Mobile Electroencephalography for Studying Neural Control of Human Locomotion

Abstract

Walking or running in real-world environments requires dynamic multisensory processing within the brain. Studying supraspinal neural pathways during human locomotion provides opportunities to better understand complex neural circuity that may become compromised due to aging, neurological disorder, or disease. Knowledge gained from studies examining human electrical brain dynamics during gait can also lay foundations for developing locomotor neurotechnologies for rehabilitation or human performance. Technical barriers have largely prohibited neuroimaging during gait, but the portability and precise temporal resolution of non-invasive electroencephalography (EEG) have expanded human neuromotor research into increasingly dynamic tasks. In this narrative mini-review, we provide a (1) brief introduction and overview of modern neuroimaging technologies and then identify considerations for (2) mobile EEG hardware, (3) and data processing, (4) including technical challenges and possible solutions. Finally, we summarize (5) knowledge gained from human locomotor control studies that have used mobile EEG, and (6) discuss future directions for real-world neuroimaging research.

Mobile electroencephalography captures differences of walking over even and uneven terrain but not of single and dual-task gait

“…We suppose that greater spatial coverage, as well as digitized electrode locations and subject-specific structural MRI, would improve artifact attenuation further. To understand neural correlates of gait further, studies including these additional measures will be of great significance, however, in line with previous studies ( 6 , 7 , 92 ), our approach demonstrates that gait-related neural activity can be investigated with small setups [for system recommendation see e.g., ( 93 )] with measurements from few additional sensors. These studies using less equipment are needed to investigate possible translations into clinical practice, where less-costly and time-consuming setups are of relevance.…”

Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration

“…One can include common preprocessing choices in P3 analysis which can be identified with the shiny app. Moreover, recommendations can help identify suitable and preprocessing approaches, as well as set-up procedures and EEG systems (Gorjan et al, 2022; Gramann, 2024; Reis et al, 2014; Song & Nordin, 2021; Wascher et al, 2021). If a scaffolding of steps exists, employing a multiverse analysis using data that will not undergo statistical evaluation can determine appropriate options for specific steps.…”

Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

“…However, the recent findings in Noor et al [40] showed that the artifacts rejection by the automatic continuous rejection and experts confirmation provided promising results in predicting TBI outcomes of specific frequency bands. Although an automated artifacts rejection can help isolate between the neural or non-neural source components from ICA decomposition, subjective method (i.e., visual classification by experts) is still typically advisable [33,70].…”