Measurement and reduction of motion and ballistocardiogram artefacts from simultaneous EEG and fMRI recordings
| Autor: | David F. Abbott, Graeme D. Jackson, Richard A.J. Masterton, Steven W. Fleming |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Adult
Male Computer science Cognitive Neuroscience Action Potentials Hemodynamics Electroencephalography Signal Motion (physics) Ballistocardiography Reference Values Image Processing Computer-Assisted medicine Humans Computer Simulation Computer vision Least-Squares Analysis Cerebral Cortex Recursive least squares filter Epilepsy Fourier Analysis medicine.diagnostic_test business.industry Reproducibility of Results Signal Processing Computer-Assisted Filter (signal processing) Middle Aged Magnetic Resonance Imaging Neurology Female Artificial intelligence Artifacts Functional magnetic resonance imaging business Software |
| Zdroj: | NeuroImage. 37:202-211 |
| ISSN: | 1053-8119 |
| Popis: | Recording the electroencephalogram (EEG) during functional magnetic resonance imaging (fMRI) permits the identification of haemodynamic changes associated with EEG events. However, subject motion within the MR scanner can cause unpredictable and frustrating artefacts on the EEG that may appear focally, bilaterally or unilaterally and can sometimes be confused for epileptiform activity. Motion may arise from a number of sources: small involuntary cardiac-related body movements (ballistocardiogram); acoustic vibrations due to the scanner machinery; and voluntary subject movements. Here we describe a new real-time technique for removing ballistocardiogram (BCG) and movement artefact from EEG recordings in the MR scanner using a novel method for recording subject motion. We record the current induced in a number of wire loops, attached to a cap worn by the subject, due to motion in the static magnetic field of the scanner (Faraday's Law). This is the same process that leads to the motion artefacts on the EEG, and hence these signals are ideally suited to filtering these artefacts from the EEG. Our filter uses a linear adaptive technique based upon the Recursive Least Squares (RLS) algorithm. We demonstrate in both simulations and real EEG recordings from epilepsy patients that our filter significantly reduces the artefact power whilst preserving the underlying EEG signal. |
| Databáze: | OpenAIRE |
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