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

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).

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.

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.

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 relatively stable and computationally inexpensive control signal. This has the potential to extend invasive BMI, based on recordings within the motor cortex, by providing additional information from subcortical regions.