Ahmad Mayeli1, Vadim Zotev2, Hazem Refai3, Jerzy Bodurka4. 1. Laureate Institute for Brain Research, Tulsa, OK, USA; Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, USA. 2. Laureate Institute for Brain Research, Tulsa, OK, USA. 3. Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, USA. 4. Laureate Institute for Brain Research, Tulsa, OK, USA; Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA; Biomedical Engineering Center, University of Oklahoma, Norman, OK, USA. Electronic address: jbodurka@laureateinstitute.org.
Abstract
BACKGROUND: Simultaneous acquisition of EEG and fMRI data results in EEG signal contamination by imaging (MR) and ballistocardiogram (BCG) artifacts. Artifact correction of EEG data for real-time applications, such as neurofeedback studies, is the subject of ongoing research. To date, average artifact subtraction (AAS) is the most widespread real-time method used to partially remove BCG and imaging artifacts without requiring extra hardware equipment; no alternative software-only real time methods for removing EEG artifacts are available. NEW METHODS: We introduce a novel, improved approach for real-time EEG artifact correction during fMRI (rtICA). The rtICA is based on real time independent component analysis (ICA) and it is employed following the AAS method. The rtICA was implemented and validated during EEG and fMRI experiments on healthy subjects. RESULTS: Our results demonstrate that the rtICA employed after the rtAAS can obtain 98.4% success in detection of eye blinks, 4.4 times larger INPS reductions compared to RecView-corrected data, and effectively reduce motion artifacts, as well as imaging and muscle artifacts, in real time on six healthy subjects. COMPARISON WITH EXISTING METHODS: We compared our real-time artifact reduction results with the rtAAS and various offline methods using multiple evaluation metrics, including power analysis. Importantly, the rtICA does not affect brain neuronal signals as reflected in EEG bands of interest, including the alpha band. CONCLUSIONS: A novel real-time ICA method was proposed for improving the EEG quality signal recorded during fMRI acquisition. The results show substantial reduction of different types of artifacts using real-time ICA method.
BACKGROUND: Simultaneous acquisition of EEG and fMRI data results in EEG signal contamination by imaging (MR) and ballistocardiogram (BCG) artifacts. Artifact correction of EEG data for real-time applications, such as neurofeedback studies, is the subject of ongoing research. To date, average artifact subtraction (AAS) is the most widespread real-time method used to partially remove BCG and imaging artifacts without requiring extra hardware equipment; no alternative software-only real time methods for removing EEG artifacts are available. NEW METHODS: We introduce a novel, improved approach for real-time EEG artifact correction during fMRI (rtICA). The rtICA is based on real time independent component analysis (ICA) and it is employed following the AAS method. The rtICA was implemented and validated during EEG and fMRI experiments on healthy subjects. RESULTS: Our results demonstrate that the rtICA employed after the rtAAS can obtain 98.4% success in detection of eye blinks, 4.4 times larger INPS reductions compared to RecView-corrected data, and effectively reduce motion artifacts, as well as imaging and muscle artifacts, in real time on six healthy subjects. COMPARISON WITH EXISTING METHODS: We compared our real-time artifact reduction results with the rtAAS and various offline methods using multiple evaluation metrics, including power analysis. Importantly, the rtICA does not affect brain neuronal signals as reflected in EEG bands of interest, including the alpha band. CONCLUSIONS: A novel real-time ICA method was proposed for improving the EEG quality signal recorded during fMRI acquisition. The results show substantial reduction of different types of artifacts using real-time ICA method.
Authors: Obada Al Zoubi; Ahmad Mayeli; Aki Tsuchiyagaito; Masaya Misaki; Vadim Zotev; Hazem Refai; Martin Paulus; Jerzy Bodurka Journal: Front Hum Neurosci Date: 2019-02-26 Impact factor: 3.169
Authors: Obada Al Zoubi; Chung Ki Wong; Rayus T Kuplicki; Hung-Wen Yeh; Ahmad Mayeli; Hazem Refai; Martin Paulus; Jerzy Bodurka Journal: Front Aging Neurosci Date: 2018-07-02 Impact factor: 5.750
Authors: Ryan Smith; Ahmad Mayeli; Samuel Taylor; Obada Al Zoubi; Jessyca Naegele; Sahib S Khalsa Journal: Biol Psychol Date: 2021-07-24 Impact factor: 3.251