About

Rodolphe Gentili is an Associate Professor in the Department of Kinesiology at the University of Maryland's School of Public Health. His research focuses on understanding the brain processes underlying human motor behavior through experimental cognitive-motor neuroscience, computational modeling, and robotics-based approaches. He employs techniques such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), electromyography (EMG), and kinematics to examine how the brain adapts during motor learning, performance, and cognitive-motor integration. Dr. Gentili's long-term goals include understanding how the brain integrates physical properties of upper-limb effectors and novel environments with specific cognitive processes like mental imagery and attentional mechanisms during adaptive behavior. His work aims to develop intelligent systems for monitoring and enhancing cognitive-motor functions through human-machine interaction, with applications in brain biomarker monitoring, intervention programs, and human-machine collaborative autonomy for rehabilitation.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Psychology
• Engineering
• Human–computer interaction
• Cognitive psychology
• Physical medicine and rehabilitation
• Algorithm
• Neuroscience
• Simulation
• Theoretical computer science
• Computer vision
• Medicine
• Mathematics

Selected publications

• Performance and mental workload dynamics during in-person and virtually-based remote learning of action sequences

  Figshare · 2026-04-16

  articleOpen accessSenior author

  Despite numerous motor learning studies, none investigated performance and mental workload during in-person and remote practice of sequential tasks. This study examined these dynamics during in-person and virtual-based remote learning of goal-oriented action sequences (i.e. the Tower of Hanoi task). Performance and mental workload were assessed <i>via</i> the Levenshtein distance and NASA Task Load Index, respectively. Besides traditional group-based analyses, cluster-based examinations were conducted to identify individual performance patterns. Results revealed that both in-person and remote learning improved performance, reduced mental workload, and enhanced cognitive-motor efficiency. However, performance improved faster in the remote group, potentially by facilitating executive functioning <i>via</i> reduced anxiety and fine sensorimotor control demands. Slower improvement in the in-person group was likely driven by greater variability in performance, with some individuals unable to achieve optimal performance by late practice. These findings can inform human cognitive-motor behaviour during remote learning with implications for tele-robotics and tele-health. Although remote learning is important for applications such as tele-health and tele-robotics, it is unclear how it differs from in-person learning. This work examines performance and mental workload evolution throughout in-person and remote learning. Both learning modalities attenuated mental workload while performance was enhanced albeit more slowly for in-person learning.

  PublisherDOI

• Performance and mental workload dynamics during in-person and virtually-based remote learning of action sequences

  Figshare · 2026-04-16

  articleOpen accessSenior author

  Despite numerous motor learning studies, none investigated performance and mental workload during in-person and remote practice of sequential tasks. This study examined these dynamics during in-person and virtual-based remote learning of goal-oriented action sequences (i.e. the Tower of Hanoi task). Performance and mental workload were assessed <i>via</i> the Levenshtein distance and NASA Task Load Index, respectively. Besides traditional group-based analyses, cluster-based examinations were conducted to identify individual performance patterns. Results revealed that both in-person and remote learning improved performance, reduced mental workload, and enhanced cognitive-motor efficiency. However, performance improved faster in the remote group, potentially by facilitating executive functioning <i>via</i> reduced anxiety and fine sensorimotor control demands. Slower improvement in the in-person group was likely driven by greater variability in performance, with some individuals unable to achieve optimal performance by late practice. These findings can inform human cognitive-motor behaviour during remote learning with implications for tele-robotics and tele-health. Although remote learning is important for applications such as tele-health and tele-robotics, it is unclear how it differs from in-person learning. This work examines performance and mental workload evolution throughout in-person and remote learning. Both learning modalities attenuated mental workload while performance was enhanced albeit more slowly for in-person learning.

  PublisherDOI

• Performance and mental workload dynamics during in-person and virtually-based remote learning of action sequences

  Ergonomics · 2026-04-16

  articleSenior authorCorresponding

  PRACTITIONER SUMMARY: Although remote learning is important for applications such as tele-health and tele-robotics, it is unclear how it differs from in-person learning. This work examines performance and mental workload evolution throughout in-person and remote learning. Both learning modalities attenuated mental workload while performance was enhanced albeit more slowly for in-person learning.

  PublisherDOI

• Using a self-modulated treadmill as a novel approach to study cognitive-motor and biomechanical outcomes during dual-task walking in individuals with and without lower limb loss

  Experimental Brain Research · 2026-01-21

  articleOpen accessSenior author

  Combined examination of mental workload and biomechanics during dual-task walking in individuals with lower-limb loss is limited to fixed, but not self-modulated walking pace, for which the latter enables dynamic cognitive-motor behavior as typically engaged during community ambulation. By assessing electroencephalography (EEG) (theta, low/high-alpha power) and biomechanics (gait speed, double limb support, stride width), the cerebral cortical activity underlying mental workload and walking mechanics were examined when individuals with and without lower-limb loss executed a cognitive task (assessed via response time and accuracy) under variable demand (seated and walking). Both populations maintained walking mechanics (unchanged gait speed, double limb support, stride width) during dual-task walking across demand and exhibited similarly elevated neurocognitive engagement (e.g., attention, action monitoring) indicated by similar theta power increase and low/high-alpha power decrease when facing greater demand. However, injured individuals exhibited relative performance decrement (degraded response time/accuracy), which suggests attenuated cognitive-motor efficiency relative to uninjured (i.e., similar cortical activity across groups with degraded performance). Moreover, while uninjured individuals during dual-task walking could robustly engage neurocognitive processes to maintain walking mechanics and successfully attend to the concurrent cognitive task, those with lower-limb loss did not exhibit such a robust recruitment (i.e., unchanged frontal/temporal high-alpha power). Such alterations in individuals with lower-limb loss leads to maintenance of walking at the cost of a concurrent task. The present work informs rehabilitation practice and reveals specific cognitive-motor outcomes for individuals with lower-limb loss in an enhanced ecological context.

  PublisherOA PDFDOI

• Revealing the brain signal complexity underlying superior putting performance in expert golfers: A multiscale entropy study with supplemental connectivity analyses

  Biological Psychology · 2025-08-10 · 5 citations

  articleOpen access

  This study aimed to investigate brain signal complexity associated with superior putting performance in expert golfers. Fifty expert golfers (handicap = −2.8 ± 3) each performed 60 putts at a distance of 10 feet. Putting performance was categorized as either a successful or unsuccessful putt (SP vs. UP), based on whether the ball was holed. Electroencephalography (EEG) was recorded during the motor preparatory period (−2 to 0 s) preceding swing onset. Multiscale Entropy (MSE) analysis was employed to quantify EEG signal complexity across six electrode sites: Fz, Cz, Pz, Oz, T3, and T4. Results revealed significantly higher neural complexity for SP compared to UP at Pz (scales 12, 15–17, 19, 21–25) and Oz (scales 20, 22, 25), but significantly lower complexity at T3 (scales 20, 23, and 24). These findings suggest that the involvement of long-timescale integrative processes of visuospatial regions, alongside reduced neural complexity in verbal-analytic regions may characterize optimal putting performance states. Supplemental cortical connectivity analyses further support the MSE findings, demonstrating that superior putting performance was associated with reduced cortical–cortical communication between T3 and midline regions (i.e., Fz, Cz, and Pz). The present findings advance previous EEG research by moving beyond traditional linear analytic methods and align with the psychomotor efficiency hypothesis, which proposes that superior cognitive-motor performance is supported by more refined neural states that enhance task-relevant processing while minimizing interference from task-irrelevant activity. This study suggests that MSE may serve as a valuable neural indicator of the mechanisms underlying optimal cognitive-motor performance in precision sports. • The present study applied a nonlinear method to quantify multiscale entropy associated with superior putting performance in expert golfers. • Superior putting performance was characterized by higher multiscale entropy at the Pz and Oz sites and lower entropy at the T3 site. • Supplemental cortical connectivity analyses revealed reduced T3–Fz/Cz/Pz communication in superior putting performance. • Multiscale entropy analysis suggests an association between optimal putting performance and refined neural states.

  PublisherDOI

• A Study of Brain Dynamics in Simulated Piloting Tracking Tasks

  2025-05-20

  article

  A study of mental workload and the resultant cognitive-motor behavior is essential to understanding the intrinsic limitations of the human information processing system, the results of which have impact on the design of safety-critical systems. While the effects of increased task demand on mental workload and the quality of cognitive-motor performance has been previously investigated, it remains unclear how system controllability (i.e., expected handling qualities) impacts perceptual workload and performance. Furthermore, traditional EEG spectral metrics lack the temporal specificity to capture dynamic workload. Consequently, the purpose of this experiment was to examine objective brain dynamics, task performance, and subjective ratings during piloting tracking tasks of varying complexity while also challenging participants with different expected levels of handling qualities. Our results revealed a trend suggestive of increasing mental workload related to increased task complexity and varying levels of expected handling qualities. To examine dynamic operator workload with increased temporal fidelity, we introduce a time-resolved cross-correlation based approach to assess synchronous dynamics between cortical activity and behavioral performance. The findings herein highlight the practical significance of including analyses of the time domain in workload assessment, in addition to the functional utility of a combination of metrics in the study of the temporally linked cognitive-motor output associated with increased mental workload.

  PublisherDOI

• Assessing the interactions between time series signals using weighted horizontal visibility graphs

  Journal of Physics Complexity · 2025-05-02

  articleOpen accessSenior author

  Abstract The visibility graph algorithm is used to map recorded time series signals to complex networks. Individual timepoints are treated as nodes, and edges are formed by some criterion of visibility between data points. Comparing two visibility graphs can facilitate assessment of the strength of interactions between the associated signals. Two existing methods for this purpose include (1) the average share of overlapping edges (average layer entanglement) between the visibility graphs and (2) the normalized mutual information (MI) between the degree distributions of the two visibility graphs. However, these methods do not always capture the full extent of interactions in some networks. Here we introduce a new approach, the community similarity score, which assesses the similarity between the structure of the communities in the visibility graphs. A community is a subset of the network where nodes are strongly connected to each other, but weakly connected to other communities. The results suggested that the community similarity score generally provided an improvement over normalized MI and average layer entanglement, achieving results that compare well to established time- and frequency-domain methods. When applied to an electroencephalography dataset, the community similarity score produced results consistent with prior literature and was robust to noise. These results suggest that our approach may provide new insights into the dynamics of complex systems and potentially serve as features in machine learning pipelines.

  PublisherDOI

• Brain Biomarkers for Assessing Practice of Robotic Arm Reaching Movements Using a Head-Controlled Interface

  2025-05-12 · 1 citations

  articleSenior author

  Limited efforts have examined the cognitive-motor processes as individuals learn to operate upper-limb assistive devices that improve interactions in their environment (e.g., prostheses, head-controlled devices). Prior work mainly focused on performance without examining cerebral cortical dynamics via brain biomarkers to assess the level of practice during the learning of such assistive devices. Specifically, it was suggested that EEG biomarkers like low- and high-beta spectral power are related to learning as they assess memory formation and attentional mechanisms. Therefore, this work examines how sensorimotor performance, and low-/high-beta spectral power are influenced as individuals without disabilities practice reaching movements with a simulated robotic effector executed via a head-controlled interface. Results revealed faster and straighter reaching movements. Additionally, as individuals progressed from early to late practice, the movement planning stage revealed an elevation of frontal, central and parietal low-beta power and an attenuation of high-beta power in the temporal region. These spectral modulations may reflect an internal model memory encoding process of the novel sensorimotor mapping imposed by this interface throughout practice which ultimately enables improved reaching performance. This work could inform patients' cognitive-motor processes when learning to control assistive systems and provide biomarkers for monitoring practice during rehabilitation.

  PublisherDOI

• An Integrative Assessment of Cognitive-Motor Processes Underlying Mental Workload and Performance Under Varying Levels of Controllability

  Lecture notes in computer science · 2024-01-01

  book-chapter
  PublisherDOI

• Assessment of a Novel Virtual Environment for Examining Cognitive-Motor Processes During Execution of Action Sequences in a Human-Robot Teaming Context

  Lecture notes in computer science · 2024-01-01

  book-chapterSenior author
  PublisherDOI

Frequent coauthors

• Charalambos Papaxanthis

  Inserm

  41 shared

• Sofiane Ouanezar

  Laboratoire Traitement et Communication de l’Information

  40 shared

• James A. Reggia

  38 shared

• Selim Eskiizmirliler

  32 shared

• Hyuk Oh

  University of Maryland, College Park

  31 shared

• Bradley D. Hatfield

  University of Maryland, College Park

  30 shared

• C. Darlot

  Université de Bourgogne

  24 shared

• Garrett E. Katz

  Syracuse University

  22 shared

Labs

• Neuromotor Control and Learning LaboratoryPI