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Rejecting deep brain stimulation artefacts from MEG data using ICA and mutual information
Omid Abbasi1, Jan Hirschmann2, Georg Schmitz3
1Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Germany; Department of Medical Engineering, Ruhr-Universität Bochum, Germany.
Journal of Neuroscience Methods
|April 20, 2016
Summary
This study introduces a new method to remove deep brain stimulation (DBS) artifacts from magnetoencephalography (MEG) recordings. The approach successfully restores brain activity data for improved research into DBS neurophysiology.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Medical Physics
Background:
- Magnetoencephalography (MEG) is crucial for understanding deep brain stimulation (DBS) mechanisms.
- Deep brain stimulation (DBS) artifacts significantly distort MEG data, hindering research.
- Effective artifact rejection is needed to analyze DBS effects on brain activity.
Purpose of the Study:
- To develop and validate an artifact rejection approach for MEG data during DBS.
- To improve the quality of MEG recordings in patients with DBS implants.
- To facilitate the study of neurophysiological mechanisms underlying DBS.
Main Methods:
- Independent Component Analysis (ICA) decomposed MEG data into independent components (ICs).
- Mutual Information (MI) identified artifactual ICs based on the stimulation signal.
- MEG signals were reconstructed using only non-artifactual ICs.
Main Results:
- The proposed method effectively removed most DBS-related artifacts from MEG data.
- Physiological brain activity signals were successfully retrieved in both stimulation conditions.
- The approach enabled the restoration of neural data for analysis.
Conclusions:
- The novel artifact rejection method significantly reduces DBS artifacts in MEG.
- This technique allows for the restoration of physiological data, aiding research.
- The method facilitates studies on DBS's impact on brain activity during tasks and rest.
Keywords:
ArtefactDeep brain stimulationMutual informationNeuromodulationOscillatory activityParkinson’s disease
