Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization
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Pegah Khosropanah
, Abdul Rahman Ramli
Abstract
EEG source localization is determining possible cortical sources of brain activities with scalp EEG. Generally, every step of the data processing sequence affects the accuracy of EEG source localization. In this paper, we introduce a fused multivariate empirical mode decomposing (MEMD) and inverse solution algorithm with an embedded unsupervised eye blink remover in order to localize the epileptogenic zone accurately. For this purpose, 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 accuracy 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.
Author Statement
Research funding: Authors state no funding involved.
Conflict of interest: Authors state no conflict of interest.
Informed consent: Informed consent is not applicable.
Ethical approval: The conducted research is not related to either human or animals use.
References
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©2018 Walter de Gruyter GmbH, Berlin/Boston
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Articles in the same Issue
- Frontmatter
- Research articles
- A new in vitro spine test rig to track multiple vertebral motions under physiological conditions
- In-service characterization of a polymer wick-based quasi-dry electrode for rapid pasteless electroencephalography
- Spike detection using a multiresolution entropy based method
- Obstacles in using a computer screen for steady-state visually evoked potential stimulation
- Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine
- Filtering of ECG signals distorted by magnetic field gradients during MRI using non-linear filters and higher-order statistics
- Failure analysis of eleven Gates Glidden drills that fractured intraorally during post space preparation. A retrieval analysis study
- Assessing multiple muscle activation during squat movements with different loading conditions – an EMG study
- In-vivo monitoring of infection via implantable microsensors: a pilot study
- Analysis of structural brain MRI and multi-parameter classification for Alzheimer’s disease
- False spectra formation in the differential two-channel scheme of the laser Doppler flowmeter
- A priori knowledge integration for the detection of cerebral aneurysm
- Is the location of the signal intensity weighted centroid a reliable measurement of fluid displacement within the disc?
- Image-based 3D surface approximation of the bladder using structure-from-motion for enhanced cystoscopy based on phantom data
- Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization
- Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm
- A hybrid active force control of a lower limb exoskeleton for gait rehabilitation
- Short communication
- Can somatosensory electrical stimulation relieve spasticity in post-stroke patients? A TMS pilot study