Mobile brain/body imaging utilizes electroencephalography (EEG) to record brain activity during human walking in dynamic environments. Motion artifact from cable sway affects the quality of EEG signals collected from the scalp, masking contributions of synchronously firing neurons. Previous studies have explored cable sway-induced motion artifact, but only during vigorous exercise or controlled sinusoidal motion. Therefore, a need remains to further understand the underlying mechanisms of this artifact, as it may help with developing real-time mitigation methods, reducing reliance on offline signal processing. In this thesis, I aimed to show that controlled cable sway could produce specific motion artifact waveforms in a benchtop setup. I programmed a robotic arm to perform three different types of waveform motions - sinusoidal, square, and sawtooth - in two setups that had different levels of cable support: constrained and unconstrained. I used a novel dual-sided EEG electrode where one side of the electrode interfaces with the scalp to record traditional EEG signals while an outer-facing electrode interfaces with a conductive fabric cap to record isolated motion artifact and noise. Additionally, I placed the electrodes in a 3D-printed holder designed to position them between two layers of conductive fabric and eliminate any electrode movement, which has not been previously constrained. Lastly, I computed correlations between cable sway and bottom electrode EEG, cable sway and top electrode EEG, and top and bottom EEG. Correlations for all variable combinations were low, ranging from -0.122 +/- 0.223 to 0.058 +/- 0.238. Out of six correlation measure comparisons (three across testing setups and three across waveform motions), five did not show significant differences (p-values = 0.391 - 0.958). These results suggest that EEG motion artifact is not the result of just mechanical deformation of the cables but likely requires simultaneous movement of the electrode itself, altering the electrode-conductive surface interface dynamics.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2769 |
| Date | 15 August 2023 |
| Creators | Rojas, David |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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