In EEG-fMRI, EEG electrodes record head motions with a high temporal resolution (EEG-motion-sensor), which can be utilized for retrospective slice-by-slice fMRI motion correction. EEG motion components derived from independent component (IC) analysis were automatically identified by the common features observed in the IC mean power spectral density, spatial projection topographic map, and signal contribution. For real-time application of the EEG-motion-sensor, pre-trained models are desirable for faster classification. We used convolutional neural network to evaluate performance of motion-IC classification model. High speed and classification accuracy were achieved on a large EEG-fMRI dataset, suggesting the possibility of real-time EEG-motion-sensor applications for fMRI.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords