1. A comparison of single-trial EEG classification and EEG-informed fMRI across three MR compatible EEG recording systems
- Author
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Faller, Josef, Hong, Linbi, Cummings, Jennifer, and Sajda, Paul
- Subjects
FOS: Computer and information sciences ,Statistics - Machine Learning ,Quantitative Biology - Neurons and Cognition ,FOS: Biological sciences ,Neurons and Cognition (q-bio.NC) ,Machine Learning (stat.ML) - Abstract
Simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be used to non-invasively measure the spatiotemporal dynamics of the human brain. One challenge is dealing with the artifacts that each modality introduces into the other when the two are recorded concurrently, for example the ballistocardiogram (BCG). We conducted a preliminary comparison of three different MR compatible EEG recording systems and assessed their performance in terms of single-trial classification of the EEG when simultaneously collecting fMRI. We found tradeoffs across all three systems, for example varied ease of setup and improved classification accuracy with reference electrodes (REF) but not for pulse artifact subtraction (PAS) or reference layer adaptive filtering (RLAF)., Comment: 1 Page, IEEE EMBS Conference 2017, Korea
- Published
- 2017
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