Saideh Ferdowsi1, Vahid Abolghasemi2, Saeid Sanei3. 1. School of Electrical Engineering and Robotics, University of Shahrood, Shahrood, Iran. Electronic address: sferdowsi@ieee.org. 2. School of Electrical Engineering and Robotics, University of Shahrood, Shahrood, Iran. 3. Faculty of Engineering and Physical Sciences, University of Surrey, GU2 7XH, UK.
Abstract
BACKGROUND: In this paper exploitation of correlation between post-movement beta rebound in EEG and blood oxygenation level dependent (BOLD) in fMRI is addressed. Brain studies do not reveal any clear relationship between synchronous neuronal activity and BOLD signal. Simultaneous recording of EEG and fMRI provides a great opportunity to recognize different areas of the brain involved in EEG events. NEW METHOD: In order to incorporate information derived from EEG signals into fMRI analysis a specific constraint is introduced in this paper. Here, PARAFAC as a variant of tensor factorization, exploits the data changes in more than two modes in order to reveal the information about the fMRI BOLD and its time course simultaneously. In addition, various constraints can be applied during the alternating process for estimation of its parameters. RESULTS: The achieved results from extensive set of experiments confirm effectiveness of the proposed method to detect the brain regions responsible for beta rebound. Moreover, fMRI-only and EEG-fMRI analysis using PARAFAC2 illustrate correct expected activities in the brain area. COMPARISON WITH EXISTING METHODS: The advantages of the proposed method are revealed when comparing the results with those of obtained using general linear model (GLM) which is a well-known model-based approach. CONCLUSIONS: The proposed method is a semi-blind decomposition technique which employs PARAFAC2 without relying on a predefined time course. The achieved results indicate that this approach can pave the path for multi-task analysis in BCI applications.
BACKGROUND: In this paper exploitation of correlation between post-movement beta rebound in EEG and blood oxygenation level dependent (BOLD) in fMRI is addressed. Brain studies do not reveal any clear relationship between synchronous neuronal activity and BOLD signal. Simultaneous recording of EEG and fMRI provides a great opportunity to recognize different areas of the brain involved in EEG events. NEW METHOD: In order to incorporate information derived from EEG signals into fMRI analysis a specific constraint is introduced in this paper. Here, PARAFAC as a variant of tensor factorization, exploits the data changes in more than two modes in order to reveal the information about the fMRI BOLD and its time course simultaneously. In addition, various constraints can be applied during the alternating process for estimation of its parameters. RESULTS: The achieved results from extensive set of experiments confirm effectiveness of the proposed method to detect the brain regions responsible for beta rebound. Moreover, fMRI-only and EEG-fMRI analysis using PARAFAC2 illustrate correct expected activities in the brain area. COMPARISON WITH EXISTING METHODS: The advantages of the proposed method are revealed when comparing the results with those of obtained using general linear model (GLM) which is a well-known model-based approach. CONCLUSIONS: The proposed method is a semi-blind decomposition technique which employs PARAFAC2 without relying on a predefined time course. The achieved results indicate that this approach can pave the path for multi-task analysis in BCI applications.
Authors: Gopikrishna Deshpande; D Rangaprakash; Luke Oeding; Andrzej Cichocki; Xiaoping P Hu Journal: Front Neurosci Date: 2017-06-07 Impact factor: 4.677