tsta

Home / سی و دومین کنفرانس ملی و دهمین کنفرانس بین المللی مهندسی زیست پزشکی ایران

An Automatic Pipeline for Simultaneous EEG-fMRI Artifact-removal (SEFA)

Authors :

Farid Hosseinzadeh¹ Amin Mohammad Mohammadi² Mehrdad Anvarifard³ ُSasan Keshavarz⁴ Elias Ebrahimzadeh⁵ Hamid Soltanian-Zadeh⁶

1- دانشگاه تهران 2- دانشگاه تهران 3- دانشگاه تهران 4- دانشگاه تهران 5- دانشگاه تهران 6- دانشگاه تهران

Keywords :

Simultaneous EEG-fMRI،EEG،preprocessing،artifact removal،automation،pipeline،ٍَُّSEFA

Abstract :

Simultaneous EEG–fMRI provides complementary temporal and spatial information about brain function, but its utility is hindered by severe scanner-induced artifacts such as gradient and ballistocardiographic (BCG) noise. Manual artifact correction is effective but labor-intensive, inconsistent, and difficult to scale. We introduce SEFA, a fully automated two-stage preprocessing pipeline for simultaneous EEG–fMRI that integrates MRI-specific artifact correction (average artifact subtraction, optimal basis set, and PCA/OBS modeling) with state-of-the-art EEG cleaning techniques adapted from a previous popular standard EEG preprocessing pipeline, HAPPE, including automated independent component classification (MARA and ICLabel), bad-channel detection, multitaper regression for line noise, and segment-level quality control. Validation against manually corrected datasets from a reward-based decision-making task demonstrated that SEFA achieves near-perfect equivalence with expert preprocessing. Event-related potentials (ERPs) from both approaches exhibited indistinguishable morphology, latency, and amplitude, with mean channel-wise correlations of r = 0.91 ± 0.14, and 72% of electrodes exceeding r > 0.90. Signal-to-noise ratio (SNR) improved from ~0.8 dB in raw data to 6.7 dB with SEFA, matching manual performance (6.9 dB). Statistical testing confirmed no significant differences in ERP amplitude or latency between automated and manual methods (all p > 0.1). By reducing operator bias and cutting processing time from hours to minutes, SEFA enables reproducible, scalable, and clinically feasible preprocessing of simultaneous EEG–fMRI data.

Papers List

List of archived papers

شناسایی ترس از ضرر در تصمیمات مالی با هوش مصنوعی

سیدسینا مرتضوی

The Impact of an Interactive Rehabilitation Protocol ‎on Reorganization of Brain Networks in Children ‎with Cerebral Palsy: A Pilot Study

Shahed Salehzehi - Mahdi Mollaei - Parisa Hosseini - Ali Koohian Mohammad abadi - Mohammad Ebrahim Hashemi - Hamid Reza Kobravi - Narges Hashemi - Mehran Beiraghi Toosi - Javad Akhondian

Gait Retraining of Musculoskeletal Patients Using Deep Learning Techniques

Kourosh Alimadadi - Masoud Shariat Panahi - Morad Karimpour - Hadi Ghattan Kashani

Finite Element Analysis of Spine ProDisc-L Using Titanium, CFR-PEEK and CoCr Endplates with UHMWPE-GUR1020 75kGy RM Core

Amirali Kashani - Siavash Kazemirad

Engineering Injectable Gelatin-Tyramine/Alginate-Tyramine Hydrogels for Bone Tissue Engineering: A Ratio-Dependent Study of Structure, Mechanics, and Biocompatibility

Melika Mansouri Moghaddam - Soroush Ghofrani Beiragh - Elaheh Jooybar - Rana Imani

تاثیر پیچیدگی وظیفه بر عملکرد حسابرسان با تأکید بر جنبه های فردی و معنوی

حیدر محمدزاده سالطه - هانیه کریم زاده

Fibroglandular Tissue Classification in Breast MRI: A Comparative Study of Automated Decision Strategies

Meysam Khalaj - Arvin Arian - Ala Torabi - Nasrin Ahmadinejad - Masoumeh Gity - Seyedeh Nooshin Miratashi Yazdi - Mohammad Pooya Afshari - Melika Sadeghi Tabrizi - Hamid Soltanian-Zadeh

بررسی تطبیقی چالش های قراردادهای هوشمند مبتنی بر بلاکچین در نظام حقوقی ایران با کشورهای آلمان و آمریکا

رضا بیرانوند - شیما ملامحمدی

Robust Speckle Noise Reduction in IVUS Imaging: Advancing Autoencoders and Non-Local Means with Particle Swarm Optimization

Shirin Ashtari Tondashti - Navid Adib - Mehran Alyali - Mahdis Yaghoubi - Seyed Kamaledin Setarehdan

عصر جدید مدل‌سازی بیومکانیکی با یادگیری ماشین آگاه از فیزیک

علی یعقوبیان - فائزه یعقوبیان