All EEG data were recorded simultaneously with fMRI recordings in a Siemens 3T Trio MR scanner (Germany). EEG data were acquired using an MR-compatible EEG system (Brain Products, Gilching, Germany). The EEG cap (BrainCap MR, EasyCap GmbH, Breitbrunn, Germany) included 63 scalp electrodes distributed according to the 10–20 system and one additional ECG electrode placed on the participants’ back. EEG signals were acquired relative to an FCz reference, with the ground in correspondence of Iz (10-5 electrode system). The EEG data were sampled at 5000 Hz, with a band-pass filtering of 0.016–250 Hz. The impedance at each electrode was kept lower than 10 kΩ.
Mr compatible eeg system
Manufactured by Brain Products
Sourced in Germany
The MR-compatible EEG system is a product designed for use in magnetic resonance imaging (MRI) environments. It allows for the simultaneous recording of electroencephalography (EEG) data while an individual undergoes MRI scanning. The system is engineered to be compatible with the MRI environment, ensuring safe and reliable data acquisition.
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Lab products found in correlation
2 protocols using mr compatible eeg system
1
Simultaneous EEG-fMRI Acquisition in 3T MRI
2
Multimodal Integration of EEG and fMRI
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Data acquisition and preprocessing. EEG data were recorded using a Brain Products MR-compatible EEG system. Brain Vision Analyzer 2.0 was used to remove MR scanner artefacts in the EEG. fMRI data were collected using a General Electric Signa 3.0T MR scanner using a GE-EPI fMRI sequence. Two resting-state fMRI (rsfMRI) with opened and closed eyes were acquired. Standard preprocessing was performed on MR images using FSL v4.1.8.
Data analyses, i) RS functional Networks (RSN) were identified after applying an ICA approach. Each RSN characteristic time-course for the opened and closed rsfMRI series was analyzed to achieve the correlation degree among RSNs. ii) Three GLM designs per fMRI series were computed using the EEG power time series (one EEG power value per fMRI volume convolved with the canonical HRF) regressors for three bands of interest -alpha, theta and beta bands -[2]
Data analyses, i) RS functional Networks (RSN) were identified after applying an ICA approach. Each RSN characteristic time-course for the opened and closed rsfMRI series was analyzed to achieve the correlation degree among RSNs. ii) Three GLM designs per fMRI series were computed using the EEG power time series (one EEG power value per fMRI volume convolved with the canonical HRF) regressors for three bands of interest -alpha, theta and beta bands -[2]
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