Neuromotor Control and Learning Laboratory

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The Neuromotor Control and Learning (NMCL) Laboratory focuses on understanding the brain processes underlying human motor behavior by employing experimental cognitive-motor neuroscience, computational and robotics-based approaches.

 

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In order to better understand human motor control and learning processes, we employ an inclusive strategy involving cognitive-motor neuroscience, biomechanical analyses, and computational neuroscience that incorporates both experimental and modeling approaches. Our experimental work utilizes brain imaging and behavioral techniques to investigate the role of internal representations and cognitive-motor processes during human motor performance and learning. The modeling efforts include neurophysiologically plausible architectures, which mimic brain structures and/or functions, as well as biomechanics of effectors. The models are then tested both in computer simulations and by conducting "robotic experimentation" with actual anthropomorphic robot effectors, to i) validate the behavior of the neurophysiological model in the real world by comparing its performance to the human counterpart and ii) to examine human cognitive-motor processes in a context of human-robot team dynamics.

The experimental and computational approaches are complementary and enable to have an integrative perspective on human adaptive cognitive-motor behavior. The empirical work provides the basis on which to build and refine the computational models. In turn, the computational modeling effort is used as a tool to inform the development of empirical hypotheses as well as public health applications.

Summer 2019:

Publication: A NMCL lab paper was accepted for publication in The Journal of Motor Learning and Development.

Publication: A NMCL lab paper was accepted for publication in Neuroscience.

Publication: A NMCL lab paper was accepted for publication in the Journal of the International Neuropsychological Society.

Publication: A NMCL lab paper was accepted for publication in Neural Networks.

Conference: The NMCL lab presented its research at the North American Society for the Psychology of Sport and Physical Activity's (NASPSPA) Annual Meeting in Baltimore, MD, USA.

 

Spring 2019:

Publication: A NMCL lab paper was accepted for publication in Experimental Brain Research.

 

Winter 2019:

Grant: In partnership with Dr James Reggia (Department of Computer Science, University of Maryland) and Dr Garrett Katz (Electrical Engineering & Computer Science, Syracuse University), the NMCL lab received a new grant from the Office of Naval Research (ONR) entitled "A neurocognitive approach to robotic cause‐effect reasoning during learning" in order to develop a computational architecture for humanoid robots based on human cognitive-motor mechanisms during complex behavior.

 

Fall 2018:

Publication: A NMCL lab paper was accepted for publication in Experimental Brain Research.

New student: William Galway joined the lab as a Master student.

Summer 2018:

Publication: A NMCL lab paper was accepted for publication in Neuroscience.

Conference: The NMCL lab presented its research at the North American Society for the Psychology of Sport and Physical Activity's (NASPSPA) Annual Meeting in Denver, CO, USA.

Spring 2018:

Publication: A NMCL lab paper was accepted for publication in Human Movement Science.

Theresa Hauge successfully defended her Master thesis. Congratulations Theresa! 

Winter 2018:

Publication: A NMCL lab paper was accepted for publication in Biological Psychology.

Fall 2017:

Publication: A NMCL lab paper was accepted for publication in Psychophysiology.

Publication: A NMCL lab paper was accepted for publication in Experimental Brain Research.

Publication: A NMCL lab paper was accepted for publication in International Journal of Psychophysiology.

New student: Christopher Gaskins joined the lab as a PhD student.

Summer 2017:

Conference: The NMCL lab presented its research at the North American Society for the Psychology of Sport and Physical Activity's (NASPSPA) Annual Meeting in San Diego, CA, USA

Spring 2017:

Publication: A NMCL lab paper was accepted for publication in Neuroscience.

Winter 2017:

Publication: A NMCL lab paper was accepted for publication in IEEE Transactions on Cognitive and Developmental Systems.

Fall 2016:

Publication: A NMCL lab paper was accepted for publication in IEEE Transactions on Cognitive and Developmental Systems.

Publication: A NMCL lab paper was accepted for publication in Neural Network.

New student: Theresa Hauge joined the lab as a Master student.

Summer 2016:

Conference: The NMCL lab presented its research at the North American Society for the Psychology of Sport and Physical Activity's (NASPSPA) Annual Meeting in Montreal, Canada.

Publication: A NMCL lab paper was accepted for publication for the 9th Artificial General Intelligence (AGI-16).

Spring 2016:

Publication: A NMCL lab paper was accepted for publication in Bioinspiration & Biomimetics.

Maryland Day 2016: Thanks to Garrett Katz and Isabelle Shuggi for spending the day inspiring future generations of scientists. Thank you for the excellent demonstrations with the Baxter robot and the human-machine interaction simulation. 

Winter 2016:

Grant: In partnership with Walter Reed National Military Medical Center, the NMCL lab received a new grant from the Department of Defense (The Henry M. Jackson Foundation) entitled "Center for Rehabilitation Sciences Research: Biomechanical and cognitive changes of walking in virtual environments" in order to examine the cognitive workload in amputees.

 

Fall 2015:

Publication: A NMCL lab paper was accepted for publication for the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT'15)

Emma Shaw received the first prize for her poster at Bioscience Day 2015. Congratulations Emma! Thanks to Hyuk, Isabelle and Helena for presenting posters too!

Hyuk Oh successfully defended his disseration. Congratulations Hyuk!! Dr. Oh is now a Research Assistant Professor in the Cognitive Motor Neuroscience Laboratory of the Department of Kinesiology, University of Maryland.

New students! Isabelle Shuggi and Emma Shaw joined the lab as PhD students.

Summer 2015:

Conference: The NMCL lab presented its research and organized a parallel session at the Human Computer Interface International (HCII) conference in Los Angeles, California. Dr. Gentili chairs the Operational Neuroscience session of the Augmented Cognition program

Publication: Two NMCL lab papers were accepted for publication in LNCS, Springer 2015.

Spring 2015:

Publication: A NMCL lab paper was accepted for publication in IJSR.

Publication: A NMCL lab paper was accepted for publication in Neuroscience.

Publication: A NMCL lab paper was accepted for publication in IEEE NER 2015.

Conference: The NMCL lab presented its research at the North American Society for the Psychology of Sport and Physical Activity's (NASPSPA) Annual Meeting in Portland, Oregon.

Publication: A NMCL lab paper was accepted for publication in IEEE TePRA.

Winter 2015:

Publication: A NMCL lab paper was accepted for publication in Biological Psychology.

Fall 2014:

Publication: Two NMCL lab papers were accepted for publication for IEEE EMBC.

COGNITIVE-MOTOR CONTROL

Examination of mental workload under various cognitive-motor task demands

This research line examines the dynamics of specific task-relevant processes such as attentional resource allocation and working memory underlying changes of mental workload under various cognitive-motor demands. To this end the cortical dynamics are examined via electroencephalography (EEG) in both the temporal (e.g., event related potentials) and spectral (local activity, functional connectivity) domains along with behavioral performance in individuals who complete various levels of cognitive-motor challenge. This work informs the underlying neural mechanisms of cognitive-motor behavior and has the potential for enhancing assessment and improvement of human performance in real-world situation (e.g., operational environment).

Collaborators
Bradley Hatfield (University of Maryland); Matthew Miller (Auburn University); Hyuk Oh (University of Maryland); United States Naval Academy.

Representative publications
Jaquess KJ, Gentili RJ, Lo LC, Oh H, Zhang J, Rietschel JC, Miller MW, Tan YY, Hatfield BD. (2017). Empirical evidence for the relationship between cognitive workload and attentional reserve. International Journal of Psychophysiology 121:46-55. 

Gentili RJ, Jaquess KJ, Shuggi IM, Shaw EP, Oh H, Lo LC, Tan YY, Domingues CA, Blanco JA, Rietschel JC, Miller MW, Hatfield BD. (2018). Combined assessment of attentional reserve and cognitive-motor effort under various levels of challenge with a dry EEG system. Psychophysiology 55(6):e13059.

Mental workload assessment in individuals with lower limb loss

This project employs a combined examination of the cortical dynamics via EEG and behavior to investigate the changes in mental workload and performance in injured individuals with lower limb loss (e.g., transtibial; transfemoral) who operate prostheses under various cognitive-motor demands during dual-task walking. This work investigates the cognitive-motor mechanisms in clinical population during operation of lower limb prostheses. Implications of this work includes the development of assessment and training approaches for individuals with lower limb loss.

Collaborators
Shuo Chen (University of Maryland); Bradley Hatfield (University of Maryland); Matthew Miller (Auburn University); Walter Reed National Military Medical Center.

Representative publications
Shaw EP, Rietschel JC, Hendershot BD, Pruziner AL, Miller MW, Hatfield BD, Gentili RJ. (2018). Measurement of attentional reserve and mental effort for cognitive workload assessment under various task demands during dual-task walking. Biological Psychology 134:39–51.

Shaw EP, Rietschel JC, Shuggi IM, Xing Y, Hendershot BD, Pruziner AL, Chen S, Miller MW, Hatfield BD, Gentili RJ. (2018). Evaluation of cerebral cortical networking as a measure of cognitive workload during dual-task walking under various levels of challenge. Accepted. 19th NASPSPA Conference, 21-23 June 2018, Denver, CO, USA.

COGNITIVE-MOTOR PRACTICE & LEARNING

Evaluation of concurrent behavioral and cortical dynamics during adaptation to altered environments

This research line investigates the concomitant changes in behavioral performance and cortical dynamics measured via EEG or fNIRS (functional near infrared spectroscopy) to assess the changes in executive function (inhibition, attention) during adaptation in individuals who face altered environment. This work informs the underlying cognitive-motor mechanisms of adaptive human motor control and has potential applications for developing systems to assess and improve performance in healthy as well as clinical populations.

Collaborators
Bradley Hatfield (University of Maryland); Hyuk Oh (University of Maryland); Patricia Shewokis (Drexel University).

Representative publications
Gentili RJ, Patricia A Shewokis, Ayaz H, Contreras-Vidal JL. (2013). Functional near-infrared spectroscopy-based correlates of prefrontal cortical dynamics during a cognitive-motor executive adaptation task. Frontiers in Human Neuroscience, 4(7):277.

Gentili RJ, Bradberry TJ, Oh H, Costanzo ME, Kerick SE, Contreras-Vidal JL, Hatfield BD. (2015). Evolution of cerebral cortico-cortical communication during visuomotor adaptation to a cognitive-motor executive challenge. Biological Psychology, 105:51-65.

Performance and mental workload assessment during the acquisition of a novel skill using human-robot interfaces 

This line of research aims to examine the behavioral performance and EEG cortical dynamics in individuals who have to practice/learn to operate a robotic effector through a human-robot interface. Individuals must perform reaching movements by employing unusual body segments (e.g., head motion) under various cognitive-motor task demands. This work can inform the human cognitive-motor mechanisms when human performs alone or in a context of team dynamics with applications to human-robot interactions as well as provide the basis for future work with clinical populations who operate assistive systems.

Collaborators
Jeffrey Herrmann (University of Maryland); Hyuk Oh (University of Maryland); Patricia Shewokis (Drexel University).

Representative publications
Shuggi IM, Oh H, Shewokis PA, Gentili RJ. (2017). Mental workload and motor performance dynamics during practice of reaching movements under various levels of task difficulty. Neuroscience. 360, 166-179.

Shuggi IM, Shewokis PA, Herrmann JW, Gentili RJ. (2017). Changes in motor performance and mental workload during learning of reaching movements: a team dynamics perspective. Experimental Brain Research. 236(2), 433-451.

Examination of high-level planning during performance and learning of complex action sequences

This research aims to examine the performance and learning of complex action sequences (e.g., task maintenance, Tower of Hanoi) in human and humanoid robots. We deploy a novel computational approach to assess in detail the quality of the sequences generated to perform a complex task with constraints and success criteria ranging from fairly controlled to very flexible. This work can reveal the cognitive-motor mechanisms of human behavior in both healthy and clinical populations and has implication for the assessment of complex performance in humanoid robots as well as human-robot interactions.

Collaborators
Garrett Katz (Syracuse University); James Reggia (University of Maryland).

Representative publications
Katz GE, Huang DW, Hauge TC, Gentili RJ, Reggia JA. (2017). A novel parsimonious cause-effect reasoning algorithm for robot imitation and plan recognition. IEEE Transactions on Cognitive and Developmental Systems, 10(2), 177 - 193.

Hauge TC, Katz G, Huang DW, Davis G, Reggia JA, Gentili RJ. (2017). High-level motor planning assessment during performance of complex actions in humans and humanoid robots: A computational approach. 47th Annual Meeting of the Society for Neuroscience Meeting, November 11-15, Washington DC, DC, USA.

NEURAL MODELS & NEUROROBOTICS

Neural network modeling and neurorobotic control systems

This research line complements the empirical work described above by modeling specific sensori-/cognitive-motor mechanisms to generate predictions that can be tested in humans. This will increase understanding of the relevant neural processes as well as develop neurorobotic control systems for humanoid robots to examine human cognitive-motor mechanisms within the context of team dynamics. The predictions and validations of these neural models are based on simulated behavioral (e.g., kinematics) and/or neurophysiological (e.g., synthetic fMRI) data. 

Collaborators
Garrett Katz (Syracuse University); Hyuk Oh (University of Maryland); James Reggia (University of Maryland).

Representative publications
Gentili RJ, Oh H, Huang D-W, Katz GE, Miller RH, Reggia JA. (2015). A neural architecture for performing actual and mentally simulated movements during self-intended and observed bimanual arm reaching movements. International Journal of Social Robotics, 7(3), 371-392. 

Oh H, Braun AR, Reggia JA, Gentili RJ. (2018). Fronto-parietal mirror neuron system modeling: visuospatial transformations support imitation learning independently of imitator perspective. Human Movement Science. In press.

 

Rodolphe Gentili, Ph. D. (email: rodolphe@umd.edu)
Assistant professor in the Department of Kinesiology

Faculty member of the Program in Neuroscience and Cognitive science

Faculty member of the Maryland Robotics Center

 

Hyuk Oh; Ph.D. Post-doctoral Researcher (email: hyukoh@umd.edu)
Background and research interests: Hyuk was trained in Computer Science and computational neuroscience at the University of Southern California and then at the University of Maryland-College Park. His work focuses on developing neurally inspired network models to inform the cognitive-motor behavior in human as well as on neuroimaging data processing.

William Galway (MA. Student; email: bgalway@terpmail.umd.edu)
Background and research interests: Before joining the lab William headed the Clinical Education Department at Biodex Medical Systems and spent 10 years as a Clinical Athletic Trainer at Hospital for Special Surgery in New York. He has been working for more than thirty years in the sports medicine and motor rehabilitation industry. His current research focuses how human mental workload is affected during motor performance under various levels of task difficulty and performance context.

Christopher Gaskins (Ph.D. Student; email: pgaskin1@terpmail.umd.edu)
Background and research interests: Before joining the lab Christopher received a Bachelor of Science degree in Exercise Science at the University of South Carolina, Columbia, South Carolina  and a Master in  Occupational Therapy degree at Howard University, Washington DC. He practiced as a neurorehabilitation occupational therapist at  Walter Reed National Military Medical Center, Bethesda, Maryland for more than 6 years. His research focuses on examining the cognitive-motor mechanisms with a particular emphasis on attentional control during adaptive upper limb motor performance in individuals with and without amputation.

Kyle Jaquess (Ph.D. Student; co-advised with Dr. Hatfield; email:  kjaquess@umd.edu)
Background and research interests: Kyle achieved a Master's degree in Psychology at California State University, San Bernardino before joining the University of Maryland where he is completing his PhD in Kinesiology under the joint supervision of Dr. Hatfield and Dr. Gentili. His research focuses on how mental workload affects cognitive-motor learning and performance.

Emma Shaw; MA (Ph.D. Student; email: eshaw210@umd.edu)
Background and research interests: Background and research interests: Emma was trained in Cognitive Neuroscience at George Mason University before joining the lab. Her research focuses on examining cognitive-motor processes, such as inhibitory and attentional control, during adaptive motor performance in individuals with and without amputation.

Isabelle Shuggi; M.Sc. (Ph.D. Student; email: ishuggi@umd.edu)
Background and research interests: Before joining the lab Isabelle was trained in Mathematics and System Engineering at the University of Maryland. Her research focuses on how human mental workload is affected during motor learning of reaching movements executed through human-machine interactions under various levels of task difficulty and performance context.

Kayla Beovich (UG Res. Assist., email: kayla.beovich@gmail.com)

Background and research interests:  coming soon!

Mycah Berson (UG Res. Assist., email: mycah045@gmail.com)
Background and research interests: Mycah is an undergraduate student in Kinesiology at the University of Maryland-College Park. She is interested in the understanding of the cognitive-motor processes underlying performance and learning of complex actions in both normal and compromised motor functioning.

 

Elena Danos (UG Res. Assist; email: edanos@terpmail.umd.edu)
Background and research interests: Elena is an undergraduate student in Kinesiology at the University of Maryland-College Park. Her research interests include the examination of cognitive-motor processes underlying mental workload during motor performance under varying task difficulty. She has a particular interest in motor rehabilitation and biomedical applications.

Melissa Hewitt (UG Res. Assist.; email: mhewitt1@terpmail.umd.edu)
Background and research interests: Melissa is an undergraduate student in Kinesiology at the University of Maryland-College Park. Her interest in physical therapy and rehabilitation led her to focus on examining the cognitive-motor learning processes via human-machine interactions in healthy individuals with possible application to assistive technology. 
Mark Houston (UG Res. Assist.; email: markhouston92@gmail.com)
Background and research interests: Before joining the lab Mark taught various martial arts for more than 20 years, running his own school from 2006 to 2016. He is currently a student in the Department of Kinesiology at the University of Maryland-College Park. His research focuses on the analysis of cognitive-motor performance and learning of complex action sequence by means of computational approach to inform human motor behavior. His research has implications for both cognitive-motor rehabilitation and human-robot interactions.

Mckayla Kelly (UG Res. Assist., email: mckaylakelly@yahoo.com)

Background and research interests:  coming soon!

   

 

We are very proud of our collaborations with:

Auburn University, School of Kinesiology, Auburn 

Department of Aerospace Engineering, Vertical Lift Research Center of Excellence (AGRC), University of Maryland, College Park 

Department of Computer Science, University of Maryland, College Park 

Drexel University, School of Biomedical Engineering, Science and Health Systems, Philadephia

Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore

Institute of Systems Research, A. James Clark School of Engineering, University of Maryland, College Park

Maryland Robotics Center, University of Maryland, University of Maryland, College Park

Program in Neuroscience and Cognitive Science, University of Maryland, University of Maryland, College Park

Syracuse University, Departmemt of Electrical Engineering and Computer Science, Syracuse

The United States Naval Academy, Annapolis

United States Food & Drug Administration, Human-Device Interaction group, Silver Spring

United States Department of Veterans Affairs - Washington DC 

Walter Reed National Military Medical Center

We are always looking for highly motivated graduate and undergraduate students who would like to gain research experience by working in our lab. Interested students with backgrounds in Kinesiology, Biology, Computer Science, Engineering, Mathematics, Neuroscience, and Physical Therapy are encouraged to contact Dr. Gentili.

In addition if you are interested in participating in one of our ongoing research studies please email Dr. Gentili at rodolphe@umd.edu

Journal articles

Papaxanthis C, Schieppati M, Gentili RJ, Pozzo T. (2002). Imagined and actual arm movements have similar durations when performed under different conditions of direction and mass. Experimental Brain Research, 143(4):447-452.

Gentili RJ, Cahouet V, Ballay Y, Papaxanthis C. (2004). Inertial properties of the arm are accurately predicted during motor imagery. Behavioural Brain Research, 155(2):231–239.

Courtine G, Papaxanthis C, Gentili RJ, Pozzo T. (2004). Gait-dependent motor memory facilitation in covert movement execution.  Brain Research, Cognitive Brain Research, 22(1):67-75.

Gentili RJ, Papaxanthis C, Pozzo T. (2006). Improvement and generalization of arm motor performance through motor imagery practice. Neuroscience, 137(3):761-772.

Gentili RJ, Cahouet V, Papaxanthis C. (2007). Motor planning of arm movements is direction-dependent in the gravity field. Neuroscience, 145(1):20-32.

Gentili RJ, Papaxanthis C, Ebadzadeh M, Eskiizmirliler S, Ouanezar S, Darlot C. (2009). Integration of gravitational torques in cerebellar pathways allows for the dynamic inverse computation of vertical pointing movements of a robot arm. Public Library of Science ONE, 4(4):e5176.

Bradberry TJ, Gentili RJ, Contreras-Vidal JL. (2010). Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals. Journal of Neuroscience, 30(9): 3432-3437.

Gentili RJ, Han CE, Schweighofer N, Papaxanthis C. (2010). Motor learning without doing: trial-by-trial improvement in motor performance during mental training. Journal of Neurophysiology, 104(2):774-783.

Bradberry TJ, Gentili RJ, Contreras-Vidal JL. (2011). Fast attainment of computer cursor control with noninvasively acquired brain signals. Journal of Neural Engineering, 8(3):036010.

Gentili RJ, Bradberry TJ, Oh H, Hatfield BD, Contreras-Vidal JL. (2011). Cerebral cortical dynamics during visuomotor transformation: adaptation to a cognitive-motor executive challenge. Psychophysiology, 48(6):813-824.

Rietschel JC, Miller MW, Gentili RJ, Goodman RN, McDonald CG, Hatfield BD. (2012). Cerebral-cortical networking and activation increase as a function of cognitive-motor task difficulty. Biological Psychology, 90(2):127-133.

Gentili RJ, Patricia A Shewokis, Ayaz H, Contreras-Vidal JL. (2013). Functional near-infrared spectroscopy-based correlates of prefrontal cortical dynamics during a cognitive-motor executive adaptation task. Frontiers in Human Neuroscience, 4(7):277.

Miller MW, Presacco A, Groman LJ, Bur S, Rietschel JC, Gentili RJ, McDonald CG, Iso-Ahola S, Hatfield BD. (2014). The effects of team environment on cerebral cortical processes and attentional reserve. Sport, Exercise, and Performance Psychology. 3(1), 61-74.

Di-Wei Huang, Gentili RJ, Reggia JA.(2015). Self-Organizing Maps Based on Limit Cycle Attractors. Neural Networks, 63:208-22.

Gentili RJ, Bradberry TJ, Oh H, Costanzo ME, Kerick SE, Contreras-Vidal JL, Hatfield BD. (2015). Evolution of cerebral cortico-cortical communication during visuomotor adaptation to a cognitive-motor executive challenge. Biological Psychology, 105:51-65.

Gentili RJ, Oh H, Huang D-W, Katz GE, Miller RH, Reggia JA. (2015). A neural architecture for performing actual and mentally simulated movements during self-intended and observed bimanual arm reaching movements. International Journal of Social Robotics, 7(3), 371-392.

Gentili RJ, Papaxanthis C. (2015). Laterality effects in motor learning by mental practice in right-handers. Neuroscience. 297:231-42.

Gentili RJ, Oh H, Kregling VA, Reggia JA. (2016). A cortical model for inverse kinematics computation of a humanoid finger with mechanically coupled joints. Bioinspiration & Biomimetics, 11(3):036013. 

Huang DW, Gentili RJ, Katz GE, Reggia JA. (2017). A limit-cycle self-organizing map architecture for stable arm control. Neural Network. 85:165-181.

Blanco JA, Johnson MK,  Jaquess KJ, Oh H, Lo L-C, Gentili RJ, Hatfield BD. (2016). Quantifying cognitive workload in simulated flight using passive, dry EEG measurements. IEEE Transactions on Cognitive and Developmental Systems. IEEE Transactions on Cognitive and Developmental Systems, 10(2), 373 - 383. 

Katz GE, Huang DW, Hauge TC, Gentili RJ, Reggia JA. (2017). A novel parsimonious cause-effect reasoning algorithm for robot imitation and plan recognition. IEEE Transactions on Cognitive and Developmental Systems, 10(2), 177 - 193.

Shuggi IM, Oh H, Shewokis PA, Gentili RJ. (2017). Mental workload and motor performance dynamics during practice of reaching movements under various levels of task difficulty. Neuroscience. 360, 166-179.

Jaquess KJ, Gentili RJ, Rietschel JC, Lo L-C, Prevost M, Miller MW, Mohler JM, Oh H, Tan YY, Hatfield BD. (2017). Cognitive workload assessment in pilots under various task demands. International Journal of Psychophysiology, 121, 46-55.

Gentili RJ, Jaquess KJ, Shuggi IM, Oh H, Lo LC, Tan YY, Domingues CA, Blanco JA, Rietschel JC, Miller MW, Hatfield BD. (2017). Combined assessment of attentional reserve and cognitive effort under various levels of challenge with a dry EEG system. Psychophysiology. In press. 

Shuggi IM, Shewokis PA, Herrmann JW, Gentili RJ. (2017). Changes in motor performance and mental workload during learning of reaching movements: a team dynamics perspective. Experimental Brain Research. 236(2), 433-451.

Shaw EP, Rietschel JC, Hendershot BD, Pruziner AL, Miller MW, Hatfield BD, Gentili RJ. (2018). Measurement of attentional reserve and mental effort for cognitive workload assessment under various task demands during dual-task walking. Biological Psychology, 134, 39-51.

Oh H, Braun AR, Reggia JA, Gentili RJ. (2018). Fronto-parietal mirror neuron system modeling: visuospatial transformations support imitation learning independently of imitator perspective. Human Movement Science. In press.

Jaquess KJ, Lo L-C, Oh H, Lu C, Ginsberg A, Tan YY, Lohse KR, Miller MW, Hatfield BD, Gentili RJ. (2018). Changes in mental workload and motor performance throughout multiple practice sessions under various levels of task difficulty. Neuroscience. In press.

 

Conference proceedings

Gentili RJ, Bradberry TJ, Hatfield BD, Contreras-Vidal JL. (2008). A new generation of non-invasive biomarkers of cognitive-motor states with application to smart brain computer interfaces. Proceeding of the 16th European Signal Processing Conference (EUSIPCO-2008), EURASIP Society, 2008, August 25-27, Lausanne, Switzerland.

Bradberry TJ, Gentili RJ, Contreras-Vidal JL. (2009). Decoding three-dimensional hand kinematics from electroencephalographic signals. Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’09), September 2-6, p. 5010-3013, Minneapolis, USA.

Gentili RJ, Bradberry TJ, Hatfield BD, Contreras-Vidal JL. (2009). Brain biomarkers of motor adaptation using phase synchronization. Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’09), September 2-6, p. 5930-5933, Minneapolis, USA.

Gillespie RB, Contreras-Vidal JL, Shewokis PA, O'Malley MK, Brown JD, Agashe H, Gentili RJ, Davis A. (2010). Toward improved sensorimotor integration and learning using upper-limb prosthetic devices. Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’10), August 31 - September 4, p. 5077-5080, Buenos Aires, Argentina.

Gentili RJ, Hadavi C, Hayaz H, Shewokis PA, Contreras-Vidal JL. (2010). Hemodynamic correlates of visuomotor adaptation by functional near infrared spectroscopy. Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’10), August 31 - September 4, p. 2918-2921, Buenos Aires, Argentina.

Oh H, Gentili RJ, Reggia JA, Contreras-Vidal JL. (2011). Learning of spatial relationships between observed and imitated actions allows invariant inverse computation in the frontal mirror neuron system. Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’11), August 30 - September 3, p. 4183-4186, Boston, USA.

Gentili RJ, Oh H, Molina J, Contreras-Vidal JL. (2011). Cortical network modeling for inverse kinematic computation of an anthropomorphic finger. Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’11), August 30 - September 3, p. 8251-8254, Boston, USA.

Gentili RJ. (2011). Non-invasive functional brain biomarkers for cognitive-motor performance assessment: Towards new brain monitoring applications. Proceedings of the 14th Human-Computer Interaction Conference. Foundations of Augmented Cognition. Directing the Future of Adaptive Systems. Lecture Notes in Computer Science, July 9-14, 6780(2011), p. 159-168, Orlando, USA.

Shewokis PA, Ayaz H, Izzetoglu M, Bunce S, Gentili RJ, Sela I, Izzetoglu K, Onaral B. (2011). Brain in the loop: Assessing learning using fNIR in cognitive and motor tasks. Proceedings of the 14th Human-Computer Interaction Conference. Foundations of Augmented Cognition. Directing the Future of Adaptive Systems. Lecture Notes in Computer Science, July 9-14, 6780(2011), p. 240-249, Orlando, USA.

Oh H, Gentili RJ, Reggia JA, Contreras-Vidal JL. (2012). Modeling of visuospatial perspectives processing and modulation of the fronto-parietal network activity during action imitation. Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’12), August 28 - September 1, p. 2551-2554, San Diego, USA.

Gentili RJ, Oh H, Molina J, Reggia JA, Contreras-Vidal JL. (2012). Cortex inspired model for inverse kinematics computation for a humanoid robotic finger. Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’12), August 28 - September 1, p. 3052-3055, San Diego, USA.

Oh H, Gentili RJ, Costanzo ME, Lo LC, Rietschel JC, Saffer M, Hatfield BD. (2013). Understanding brain connectivity patterns during motor performance under social-evaluative competitive pressure. Proceedings of the 15th Human-Computer Interaction Conference. Foundations of Augmented Cognition. Lecture Notes in Computer Science, 21 - 26 July, 8027(2013), p. 361-370, Las Vegas, USA.

Gentili RJ, Oh H, Shuggi I, Rietschel JC, Hatfield BD, Reggia JA. (2013). Human-robotic collaborative intelligent control for reaching performance. Proceedings of the 15th Human-Computer Interaction Conference. Foundations of Augmented Cognition. Lecture Notes in Computer Science, 21 - 26 July, 8027(2013), p. 666-675, Las Vegas, USA.

Langsfeld JD, Kaipa KN, Gentili RJ, Reggia JA, Gupta SK. Incorporating failure-to-success transitions in imitation learning for a dynamic pouring task. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ‘14), September 14–18, Chicago, USA.

Gentili RJ, Rietschel JC, Jaquess KJ, Lo L-C, Prevost M, Miller MW, Mohler JM, Oh H, Tan YY, Hatfield BD. (2014). Brain biomarkers based assessment of cognitive workload in pilots under various task demands. Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’14), August 28 - September 01, p. 5860 - 5863, San Diego, USA.

Gentili RJ, Oh H, Huang D-W, Katz GE, Miller RH, Reggia JA. (2014). Towards a multi-level neural architecture that unifies self-intended and imitated arm reaching performance. Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’14), August 28 - September 01, p. 2537 - 2540, San Diego, USA.

Huang D-W, Gentili RJ, Reggia JA. (2014). Limit cycle representation of spatial locations using self-organizing maps. IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB'14), p. 79-84, Orlando, FL, USA.

Johnson MK, Blanco JA, Gentili RJ, Jacquess KJ, Oh H, Hatfield BD. (2015). Probe-independent EEG assessment of mental workload in pilots. IEEE Neural Engineering and Rehabilitation (IEEE NER 2015). ), p. 581 – 584, Montpelier, France.

Oh H, Hatfield BD, Jaquess KJ, Lo L-C, Tan YY, Prevost MC, Mohler JM, Postlethwaite H, Rietschel JC, Miller MW, Postlethwaite H, Blanco JA, Chen S, Gentili RJ. (2015). A composite cognitive workload assessment system in pilots under various task demands using ensemble learning. Proceedings of the 17th Human-Computer Interaction Conference. Foundations of Augmented Cognition. Lecture Notes in Computer Science, 9183), 91-100, 2-7 August, Los Angeles, USA, in press.

Gentili RJ, Shuggi IM, King KM, Oh H, Shewokis PA. (2015). Cognitive-motor processes during arm reaching performance through a human body-machine interface. Proceedings of the 17th Human-Computer Interaction Conference. Foundations of Augmented Cognition. Lecture Notes in Computer Science, (9183), 381-392, 2-7 August, Los Angeles, USA.

Huang D-W, Katz GE, Langsfeld J, Gentili RJ, Reggia JA. (2015). A virtual demonstrator environment for robot imitation learning. IEEE International Conference on Technologies for Practical Robot Applications (TePRA 2015), p. 1-6, Boston, MA.

Katz GE, Huang D-W, Gentili RJ, Reggia JA. (2016). Imitation learning as cause-effect reasoning. 9th Artificial General Intelligence (AGI-16). In Steunebrink, B., Wang, P., & Goertzel, B. (Eds.), Artificial General Intelligence, Springer, 64–73, July 16–19, New York City, NY, USA.

Katz GE, Dullnig D, Davis GP, Gentili RJ, Reggia JA. (2017). Autonomous causally-driven explanation of actions. Proceedings of the International Symposium on Artificial Intelligence. In press. 

Katz GE, Huang D-W, Gentili RJ, Reggia JA. (2017). An empirical characterization of parsimonious intention inference for cognitive-level imitation learning. Proceedings of the International Conference on Artificial Intelligence. CSREA Press, 83-89.

 

Book chapters

Gentili RJ, Oh H, Bradberry TJ, Hatfield BD, Contreras-Vidal JL. (2010). Signal processing for non-invasive brain biomarkers of sensorimotor performance and brain monitoring. In S. Miron (Eds.). Signal Processing, p. 461-502, In-Tech, Vienna, Austria.

Gentili RJ, Oh H, J. Molina, Contreras-Vidal JL. (2011). Neural network models for reaching and dexterous manipulation in humans and anthropomorphic robotic systems. In V. Cutsuridis, A. Hussain, J.G. Taylor (Eds.). Perception-Action Cycle: Models, Architectures, and Hardware, p. 187-217, Springer, New York, USA.

HARDWARE

Human Experimental Platforms:

  • Wacom digitizing tablet
  • Motion capture system (Optotrak)
  • IMU (Xsens)
  • Data Glove (CyberGlove)

Computational/Robotic Platforms:

  • Humanoid torso with head and two 7 DOF arms (Baxter; Rethink Robotics)
  • Humanoid finger (ShadowHand)

SOFTWARE

  • Matlab (Linux and Windows OS)
  • Xsens software
  • Optotrak software
  • Cyberglove software

Additional systems and software such EEG (Brainvision) are also available through the Cogmo Division.