Feasibility and Accuracy of a Dry and Wireless EEG Helmet for Upper Limb Motor Imagery-Based Brain-Computer Interfaces

EEG-based brain-computer interfaces (BCIs) are a common tool in neurorehabilitation. However, the benefits of EEG-based BCIs can be limited by the use of wet electrodes, which are often uncomfortable and time-consuming to arrange. Here, we investigated the efficacy of an 8-channel wireless and dry EEG system (Helmate, ab medica s.p.a.) in the context of motor imagery-based BCIs, depending on the positioning of the channels. We collected EEG signals from ten healthy subjects and identified the best electrode configuration to discriminate imagined left and right arm movements based on characteristic event-related desynchronization patterns. This study remarks on and refines the use of the Helmate as a tool for motor imagery-based BCI applications.