Biomed. Eng.-Biomed. Tech. 2017; aop Pegah Khosropanah*, Abdul Rahman Ramli, Kheng Seang Lim, Mohammad Hamiruce Marhaban and Anvarjon Ahmedov Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization DOI 10.1515/bmt-2017-0011 Received January 19, 2017; accepted June 21, 2017 Abstract: EEG source localization is determining possible cortical sources of brain activities with scalp EEG. Gener- ally, every step of the data processing sequence affects the accuracy of EEG source localization. In this paper, we introduce a fused multivariate empirical mode decom- posing (MEMD) and inverse solution algorithm with an embedded unsupervised eye blink remover in order to localize the epileptogenic zone accurately. For this pur- pose, we constructed realistic forward models using MRI and boundary element method (BEM) for each patient to obtain results that are more realistic. We also developed an unsupervised algorithm utilizing a wavelet method to remove eye blink artifacts. Additionally, we applied MEMD, which is one of the recent and suitable feature extraction methods for non-linear, non-stationary, and multivariate signals such as EEG, to extract the signal of interest. We examined the localization results using the two most reliable linear distributed inverse methods in the literature: weighted minimum norm estimation (wMN) and standardized low resolution tomography (sLORETA). Results affirm the success of the proposed algorithm with the highest agreement compared to MRI reference by a specialist. Fusion of MEMD and sLORETA results in approximately zero localization error in terms of spatial difference with the validated MRI reference. High accu- racy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics. Keywords: BEM; EEG; epilepsy source localization; inverse solution; MEMD; realistic head model. Introduction An epileptic seizure is due to abnormal excessive or syn- chronous neuronal activity in the brain. About 1–2% of the world’s population suffer from this chronic disorder [33]. Surgery may be recommended for about 30 percent of patients with medically intractable focal epilepsy. In this practice, surgeons resect the irritative part of the brain which does not have essential functionality in order to improve the patient’s life quality by omitting seizures. Therefore, determining the source of epilep- tic activities is crucial and can affect the post-surgical results. Unfortunately, epilepsy source localization without invasive EEG (iEEG) is still a very difficult task to perform. Nonetheless, iEEG has its own limitation such as sampling time, field of view etc. Therefore, recent methods are trying to be less invasive to overcome iEEG limitations and increase clinical application of source localization [38]. Rapid development of non-invasive medical imaging systems help researchers to better understand the sources of epilepsy and as a result increase the precision of epi- lepsy surgery and post-surgical results [11]. For instance, combination of EEG and functional magnetic resonance imaging (fMRI) recordings with high temporal and spatial resolution, respectively, allows more precise mapping of blood oxygenation level-dependent (BOLD) signal changes related to particular features of epileptic discharges rec- ognized on EEG. Therefore, obtained data can be used to identify epileptogenic zone for surgical resection [11]. *Corresponding author: Pegah Khosropanah, Department of Computer and Communication Systems Engineering, University Putra Malaysia, 43400 UPM – Serdang, Malaysia, Phone: +(60)182063014, E-mail: khosropanahpegah7@gmail.com Abdul Rahman Ramli: Department of Computer and Communication Systems Engineering, University Putra Malaysia, 43400 UPM – Serdang, Malaysia Kheng Seang Lim: Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia Mohammad Hamiruce Marhaban: Department of Electrical and Electronic Engineering, University Putra Malaysia, 43400 UPM – Serdang, Malaysia Anvarjon Ahmedov: Department of Process and Food Engineering, University Putra Malaysia, 43400 UPM – Serdang, Malaysia