Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

Abstract

Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson's disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about 0.729 ± 0.16 for decoding movement from the resting state and about 0.671 ± 0.14 for decoding left and right visually cued movements.

Machine learning for adaptive deep brain stimulation in Parkinson’s disease: closing the loop

“…Additionally, logistic regression was able to identify periods when patients have rest tremor using LFPs from the subthalamic nucleus [ 52 ]. Two studies with LFPs, using neuronal networks and hidden Markov model classifiers were also successful in decoding movement and laterality (the side in motion), as well as tremor prediction [ 53 , 54 ].…”

Random Forest Algorithm Based on Speech for Early Identification of Parkinson’s Disease

“…The main pathological changes in patients with PD are the death and loss of dopaminergic neurons in the substantia nigra pars compacta which is irreversible. It means that patients with PD will never be cured, but anti-Parkinson's medication or deep brain stimulation surgery can slow down the progression of the disease [ 1 ]. So, early detection, early intervention, and early treatment of PD patients are essential to alleviate their pain and the burden of their families.…”

Toward Electrophysiology-Based Intelligent Adaptive Deep Brain Stimulation for Movement Disorders

“…Ideally, intelligent aDBS could evolve from a computational model based on deep learning with hierarchically organized artificial neural networks that are optimized to predict the need to adapt DBS stimulation parameters in real-time. Practically, electrophysiological time series data from LFP and ECoG electrodes of each channel can be transformed to the timefrequency domain to produce feature matrices with high temporal resolution from relevant frequency bands (theta, alpha, low beta, high beta, low gamma, high gamma) in addition to raw data and full spectrum features such as total power and variance that have previously been used to decode behavior from neural fields [64,[208][209][210][211][212]. Recurrent neural network approaches (e.g., long short-term memory networks; LSTM [213,214]) could be used for training on oscillatory neural time series data [215] to simultaneously conduct hierarchical classifications and predictions that can ultimately guide DBS parameter adaptations.…”