Abstract
Objective. Due to its high temporal resolution, electroencephalography (EEG) has become a promising tool for quantifying cortical dynamics and effective connectivity in a mobile setting. While many connectivity estimators are available, the efficacy of these measures has not been rigorously validated in real-world scenarios. The goal of this study was to quantify the accuracy of independent component analysis and multiple connectivity measures on ground-truth connections while exposed real-world volume conduction and head motion. Approach. We collected high-density EEG from a phantom head with embedded antennae, using neural mass models to generate transiently interconnected signals. The head was mounted upon a motion platform that mimicked recorded human head motion at various walking speeds. We used cross-correlation and signal to noise ratio to determine how well independent component analysis recovered the original antenna signals. For connectivity measures, we computed the average and standard deviation across frequency of each estimated connectivity peak. Main results. Independent component analysis recovered most antenna signals, as evidenced by cross-correlations primarily above 0.8, and maintained consistent signal to noise ratio values near 10 dB across walking speeds compared to scalp channel data, which had decreased signal to noise ratios of ~2 dB at fast walking speeds. The connectivity measures used were generally able to identify the true interconnections, but some measures were susceptible to spurious high-frequency connections inducing large standard deviations of ~10 Hz. Significance. Our results indicate that independent component analysis and some connectivity measures can be effective at recovering underlying connections among brain areas. These results highlight the utility of validating EEG processing techniques with a combination of complex signals, phantom head use, and realistic head motion.
Highlights
A recent thrust of neuroimaging research has been to measure brain activity during mobile real-world scenarios [1]
Visual inspection of the independent component power and original antenna signal power spectra indicated that volume conduction, head motion, and pink noise mostly added power to the delta (1–4 Hz) and gamma (>30 Hz) power bands
We found that independent component analysis primarily recovered the original signals of interest and separated out motion artifact
Summary
A recent thrust of neuroimaging research has been to measure brain activity during mobile real-world scenarios [1]. Blind source separation using independent component analysis can separate out cortical and artefactual sources, reducing the impact of artifact contamination and improving spatial resolution [5,6]. Such high-density, source-localized experiments have been performed during mobile tasks such as treadmill walking, stair stepping, and balance-beam walking [7,8,9]
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