Abstract
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 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.
Highlights
Brain-machine interfaces (BMI) with both neural decoding and stimulation have recently attracted great attention in the deep brain stimulation (DBS) and optogenetics research fields
The recording of subthalamic nucleus (STN) or globus pallidus interna (GPi) local field potentials (LFPs) were made from the electrode leads which were externalised for 4-6 days post-operatively after the patients had been off medication overnight
Deep brain LFPs recorded from twelve subjects were used for testing various decoding strategies
Summary
Brain-machine interfaces (BMI) with both neural decoding and stimulation have recently attracted great attention in the deep brain stimulation (DBS) and optogenetics research fields. Such oscillatory activity shows functional connectivity with disparate cortical oscillations and movement-related desynchronisation and synchronisation in the STN and/or GPi during externally cued or self-paced movements [19,26,29] This suggests that oscillations may be involved in motor preparation and control, and provides a means of communication of neuronal information, for example, through neural binding. Decoding movements from basal ganglia oscillatory neural activities for left and right hands will provide additional information for motor control and bilateral co-ordination [33] Such basal ganglia movement onset information, from the STN or GPi, incorporated with motor cortex information could potentially enhance movement decoding performance towards the development of robust BMI applications for neuroprothetics.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
