Abstract
BackgroundPhase synchrony has extensively been studied for understanding neural coordination in health and disease. There are a few studies concerning the implications in the context of BCIs, but its potential for establishing a communication channel in patients suffering from neuromuscular disorders remains totally unexplored. We investigate, here, this possibility by estimating the time-resolved phase connectivity patterns induced during a motor imagery (MI) task and adopting a supervised learning scheme to recover the subject’s intention from the streaming data.MethodsElectroencephalographic activity from six patients suffering from neuromuscular disease (NMD) and six healthy individuals was recorded during two randomly alternating, externally cued, MI tasks (clenching either left or right fist) and a rest condition. The metric of Phase locking value (PLV) was used to describe the functional coupling between all recording sites. The functional connectivity patterns and the associate network organization was first compared between the two cohorts. Next, working at the level of individual patients, we trained support vector machines (SVMs) to discriminate between “left” and “right” based on different instantiations of connectivity patterns (depending on the encountered brain rhythm and the temporal interval). Finally, we designed and realized a novel brain decoding scheme that could interpret the intention from streaming connectivity patterns, based on an ensemble of SVMs.ResultsThe group-level analysis revealed increased phase synchrony and richer network organization in patients. This trend was also seen in the performance of the employed classifiers. Time-resolved connectivity led to superior performance, with distinct SVMs acting as local experts, specialized in the patterning emerged within specific temporal windows (defined with respect to the external trigger). This empirical finding was further exploited in implementing a decoding scheme that can be activated without the need of the precise timing of a trigger.ConclusionThe increased phase synchrony in NMD patients can turn to a valuable tool for MI decoding. Considering the fast implementation for the PLV pattern computation in multichannel signals, we can envision the development of efficient personalized BCI systems in assistance of these patients.
Highlights
Phase synchrony has extensively been studied for understanding neural coordination in health and disease
In the case of movement imagination, called hereafter motor imagery (MI), brain decoding usually relies on the sensorimotor rhythm (SMR) detected in the EEG signal from the electrodes located over the sensory-motor cortex, the part of the brain that is associated with planning, control and execution of voluntary movements [17]
Group analysis of pairwise couplings The first part of our analysis was devoted to confirming the hypothesis that there were significant differences between neuromuscular disease (NMD) patients and controls regarding the strength of functional couplings
Summary
Phase synchrony has extensively been studied for understanding neural coordination in health and disease. There are a few studies concerning the implications in the context of BCIs, but its potential for establishing a communication channel in patients suffering from neuromuscular disorders remains totally unexplored. We investigate, here, this possibility by estimating the time-resolved phase connectivity patterns induced during a motor imagery (MI) task and adopting a supervised learning scheme to recover the subject’s intention from the streaming data. BCIs create a communication channel between the brain and machines, such as computers, as they “translate” brain signals into machine commands without requiring any muscle or peripheral nerve activity [1, 2]. In the case of movement imagination, called hereafter motor imagery (MI), brain decoding usually relies on the sensorimotor rhythm (SMR) detected in the EEG signal from the electrodes located over the sensory-motor cortex, the part of the brain that is associated with planning, control and execution of voluntary movements [17]
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