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Movement decoding using neural synchronization and inter-hemispheric connectivity from deep brain local field

K A Mamun1, M Mace, M E Lutman

  • 1Institute of Sound and Vibration Research, University of Southampton, Southampton, UK. Institute of Biomaterials and Biomedical Engineering, University of Toronto and Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada. Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.

Journal of Neural Engineering
|August 26, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to decode movement and laterality from brain activity, achieving high accuracy for movement identification and improving brain-machine interfaces (BMIs). The findings offer a stable, inexpensive control signal for adaptive BMIs without additional surgery.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Decoding brain activity is crucial for treating neurological disorders with interventions like deep brain stimulation (DBS) and for advancing brain-machine interfaces (BMIs).
  • Accurate correlation of brain signals with movement is essential for developing effective neuroprosthetics and therapeutic strategies.

Purpose of the Study:

  • To introduce a novel decoding strategy for capturing movement and its laterality from deep brain local field potentials (LFPs).
  • To develop and validate a method for extracting neural synchronization and inter-hemispheric connectivity features for improved decoding.

Main Methods:

  • Local field potentials (LFPs) were recorded from the subthalamic nucleus or globus pallidus interna in patients with Parkinson's disease or dystonia.
  • Wavelet packet transform (WPT) and Granger causality were used to extract frequency-dependent neural synchronization and connectivity features.
  • A weighted sequential feature selection algorithm identified optimal feature subsets for decoding high-dimensionality data with limited trials.

Main Results:

  • The novel approach achieved 99.8% accuracy in identifying movement and 81.5% accuracy in classifying laterality from LFP activity.
  • Feature analysis revealed stronger contralateral causal driving between basal ganglia hemispheres, significantly improving laterality discrimination.
  • The method effectively decodes movement-related behaviors, providing informative control signals.

Conclusions:

  • Optimally selected neural synchronization and inter-hemispheric connectivity measures offer an effective control signal for adaptive BMIs.
  • This approach provides a stable and computationally inexpensive control signal for DBS patients without requiring additional surgery.
  • The findings extend invasive BMI capabilities by incorporating subcortical information, complementing motor cortex recordings.