Human electrocortical dynamics while stepping over obstacles

Abstract

To better understand human brain dynamics during visually guided locomotion, we developed a method of removing motion artifacts from mobile electroencephalography (EEG) and studied human subjects walking and running over obstacles on a treadmill. We constructed a novel dual-layer EEG electrode system to isolate electrocortical signals, and then validated the system using an electrical head phantom and robotic motion platform. We collected data from young healthy subjects walking and running on a treadmill while they encountered unexpected obstacles to step over. Supplementary motor area and premotor cortex had spectral power increases within ~200 ms after object appearance in delta, theta, and alpha frequency bands (3–13 Hz). That activity was followed by similar posterior parietal cortex spectral power increase that decreased in lag time with increasing locomotion speed. The sequence of activation suggests that supplementary motor area and premotor cortex interrupted the gait cycle, while posterior parietal cortex tracked obstacle location for planning foot placement nearly two steps ahead of reaching the obstacle. Together, these results highlight advantages of adopting dual-layer mobile EEG, which should greatly facilitate the study of human brain dynamics in physically active real-world settings and tasks.

Multimodal Imaging of Brain Activity to Investigate Walking and Mobility Decline in Older Adults (Mind in Motion Study): Hypothesis, Theory, and Methods

“…The Mind in Motion study will use high-density EEG to quantify electrical brain dynamics during walking. By combining novel dual-electrodes for noise cancellation (Nordin et al, 2018) with Independent Component Analysis and source localization via person-specific inverse electrical head models, it is possible to identify areas of electrical brain spectral power fluctuations with high spatial and temporal precision (Nordin et al, 2019). With this approach, we will determine whether electrical brain activity during actual walking exhibits CRUNCH patterns as participants walk over increasingly uneven terrains.…”

“…Traditionally, motion artifacts have prevented researchers from using EEG to study human brain function during locomotion (Castermans et al, 2014;Kline et al, 2015). We counter this problem using a custom-built dual EEG electrode ( Figure 3) and advanced signal processing, which we have vigorously validated (Oliveira et al, 2016a,b;Oliveira et al, 2017a,b;Nordin et al, 2018Nordin et al, , 2019. After fitting a 128-channel head cap, we will record the location of each electrode relative to bony landmarks with a digitization pen.…”

“…The conductive cap serves as artificial skin so that these inverted electrodes are not completely electrically isolated from each other during data collection. With this approach, each secondary electrode captures electrical noise and movement artifact only, which can then be subtracted from the recordings of the primary electrode to better distinguish physiological components of the EEG signal (Nordin et al, 2018(Nordin et al, , 2019. We will apply an adaptive mixture independent component analysis algorithm (AMICA) that generalizes infomax (Bell and Sejnowski, 1995;Lee et al, 1999) and multiple mixture ICA approaches, to parse EEG signals into spatially static, maximally independent component processes.…”

“…The black signal is the isolated neural signal (red minus blue) after noise correction that is used for analysis. The noise subtraction can either occur in the frequency domain for each pair of dual electrodes, or all the electrode signals can be entered into the independent component analysis to filter out the noise content (Nordin et al, 2018(Nordin et al, , 2019. This figure was created by Dr. Andrew D. Nordin.…”

Activation Complexity: A Cognitive Impairment Tool for Characterizing Neuro-isolation

“…Based on this rudimentary and fundamental point, this relationship between cognitive states and EEG patterns was first documented by Berger 14 , who noted an attenuation in alpha waves (8-12 Hz) when comparing conscious waking states with rest/sleep 15 .These observations became visibly apparent because the EEG alpha frequency is band-limited (8-12 Hz) and its intensity is more dominant during specific conditions (such as sleep). Thus, spectral intensity analysis methods have been the hallmark approaches for EEG analysis 7,12 , and Fourier methods have been the typical method for analyzing the intensity of specific frequency bands (i.e., delta δ, theta θ, alpha α) 16,17 . However, EEG signals are non-stationary 12 , making Fourier approaches problematic since they assume that the signal is infinitely long and stationary 18 .…”