Feature Stability and Setup Minimization for EEG-EMG-Enabled Monitoring Systems

Delivering healthcare at home emerged as a key advancement to reduce healthcare costs and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training applications, wearable and portable devices can be employed for movements recognition and monitoring of the associated brain signals. In this context, it is essential to minimize the monitoring setup and the amount of data to collect, process, and share. In this paper, we address this challenge for a monitoring system that includes high-dimensional EEG and EMG data for hand movements classification. We fuse EEG and EMG into the magnitude squared coherence (MSC) signal, from which we extracted features using different algorithms (one from the authors) to solve binary classification problems. Finally, we propose a mapping-and-aggregating strategy to increase the interpretability of the machine learning results. The proposed approach provides very low mis-classification errors ( < 0. 1 ), with very few and stable MSC features ( < 10% of the initial set of available features). Furthermore, we identified a common pattern across algorithms and classification problems, i.e., the activation of the centro-parietal brain areas and arm ’s muscles in 8 ÷ 80 Hz, in line with previous literature. Thus, this study represents a step forward to the minimization of a reliable EEG-EMG setup to enable precise motor training at home.