Machine-learning classification of postural sway in young adults during colored noisy vestibular stimulation

We compared the accuracy with which a machine-learning algorithm could distinguish among center-of-pressure (CoP) trajectories during upright standing when noisy galvanic vestibular stimulation (nGVS) was applied at intensities relative to the perceptual threshold. This report comprises a secondary analysis of data published in Gavriilidou et al. (2025). The k-nearest neighbor (KNN) algorithm was used to classify CoP trajectories recorded while young healthy adults stood on a firm surface with feet together and eyes closed. From 7 variables in the time domain and 84 bandwidths in each axis in the time-frequency domain, the three most important features in the time domain and two in the time-frequency domain were selected by permutation feature importance and correlation-based feature selection techniques, respectively. Models were developed to determine classification accuracy in four conditions derived from combinations of stimulus intensity (% perceptual threshold), type of superimposed noise (Pink or White), and the responsiveness of participants to the perturbation. Classification accuracy was >96% in all four conditions, which indicates that the CoP trajectories were unique at each level within the four conditions. Critically, the machine-learning model was able to discriminate the features extracted from CoP trajectories for participants who either did or did not exhibit a stochastic-resonance effect in response to nGVS. Moreover, SHapley Additive exPlanation analysis found that the contribution of the five extracted features in classifying these two groups of participants was greater during the White-noise condition. These results indicate that nGVS had unique effects on CoP trajectories within each of the four conditions.