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Recorded experimental data (A) Experimental setup: human participant face-to-face with Pepper humanoid. Robot performs preprogrammed motor sequences. Human mimics robot motion, mirroring the robot’s movements. Both have reflective markers for motion tracking. (B) Motion tracking data: position in 1 and 2D, and velocity. Human in green, robot in black. Light-to-dark follows beginning to end in an example sequence of movements between the four spatial targets. Note Lag as the time offset from robot to human movement onset. used as a performance measure. (C) ERSP grand average illustrating task-related desynchronization between task epochs of rest, fixation and movement for Theta, mu and alpha bands. Note strong desynchronization in mu and beta during the 80 element movement sequence. Color bar corresponds to min-max ERSP values in decibels (dB). (D) EEG signals mapped onto 10–20 coordinates for the 9 included electrodes that will be used in the MSLR model.

Journal: iScience

Article Title: Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task

doi: 10.1016/j.isci.2025.112914

Figure Lengend Snippet: Recorded experimental data (A) Experimental setup: human participant face-to-face with Pepper humanoid. Robot performs preprogrammed motor sequences. Human mimics robot motion, mirroring the robot’s movements. Both have reflective markers for motion tracking. (B) Motion tracking data: position in 1 and 2D, and velocity. Human in green, robot in black. Light-to-dark follows beginning to end in an example sequence of movements between the four spatial targets. Note Lag as the time offset from robot to human movement onset. used as a performance measure. (C) ERSP grand average illustrating task-related desynchronization between task epochs of rest, fixation and movement for Theta, mu and alpha bands. Note strong desynchronization in mu and beta during the 80 element movement sequence. Color bar corresponds to min-max ERSP values in decibels (dB). (D) EEG signals mapped onto 10–20 coordinates for the 9 included electrodes that will be used in the MSLR model.

Article Snippet: Markov-Switching Linear Regression (MSLR) models, which we ran using Dynamax , are a powerful tool for modeling time series data that exhibit regime-switching behavior, where the underlying dynamics of the system change over time.

Techniques: Sequencing

Modeling pipeline and example results for time-resolved predictions (A) The time-resolved model (Markov-switching linear regression, MSLR) learns the linear mapping from EEG inputs to movement readouts. However, this linear relationship varies over time, through different hidden states. After training the model, it will output movement and hidden state predictions from novel EEG inputs. (B) The model is able to predict human velocity (HV), robot X (RX), and human X (HX) positions; ground truth traces are shown in gray, model predictions in light blue. (C) Mapping inferred states as color codes onto the predicted movement readouts, over time.

Journal: iScience

Article Title: Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task

doi: 10.1016/j.isci.2025.112914

Figure Lengend Snippet: Modeling pipeline and example results for time-resolved predictions (A) The time-resolved model (Markov-switching linear regression, MSLR) learns the linear mapping from EEG inputs to movement readouts. However, this linear relationship varies over time, through different hidden states. After training the model, it will output movement and hidden state predictions from novel EEG inputs. (B) The model is able to predict human velocity (HV), robot X (RX), and human X (HX) positions; ground truth traces are shown in gray, model predictions in light blue. (C) Mapping inferred states as color codes onto the predicted movement readouts, over time.

Article Snippet: Markov-Switching Linear Regression (MSLR) models, which we ran using Dynamax , are a powerful tool for modeling time series data that exhibit regime-switching behavior, where the underlying dynamics of the system change over time.

Techniques: