Ricerc@Sapienza

The functional connectivity between cortex and muscle during motor tasks can change after stroke, motor rehabilitation has to restore it, either by re-establishing this connectivity or supporting the development of alternative brain-muscle connectivity. Recently, as an add-on to traditional therapies, motor rehabilitation led by Brain-Computer Interfaces (BCIs) is used to improve functionality in stroke patients.
In this context, we propose cortico-muscular coherence (CMC) between Electroencephalographic (EEG) and Electromyographic (EMG) signals as a potential hybrid feature to discriminate movement tasks. The ultimate goal is to drive the development of a novel, connectivity-based, hybrid BCI system for motor-rehabilitation after stroke.
To achieve this goal, it is necessary first to identify the physiological characteristics of CMC patterns when performing simple upper-limb movements in healthy subjects. Hence, in the present proposal EEG and EMG data will be collected from 20 healthy subjects during the execution of fingers extension and grasping. The recorded data will be analysed first separately and then together, advanced approaches will be used to estimate their coherence. The EEG-EMG connectivity patterns will be studied and compared in different conditions with aim of identifying their distinctive traits. Thanks to this analysis, relevant CMC features will be extracted and used for the classification of upper limb movements.