Towards a hybrid EEG-EMG feature for the classification of upper limb movements: comparison of different processing pipelines**Research supported by the Italian Ministry of Health (GR-2018-12365874 and RF-2018-12365210), by Sapienza University of Rome-Progetti di Avvio alla Ricerca, 2020 (AR120172B8B5B405) and Sapienza University of Rome - Progetti di Ateneo 2020 (RM120172B8899B8C).

The functional connectivity between cortex and muscle during motor tasks can change after stroke. Motor rehabilitation aims at restoring it, either by re-establishing “close-to-normal” connectivity or supporting the development of alternative pathways. With the ultimate aim to design a rehabilitative approach based on a combination of Electroencephalographic (EEG) and Electromyographic (EMG) signals, we studied how different processing pipelines affect the extraction of a potential hybrid feature to discriminate movement tasks. Such feature will be employed in a novel hybrid Brain-Computer Interface (BCI) system for motor-rehabilitation. In this setting, the control feature will be derived from a combined EEG and EMG connectivity pattern estimated online during upper limb movement attempts.