Removal of time-varying gradient artifacts from EEG data acquired during continuous fMRI.
OBJECTIVE
Recording low amplitude electroencephalography (EEG) signals in the face of large gradient artifacts generated by changing functional magnetic resonance imaging (fMRI) magnetic fields continues to be a challenge. We present a new method of removing gradient artifacts with time-varying waveforms, and evaluate it in continuous (non-interleaved) simultaneous EEG-fMRI experiments.


METHODS
The current method consists of an analog filter, an EEG-fMRI timing error correction algorithm, and a temporal principal component analysis based gradient noise removal algorithm. We conducted a phantom experiment and a visual oddball experiment to evaluate the method.


RESULTS
The results from the phantom experiment showed that the current method reduced the number of averaged samples required to obtain high correlation between injected and recovered signals, compared to a conventional average waveform subtraction method with adaptive noise cancelling. For the oddball experiment, the results obtained from the two methods were very similar, except that the current method resulted in a higher P300 amplitude when the number of averaged trials was small.


CONCLUSIONS
The current method enabled us to obtain high quality EEGs in continuous simultaneous EEG-fMRI experiments.


CONCLUSIONS
Continuous simultaneous EEG-fMRI acquisition enables efficient use of data acquisition time and better monitoring of rare EEG events.
Department of Diagnostic Radiology, Yale University School of Medicine, P.O. Box 208043, TAC Building MRRC Rm. N128, New Haven, CT 06520-8043, USA. michiro.negishi@yale.edu
Michiro
M
Negishi
Mark
M
Abildgaard
Terry
T
Nixon
Robert Todd
RT
Constable
D000465:Algorithms; D000513:Alpha Rhythm / Q000379:methods*; D016477:Artifacts; D018913:Event-Related Potentials, P300; D006801:Humans; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical; D019047:Phantoms, Imaging