Feature stability and setup minimization for EEG-EMG-enabled monitoring systems

Giulia Cisotto; Martina Capuzzo; Anna Valeria Guglielmi; Andrea Zanella

doi:10.1186/s13634-022-00939-3

Feature stability and setup minimization for EEG-EMG-enabled monitoring systems

EURASIP J Adv Signal Process. 2022;2022(1):103. doi: 10.1186/s13634-022-00939-3. Epub 2022 Oct 27.

Authors

Giulia Cisotto^{1

2

3}, Martina Capuzzo^{1

4}, Anna Valeria Guglielmi¹, Andrea Zanella^{1

2

4}

Affiliations

¹ Department of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy.
² Inter-University Consortium for Telecommunications (CNIT), Padova, Italy.
³ Department of Informatics, Systems and Communications, University of Milano-Bicocca, Viale Sarca, 336, 20126 Milano, Italy.
⁴ Human Inspired Technologies Research Center, University of Padova, Via Luzzatti, 4, 35121 Padova, Italy.

Abstract

Delivering health care 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 movement recognition and monitoring of the associated brain signals. This is one of the contexts where 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 the classification of a specific type of hand movement. 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-aggregation 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 frequency band, in line with previous literature. Thus, this study represents a step forward to the minimization of a reliable EEG-EMG setup to enable gesture recognition.

Keywords: Consensus clustering; Cyber-physical systems; Data fusion; EEG; EMG; Feature selection; Feature stability; Human activity recognition; Magnitude-square coherence; Mobile health; Multi-modal; Usability.