Imperial College London

DrRaviVaidyanathan

Faculty of EngineeringDepartment of Mechanical Engineering

Senior Lecturer in Bio-Mechatronics
 
 
 
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Contact

 

+44 (0)20 7594 7020r.vaidyanathan CV

 
 
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Location

 

717City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Mamun:2015:5/056011,
author = {Mamun, KA and Mace, M and Lutman, ME and Stein, J and Liu, X and Aziz, T and Vaidyanathan, R and Wang, S},
doi = {5/056011},
journal = {Journal of Neural Engineering},
title = {Movement decoding using neural synchronisation and inter-hemispheric connectivity from deep brain local field potentials},
url = {http://dx.doi.org/10.1088/1741-2560/12/5/056011},
volume = {12},
year = {2015}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Objective. Correlating electrical activity within the human brain to movement is essential for developing and refining interventions (e.g. deep brain stimulation (DBS)) to treat central nervous system disorders. It also serves as a basis for next generation brain–machine interfaces (BMIs). This study highlights a new decoding strategy for capturing movement and its corresponding laterality from deep brain local field potentials (LFPs). Approach. LFPs were recorded with surgically implanted electrodes from the subthalamic nucleus or globus pallidus interna in twelve patients with Parkinson's disease or dystonia during a visually cued finger-clicking task. We introduce a method to extract frequency dependent neural synchronization and inter-hemispheric connectivity features based upon wavelet packet transform (WPT) and Granger causality approaches. A novel weighted sequential feature selection algorithm has been developed to select optimal feature subsets through a feature contribution measure. This is particularly useful when faced with limited trials of high dimensionality data as it enables estimation of feature importance during the decoding process. Main results. This novel approach was able to accurately and informatively decode movement related behaviours from the recorded LFP activity. An average accuracy of 99.8% was achieved for movement identification, whilst subsequent laterality classification was 81.5%. Feature contribution analysis highlighted stronger contralateral causal driving between the basal ganglia hemispheres compared to ipsilateral driving, with causality measures considerably improving laterality discrimination. Significance. These findings demonstrate optimally selected neural synchronization alongside causality measures related to inter-hemispheric connectivity can provide an effective control signal for augmenting adaptive BMIs. In the case of DBS patients, acquiring such signals requires no additional surgery whilst providing a rela
AU - Mamun,KA
AU - Mace,M
AU - Lutman,ME
AU - Stein,J
AU - Liu,X
AU - Aziz,T
AU - Vaidyanathan,R
AU - Wang,S
DO - 5/056011
PY - 2015///
SN - 1741-2560
TI - Movement decoding using neural synchronisation and inter-hemispheric connectivity from deep brain local field potentials
T2 - Journal of Neural Engineering
UR - http://dx.doi.org/10.1088/1741-2560/12/5/056011
UR - http://hdl.handle.net/10044/1/24583
VL - 12
ER -