Local field potential-based brain-machine interface to inhibit epileptic seizures by spinal cord electrical stimulation.

This study proposes a closed-loop brain-machine interface (BMI) based on spinal cord stimulation to inhibit epileptic seizures, applying a semi-supervised machine learning approach that learns from Local Field Potential (LFP) patterns acquired on the pre-ictal (preceding the seizure) condition.LFP epochs from the hippocampus and motor cortex are band-pass filtered from 1 to 13 Hz, to obtain the time-frequency representation using the continuous Wavelet transform, and successively calculate the phase lock values (PLV). As a novelty, the-score-based PLV normalization using both modified-means and Davies-Bouldin's measure for clustering is proposed here.

Spinal Cord Stimulation Modulates Rat Cortico-Basal Ganglia Locomotor Circuit.

Objective: The cortico-basal ganglia circuit is crucial to understanding locomotor behavior and movement disorders. Spinal cord stimulation modulates that circuit, which is a promising approach to restoring motor functions. However, the effects of electrical spinal cord stimulation in the healthy brain motor circuit in pre- and postgait are poorly understood.