Simultaneous EIT and EEG using frequency division multiplexing
Author(s) -
James Avery,
Tom Dowrick,
Anna Witkowska-Wrobel,
Mayo Faulkner,
Kirill Aristovich,
David Holder
Publication year - 2019
Publication title -
physiological measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.674
H-Index - 101
eISSN - 1361-6579
pISSN - 0967-3334
DOI - 10.1088/1361-6579/ab0bbc
Subject(s) - electroencephalography , computer science , imaging phantom , signal (programming language) , artificial intelligence , medicine , psychology , neuroscience , nuclear medicine , programming language
Methods have previously been reported for simultaneous EIT and EEG recording, but these have relied on post-hoc signal processing to remove switching artefacts from the EEG signal and require dedicated hardware filters and the use of separate EEG and EIT electrodes. This work aims to demonstrate that an uncorrupted EEG signal can be collected simultaneously with EIT data by using frequency division multiplexing (FDM), and to show that the EIT data provides useful information when compared to EEG source localisation.
Approach: A custom FDM EIT current source was created and evaluated in resistor phantom and neonatal head tank experiments, where a static and dynamic perturbation was imaged. EEG and EIT source localisation were compared when an EEG dipole was placed in the tank. EEG and EIT data were collected simultaneously in a human volunteer, using both a standard EEG and a Visual Evoked Potential (VEP) paradigms.
Main Results: Differences in EEG and VEP collected with and without simultaneous EIT stimulation showed no significant differences in amplitude, latency or PSD (p-values \textgreater{} 0.3 in all cases). Compared with EEG source localisation, EIT reconstructions were more accurately able to reconstruct both the centre of mass and volume of a perturbation.
Significance: The reported method is suitable for collecting EIT in a clinical setting, without disrupting the clinical EEG or requiring additional measurement electrodes, which lowers the barrier to entry for data collection. EIT collection can be integrated with existing clinical workflows in EEG/ECoG, with minimal disruption to the patient or clinical team.
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