(no subject)

Neural decoding of treadmill walking from non-invasive, 2 electroencephalographic (EEG) signals

Abstract

Chronic recordings from ensembles of cortical neurons in primary motor and somatosensory areas in rhesus macaques provide accurate information about bipedal locomotion (Fitzsimmons et al. 2009). Here we show that the linear and angular kinematics of the ankle, knee and hip joints during both normal and precision (attentive) human treadmill walking can be inferred from noninvasive scalp electroencephalography (EEG) with decoding accuracies comparable to those from neural decoders based on multiple single-unit activity (SUAs) recorded in nonhuman primates. Six healthy adults were recorded. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs (i.e., precision walking), to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular kinematics of the left and right hip, knee and ankle joints and EEG were recorded, and neural decoders were designed and optimized using cross-validation procedures. Of note, these decoders were also used to accurately infer gait trajectories in a normal walking task that did not require subjects to control and monitor their foot placement. Our results indicate a high involvement of a fronto-posterior cortical network in the control of both precision and normal walking and suggest that EEG signals can be used to study in real-time the cortical dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function.

Key Words: BCI; BMI; EEG; neural decoding; treadmill; walking