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The balance perturbation-evoked N1 potential is a reliable cortical response during reactive balance control that is correlated to a variety of cognitive and motor functions. Although the supplementary motor area (SMA) has been identified as the primary source of the N1, it is less understood whether other brain regions contribute to N1 recorded at the scalp. We used source localization on electroencephalography (EEG) data from 25 younger adults recorded during backward whole-body perturbations during stance. We identified the sources that contribute to channel-based N1 recordings and quantified their impact on N1 amplitude and latency. In younger adults, N1 amplitudes can be explained by one single source in a central midline cortical region covering the SMA. When reconstructing N1 signals using backprojections with one versus all independent components (IC) identified as brain sources there was no difference in peak amplitudes and a small but significant difference in N1 peak latencies. Parallel brain sources thus deflect the time course of the N1, but not its magnitude. Brain areas associated with IC’s contributing to the shift in N1 latency varied between participants. Our results emphasize the dominant influence of central cortical areas on the N1 response, informing hypothesizes regarding the nature of the signal and its functional role. Importantly, the extent and location of other cortical structures that influence N1 timing, such as parietal cortex areas and the anterior cingulate cortex, may further elucidate cortical contributions to balance. These markers could be crucial for the early detection of balance problems in clinical populations.

Background Exoskeletons have the potential to augment balance and decrease fall risk. However, existing balance-augmenting wearable robotic controllers have only been tested in single planes of motion during either standing or walking. Thus, it is unclear whether a single control scheme can generalize across perturbations with varying spatial properties or from standing to walking. Inspired by the nervous system’s generalizable balance control strategy across perturbation types and conditions, we propose a novel torque control framework that modulates multi-joint reactive torques based on center of mass (CoM) deviation. We evaluated the generalizability of our delayed CoM feedback controller to predict multi-joint torque responses to perturbations of varying magnitudes, directions, and across movement contexts. Methods In nine healthy young adults, we tested the ability of a delayed CoM feedback scheme to predict multi-joint torque responses to (1) ramp-and-hold support surface perturbations at three magnitudes in 8 directions, (2) a continuous sinusoidal movement, resulting in a cyclical movement of the CoM with similar periodic features as walking, and (3) a sinusoidal motion with random perturbations superimposed to mimic perturbations during cyclic tasks. We trained the model on single ramp-and-hold conditions and evaluated its ability to generalize across directions, magnitudes, movement contexts, and subjects. Results The delayed CoM feedback controller trained on a single ramp-and-hold condition generalized to all ramp-and-hold perturbations for all joints, predicting the joint torques for perturbations of varying directions and magnitudes with high fidelity (average R2 > 0.84 and RMSE < 0.08 Nm/kg). However, generalization from standing to cyclic movement only occurred for hip and knee flexion. The CoM feedback parameters from ramp-and-hold perturbations generalized to the continuous sinusoidal movement (cyclic movement) and the sinusoidal movement with superimposed perturbations (unexpected perturbations) for hip flexion and knee flexion (average R²>0.70 and RMSE < 0.13 Nm/kg), but not for ankle plantarflexion and hip adduction (R²>0.20 and RMSE < 0.22 Nm/kg). Conclusion Our findings show that a physiologically-inspired CoM feedback controller can robustly predict balance-correcting torques appropriate for driving a hip or knee wearable robotic device during standing and movement, and an ankle device during standing only. The goodness-of-fit of joint torque is comparable to top machine learning algorithms, yet requires orders of magnitude less training data, enabling rapid implementation to reduce fall risk.