A classification framework for investigating neural correlates of the limit of stability during weight-shifting in lower limb amputees

Abstract

The coordination of the body with the central nervous system has been studied using various biomechanical, neurophysiological, and neuroimaging studies. Different postural strategies provide evidence of cortical involvement to maintain postural stability, which can be utilised to minimise the risk of falls in the elderly and various pathological individuals. In this paper, we investigated the effect of vibrotactile feedback in Electroencephalography (EEG) based classification of voluntary postural sway during weight-shifting exercises in healthy and transfemoral amputees. The EEG data recorded during forward, backward, right, and left shifting as well as normal standing, with and without vibrotactile feedback, is decomposed using discrete wavelet transform. The energy of the coefficients from levels 4 to 7 forms the feature space to be forwarded to the weighted kNN classifier and ensemble bagged trees. We have achieved significantly higher classification rates across all the conditions for healthy and amputee subjects. Predictor importance from ensemble bagged tree models provides the highest contributions from the low-frequency band of 0–3.9 Hz and channels located over the motor and somatosensory cortex. We have also observed the contributions associated with the spinocerebellum and cerebrocerebellum.