Background: In individuals with chronic stroke and hemiparesis, noninvasive brain stimulation (NIBS) may be used as an adjunct to therapy for improving motor recovery. Specific states of movement during motor recovery are more responsive to brain stimulation than others, thus a system that could auto-detect movement state would be useful in correctly identifying the most effective stimulation periods. The aim of this study was to compare the performance of different machine learning models in classifying movement periods during EEG recordings of hemiparetic individuals receiving noninvasive brain stimulation.
View Article and Find Full Text PDFParticulate matter suspended in the air that is comprised of microscopic particles with a diameter of 2.5μm or less (PM) is among the most impactful pollutants globally. Extensive evidence shows exposure to ambient PM is associated with a wide range of poor health outcomes.
View Article and Find Full Text PDFRestoring motor function after stroke necessitates involvement of numerous cognitive systems. However, the impact of damage to motor and cognitive network organization on recovery is not well understood. To discover correlates of successful recovery, we explored imaging characteristics in chronic stroke subjects by combining noninvasive brain stimulation and fMRI.
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