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Computational Intelligence and Neuroscience
Volume 2017, Article ID 5151895, 16 pages
Research Article

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

1Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
2AIMS Lab, Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
3Nanjing University of Aeronautics and Astronautics, Nanjing, China

Correspondence should be addressed to Khondaker A. Mamun; ca.uiu.esc@numam

Received 19 January 2017; Revised 12 June 2017; Accepted 11 July 2017; Published 19 October 2017

Academic Editor: George A. Papakostas

Copyright © 2017 Mohammad S. Islam et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 for decoding movement from the resting state and about for decoding left and right visually cued movements.