ArticleiScience2025
Following the robot's lead: Predicting human and robot movement from EEG in a motor learning HRI task.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.