 , Pages 233-237 ; 9781424435555 (ISBN) Hajipour, S ; Shamsollahi, M. B ; Mamaghanian, H ; Abootalebi, V ; IEEE Signal Processing Society and IEEE Computer Society ; Sharif University of Technology

2008

Abstract

The activity generated in the brain in response to external stimulations which is named the evoked potential (EP) is typically buried in the background EEG. Because of the low signal to noise ratio ofEPs, it is difficult to record single trial evoked potentials. The traditional technique which is based on ensemble averaging destroys the dynamic information of single trials. In this paper, a new method has been proposed based on generalized eigen value decomposition to extract single trial EPs from single channel EEG recordings. The extraction of the N75-P100-N135 complex in simulated and actual visual evoked potentials is mainly taken under consideration. To illustrate the effectiveness of...