Ahmad Mayeli1,2, Vadim Zotev1, Hazem Refai2, and Jerzy Bodurka1,3
1Laureate Institute for Brain Research, Tulsa, OK, United States, 2Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States, 3College of Engineering, University of Oklahoma, Tulsa, OK, United States
Synopsis
Recording
EEG signals during fMRI acquisition
result EEG signals contamination from imaging and BCG artifacts in addition to other
types of artifacts. In this abstract we introduced a novel method for detecting
and reducing artifacts using second order blind separation in real time. The
algorithm was tested on EEG signals from 12 subjects and it has proven
successful for reducing various types of artifacts. The proposed algorithm can
be applied in real time after the average artifact subtraction of imaging and BCG
artifacts for various applications that require a real-time EEG & fMRI system.Target audience:
Researchers and clinicians interested in employing simultaneous EEG-fMRI
techniques.
Purpose:
Simultaneously EEG and fMRI measurement requires
correction of various artifacts in EEG data. Besides
Ballistocardiogram (BCG) and imaging (MR) artifacts, other types of artifacts,
such as muscle and ocular artifacts can be present in EEG data regardless
whether the EEG is recorded inside or outside the MRI scanner. To date, Average Artifact Subtraction (AAS) [1-2] is
the only real-time method used to partially remove BCG and imaging artifacts; and no
alternative real-time methods for removing other artifacts are available.
However, independent component analysis (ICA) is widely used to suppress
residual BCG and imaging, and other artifacts from EEG data offline. We developed a
novel real-time ICA-based method for automatic detection and removal of various
artifacts from EEG data simultaneously recorded with fMRI. The algorithm was validated in real time EEG & fMRI
experiments and offline with prerecorded EEG data.
Methods:
EEG and fMRI data were acquired with a
32-channel MR-compatible EEG (Brain Products GmbH) and a GE Discovery MR750 3T
MRI scanner. RecView software was used to suppress imaging and BCG artifacts from EEG data in real time, and then to down-sample data
to 250 S/s. To obtain reliable and stable results from ICA
decomposition, data submitted to the algorithm should be at least a multiple k
of n
2 [3] where n is the number of channels. In this study, to increase algorithm speed, 22
channels were selected as opposed to performing ICA on all channels. Given
22-channel data and k=20, the number of data points submitted to ICA must be
more than 9,680, rounded up to 10,000 data points. We used SOBI [4], due to its
faster convergence (compared to other ICA algorithms) for ICA decomposition. The
algorithm was implemented in the following steps [5]: After the first 10,000
data samples are received, the first pass for artifact detection and reduction
is executed on the last 1,000 data points. ICA is applied to the 10,000 data
points each time, and the artifact-free EEG signal is updated every 4 sec. As subsequent 1,000 data samples are received, automated
ICA proceeds and the artifact-free EEG signal is updated. Various features
(i.e., energy, kurtosis, topographical map and power spectral density (PSD)) are
extracted from Independent Components (ICs) for classification as brain activity and artifacts. Fig. 1
shows typical ICs distinguished as different types of artifacts along with
their features. After ICA is performed on 22-channel data, maximum of overall
11 ICs for various types of artifacts are accounted for. Corrected EEG data can
be obtained by removing components related to these artifacts, and then using
inverse ICA to reconstruct the EEG signal.
Results:
The real-time ICA (rtICA) algorithm was applied
on prerecorded EEG data from three healthy female subjects (mean age: 26±6
years) and three male subjects (mean age: 29±9
years) with combat related post-traumatic
stress disorder (PTSD) diagnosis. EEG data were acquired under similar
conditions during fMRI neurofeedback task runs (3640 sec) [6]. Furthermore, the method
was tested on six healthy subjects (mean age: 36±14 years, 3 female) in
real-time experiments. Four resting state runs (8 min
40 sec each) were collected on the subjects. The participants were instructed to
relax and rest with eyes closed for two runs and eyes open and fixed on a fixation
cross for the other two runs. Fig. 2 shows two examples of PSD before
and after applying rtICA artifact correction. After artifact corrections, large
power reductions were observed in EEG Delta and Theta bands. Smaller power
reductions were observed in the Alpha and Beta bands. To quantify BCG artifact changes and correction improvements, we
computed the normalized power spectrum ratio (INPS) [7] as the ratio of the
sums of PSD values for cardiac harmonics before and after the BCG reduction and
the results are shown in Fig. 3. To evaluate algorithm performance for
detecting eye blinks, detection algorithm output was compared with a manual
inspection of EEG data. The average percentage of correctly detected eye blinks
for 12 subjects was found to be 94.87%±5%. Fig. 4 illustrates an example of
RecView-corrected EEG signal (red lines) and further improved EEG data after
removing artifacts detected by our ICA-based automatic method (black lines).
Discussion and Conclusion:
We developed a novel
real-time ICA-based method for suppression EEG artifacts during simultaneous
EEG & fMRI. This method can detect and remove BCG, imaging, muscle, eye blinks and
motion artifacts in EEG data recorded simultaneously with fMRI. The method can
improve real-time estimation of EEG signals and in particular multimodal EEG
and fMRI neurofeedback applications.
Acknowledgements
This
research was supported by W81XWH-12-1-0697 grant from the US Department of
Defense.References
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