Machines 2022, 10, 603. https://doi.org/10.3390/machines10080603 www.mdpi.com/journal/machines Article EEG-Based Empathic Safe Cobot Alberto Borboni 1, *, Irraivan Elamvazuthi 2 and Nicoletta Cusano 1,3 1 Mechanical and Industrial Engineering Department, University of Brescia, Via Branze 38, 25073 Brescia, Italy; nicoletta.cusano@libero.it 2 Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia; irraivan_elamvazuthi@utp.edu.my 3 Faculty of Political Science and Sociopsychological Dynamics, Università degli Studi Internazionali di Roma, Via Cristoforo Colombo 200, 00147 Rome, Italy * Correspondence: alberto.borboni@unibs.it; Tel.: +39-030-371-5401 Abstract: An empathic collaborative robot (cobot) was realized through the transmission of fear from a human agent to a robot agent. Such empathy was induced through an electroencephalo- graphic (EEG) sensor worn by the human agent, thus realizing an empathic safe brain-computer interface (BCI). The empathic safe cobot reacts to the fear and in turn transmits it to the human agent, forming a social circle of empathy and safety. A first randomized, controlled experiment in- volved two groups of 50 healthy subjects (100 total subjects) to measure the EEG signal in the pres- ence or absence of a frightening event. The second randomized, controlled experiment on two groups of 50 different healthy subjects (100 total subjects) exposed the subjects to comfortable and uncomfortable movements of a collaborative robot (cobot) while the subjects’ EEG signal was ac- quired. The result was that a spike in the subject’s EEG signal was observed in the presence of un- comfortable movement. The questionnaires were distributed to the subjects, and confirmed the re- sults of the EEG signal measurement. In a controlled laboratory setting, all experiments were found to be statistically significant. In the first experiment, the peak EEG signal measured just after the activating event was greater than the resting EEG signal (p < 10 −3 ). In the second experiment, the peak EEG signal measured just after the uncomfortable movement of the cobot was greater than the EEG signal measured under conditions of comfortable movement of the cobot (p < 10 −3 ). In conclu- sion, within the isolated and constrained experimental environment, the results were satisfactory. Keywords: empathy; empathic; cobot; robot; EEG; electroencephalographic; BCI; brain-computer interface; safe; safety 1. Introduction Collaborative robots (cobot) [1] are special robots that can collaborate with humans. They were originally intended for industrial applications [2–9], but they have also found use in biomedical [10–18], domestic [19–22], and military [23–26] fields. Because of the physical proximity of humans and robots, many scientific works in the field of mechatronics [27–29] focus on the relationship’s safety [30] and comfort [31]. Several authors have evaluated the use of natural interface systems, such as vision [32,33] and face recognition and communication [34–37], gestures [38–40], and spoken natural language [41–44], to improve the relationship. This process has led to research into incor- porating the concept of empathy [45] into the human–robot relationship by giving the robot the ability to decode the emotional state of the human subject [46]. The results in the literature are almost entirely based on the analysis of human facial expressions [47–50], sometimes supplemented by gestures and body language [51–53] or by voice modulation [54–57]. Citation: Borboni, A.; Elamvazuthi, I.; Cusano, N. EEG-Based Empathic Safe Cobot. Machines 2022, 10, 603. https://doi.org/10.3390/ma- chines10080603 Academic Editor: Huosheng Hu Received: 10 June 2022 Accepted: 21 July 2022 Published: 24 July 2022 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional claims in published maps and institu- tional affiliations. Copyright: © 2022 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https://cre- ativecommons.org/licenses/by/4.0/).