Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task
Summary: A large proportion of human behavior is organized in time in the form of sensorimotor sequences. Learning new behavioral sequences recruits cognitive functions with their neural underpinnings. Here, we characterize how neurophysiological activity revealed in the EEG signal can reflect these...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225011757 |
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| author | Tanaya Chatterjee Adrien Guzzo Alejandro Tlaie Ahmad Kaddour Charalambos Papaxanthis Jeremie Gaveau Peter Ford Dominey |
| author_facet | Tanaya Chatterjee Adrien Guzzo Alejandro Tlaie Ahmad Kaddour Charalambos Papaxanthis Jeremie Gaveau Peter Ford Dominey |
| author_sort | Tanaya Chatterjee |
| collection | DOAJ |
| description | Summary: A large proportion of human behavior is organized in time in the form of sensorimotor sequences. Learning new behavioral sequences recruits cognitive functions with their neural underpinnings. Here, we characterize how neurophysiological activity revealed in the EEG signal can reflect these behavioral processes. This was investigated in a face-to-face human-robot interaction, where the robot demonstrated a continuous pointing sequence, which the human mimicked. We observed task-related modulation of the event-related spectral perturbations (ERSP) in distinct ways for rest, fixation, and movement sequences. We also observed modulation of the ERSP by the motor sequence learning. Using a Markov-switching linear regression model, we further demonstrated that the EEG signal could be used to decode the human and robot movements. These results are significant both in the context of neural coding of motor performance and learning, as well as in the context of neural coding of joint action, in the face-to-face human-robot interaction. |
| format | Article |
| id | doaj-art-02e67863a5aa4a9aaa11f22f7544f28b |
| institution | Kabale University |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-02e67863a5aa4a9aaa11f22f7544f28b2025-08-20T03:50:05ZengElsevieriScience2589-00422025-07-0128711291410.1016/j.isci.2025.112914Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI taskTanaya Chatterjee0Adrien Guzzo1Alejandro Tlaie2Ahmad Kaddour3Charalambos Papaxanthis4Jeremie Gaveau5Peter Ford Dominey6Université Bourgogne Europe, INSERM, CAPS UMR 1093, 21000 Dijon, France; GIS STARTER, Dijon, France; Robot Cognition Laboratory, Marey Institute, Dijon, FranceUniversité Bourgogne Europe, INSERM, CAPS UMR 1093, 21000 Dijon, France; GIS STARTER, Dijon, France; Robot Cognition Laboratory, Marey Institute, Dijon, FranceErnst Strüngmann Institute for Neuroscience in Cooperation with the Max Planck Society, Frankfurt am Main 60528, Germany; Laboratory for Clinical Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, SpainUniversité Bourgogne Europe, INSERM, CAPS UMR 1093, 21000 Dijon, France; GIS STARTER, Dijon, France; Robot Cognition Laboratory, Marey Institute, Dijon, FranceGIS STARTER, Dijon, France; Robot Cognition Laboratory, Marey Institute, Dijon, France; Université Bourgogne Europe, CHU Dijon Bourgogne, INSERM, CAPS UMR 1093, 21000 Dijon, FranceUniversité Bourgogne Europe, INSERM, CAPS UMR 1093, 21000 Dijon, France; GIS STARTER, Dijon, France; Robot Cognition Laboratory, Marey Institute, Dijon, FranceUniversité Bourgogne Europe, INSERM, CAPS UMR 1093, 21000 Dijon, France; GIS STARTER, Dijon, France; Robot Cognition Laboratory, Marey Institute, Dijon, France; Corresponding authorSummary: A large proportion of human behavior is organized in time in the form of sensorimotor sequences. Learning new behavioral sequences recruits cognitive functions with their neural underpinnings. Here, we characterize how neurophysiological activity revealed in the EEG signal can reflect these behavioral processes. This was investigated in a face-to-face human-robot interaction, where the robot demonstrated a continuous pointing sequence, which the human mimicked. We observed task-related modulation of the event-related spectral perturbations (ERSP) in distinct ways for rest, fixation, and movement sequences. We also observed modulation of the ERSP by the motor sequence learning. Using a Markov-switching linear regression model, we further demonstrated that the EEG signal could be used to decode the human and robot movements. These results are significant both in the context of neural coding of motor performance and learning, as well as in the context of neural coding of joint action, in the face-to-face human-robot interaction.http://www.sciencedirect.com/science/article/pii/S2589004225011757applied scienceshealth sciencesnatural sciences |
| spellingShingle | Tanaya Chatterjee Adrien Guzzo Alejandro Tlaie Ahmad Kaddour Charalambos Papaxanthis Jeremie Gaveau Peter Ford Dominey Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task iScience applied sciences health sciences natural sciences |
| title | Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task |
| title_full | Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task |
| title_fullStr | Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task |
| title_full_unstemmed | Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task |
| title_short | Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task |
| title_sort | following the robot s lead predicting human and robot movement from eeg in a motor learning hri task |
| topic | applied sciences health sciences natural sciences |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225011757 |
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