Lee M. Miller is a Professor and Vice Chair of Academic Affairs in the Department of Neurobiology, Physiology and Behavior at the University of California, Davis, affiliated with the Center for Mind and Brain. His research focuses on neuroengineering, computational neuroscience, and neural mechanisms underlying attention, speech processing, and multisensory integration. Research interests include the development of neural prosthetics, decoding of neuromuscular signals for prosthetic control, and understanding how auditory and visual systems interact during speech perception and attentional processes. His work bridges clinical applications (e.g., cochlear implants) with fundamental neuroscience, leveraging tools like electrophysiological recordings, EEG/MEG, and advanced signal processing techniques. Recent publications highlight innovations in electromyographic speech neuroprosthetics, the topology of neuromuscular signals, and the neural basis of speech-in-noise processing. Miller’s studies emphasize translational potential, such as improving speech synthesis from brain signals and designing haptic feedback systems for motor coordination. His contributions have advanced understanding of neural mechanisms in sensory integration, auditory attention, and the impact of cognitive factors on perception. Miller maintains a lab dedicated to these interdisciplinary efforts, with a focus on both basic science and clinical applications.
Emma Colamarino is a Researcher at the Department of Computer, Control and Management Engineering "Antonio Ruberti" of Sapienza University of Rome. She holds an M.Sc. in Biomedical Engineering (2014, cum laude) and a Ph.D. in Bioengineering (2019). Since 2015, she has been a research collaborator at the Neuroelectrical Imaging and Brain-Computer Interfaces Lab of IRCCS Fondazione Santa Lucia in Rome and served as a Visiting Ph.D. student at Imperial College London (2018). From 2019 to March 2023, she was a Post-Doctoral Fellow at Sapienza University. Her research focuses on Advanced electroencephalographic (EEG) and electromyographic (EMG) signal processing Brain-Computer Interface (BCI) protocols for cerebral function recovery Machine learning in neurorehabilitation Hybrid BCIs integrating cortico-muscular networks Recent publications address stroke rehabilitation, BCI design, spectral graph theory, and EMG-EEG integration. Her work spans biomedical data analysis, neuroengineering, and rehabilitation technology validation. Scientific awards include multiple grants from Sapienza University and the Italian Ministry of Health, a Student Award at the 7th International BCI Meeting (2018), and recognition as a Subject Expert (2019). She has supervised/co-supervised 18 MD theses across Biomedical, Management, and Robotics Engineering disciplines.
Jenny Vojtech is a Research Assistant Professor in the Department of Speech, Language & Hearing Sciences at Boston University and serves as Associate Director of the STEPP Lab for Sensorimotor Rehabilitation Engineering. Her interdisciplinary work bridges biomedical engineering and clinical speech pathology to develop computational solutions for voice and speech disorders. Her academic credentials include: PhD in Biomedical Engineering, Boston University (2020) MS in Biomedical Engineering, Boston University (2019) BS in Bioengineering, University of Maryland (2015) Dr. Vojtech's research centers on sensorimotor rehabilitation engineering with specialization in voice disorder analysis , speech motor control , and augmentative communication systems . She develops computational algorithms to objectively characterize voice pathologies and creates software for speech analytics in cognitive monitoring applications. Her work uniquely integrates electromyographic signal processing , human-computer interaction design , and clinical rehabilitation principles to address challenges in speech pathology. Analysis of her recent publications (2019-2023) reveals consistent focus on computational approaches to voice and speech analysis, with strong emphasis on methodological refinement for clinical applications. Key thematic threads include algorithmic development for fundamental frequency estimation, EMG-based voice prediction systems, and personalized AAC interface design that accommodates motor impairments. As Associate Director of the STEPP Lab, Dr. Vojtech leads an interdisciplinary research environment focused on engineering solutions for sensorimotor speech disorders. The lab provides infrastructure for developing and validating computational methods that translate engineering innovations into clinically viable rehabilitation tools.
Dr. Paul E. Barkhaus is a Professor of Neurology at the Medical College of Wisconsin (MCW), where he serves as Director of the Amyotrophic Lateral Sclerosis Program (an ALS Association Certified Center of Excellence), Director of the Clinical Neurophysiology Program, and Section Chief of Neuromuscular Diseases. He maintains clinical appointments at Froedtert Hospital, Zablocki VA Medical Center, Children's Wisconsin, and Froedtert Menomonee Falls Hospital, providing comprehensive care for patients with neuromuscular disorders. Dr. Barkhaus earned his MD from Wayne State University School of Medicine in 1975, followed by a Neurology residency at the same institution (1975-1978). His specialized training includes fellowships in Electromyography at the University of Minnesota (1978-1979), Neuromuscular Diseases at the University of Arizona (1979-1980), and additional research fellowships in Single Fiber Electromyography at Uppsala University, Sweden and Electromyography at Duke University Medical Center. He has held progressive academic positions from Assistant Professor at the University of Cincinnati (1980-1985) to his current Professorship at MCW (2002-present). His research primarily focuses on Amyotrophic Lateral Sclerosis (ALS), with particular emphasis on drug trials for ALS patients, Motor Unit Number Indexing (MUNIX), and Compound Muscle Action Potentials. Dr. Barkhaus has developed significant expertise in quantitative electromyography and has contributed extensively to the understanding of electrophysiological markers in neuromuscular disorders. His recent work increasingly integrates machine learning and augmented intelligence approaches to analyze electromyographic data, demonstrating a clear evolution from fundamental electrophysiological principles to sophisticated computational analysis in clinical applications. Dr. Barkhaus has authored over 125 publications, with recent work spanning ALS treatment alternatives, advanced electromyography techniques, and neuromuscular disease diagnostics. His research demonstrates consistent methodological rigor and clinical relevance, with publications appearing in leading journals including Muscle & Nerve, Clinical Neurophysiology, and Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration. Distinguished Service Award, Wayne State University School of Medicine (1975) Trainee Award, Federation of Western Societies of Neurological Sciences (1980) Clinical Neurophysiology Teacher of The Year, MCW Department of Neurology (2001, 2010) Selected for inclusion in "Best Doctors®" (2005-present) Special Commendation by Paralyzed Veterans of America for ALS advocacy (2010) As an educator, Dr. Barkhaus has directed multiple training programs including the Clinical Neurophysiology Training Program at MCW since 2000. He has mentored numerous trainees in electromyography and neuromuscular diseases, contributing significantly to the development of the next generation of neuromuscular specialists. His laboratory work has been instrumental in developing quantitative approaches to electromyography that are now widely used in clinical practice worldwide.
Dr. Ale Aranceta-Garza is a Senior Lecturer in Biomedical Engineering at the School of Science and Engineering , University of Dundee. Her work focuses on high-density surface electromyography (HDsEMG) for motor control analysis and the development of assistive medical technologies. PhD in Biomedical Engineering from the University of Strathclyde (2015) Research Interests: Ale's research bridges neurorehabilitation and assistive technology design , particularly for vulnerable populations in low-income regions. She develops HDsEMG biomarkers to improve stroke rehabilitation and collaborates with TalarMade (UK) and institutions in Vietnam and Mexico on orthotic and prosthetic solutions. Publication Trends: Ale's recent work emphasizes prosthetic control systems , stroke recovery , and orthotic device validation , with applications in low-income healthcare and neurological disorders . Her studies often integrate electromyographic analysis and patient-centered outcomes . Scientific Awards: 2020 Delsys Prize Award by the DeLuca Foundation Fellow of the Higher Education Academy (HEA) Teaching & Supervision: Ale leads modules in Biomedical Sensors , Design Centre Learning , and Rehabilitation Engineering . She supports PhD students and serves on the EPSRC Review College Panel. Collaborative Projects: Ale's active project EMBRS (2022–2026) involves international teams from Slovenia, Mexico, and NHS Tayside to develop HDsEMG biomarkers for stroke rehabilitation.
Dr. Andrew Hamilton-Wright is an Associate Professor at the School of Computer Science, University of Guelph, part of the College of Engineering and Physical Sciences. He is a member of the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) and has been at the university since 2017. He holds a PhD from the University of Waterloo (2006) and previously worked as a Postdoctoral Researcher at Queen’s University and an Associate Professor at Mount Allison University. His research focuses on machine learning, association mining, and data visualization to support decision-making in healthcare. Key areas include electrophysiological data analysis for disease characterization, fatigue and pain prediction using biophysical signals, and the development of robust software tools for visualizing complex data. He has pioneered work on EMG simulators and models for validating techniques in neuromuscular research. Notable awards include an NSERC Discovery Grant (2021) and an NSERC Engage Grant (2018). His work has been featured in media coverage, such as CTV News’ report on an iron-tracking app for genetic condition management. He is also affiliated with the OneHealth Institute and editorial boards in scientific journals. His grants and collaborations include NSERC-funded projects and partnerships with industry. He advises on ergonomic and postural data analysis, with applications in occupational health and safety. His lab focuses on integrating machine learning with biomedical systems to improve clinical decision-making.
Lusophone University of Humanities and TechnologiesPortugal
Sérgio Miguel Álvaro Marta is a researcher affiliated with Universidade Lusófona de Humanidades e Tecnologias, focusing on sports biomechanics and neuromuscular activity. His academic background includes a Doctorate in Human Motricity and a Master’s in Physical Education and School Sports from Universidade Lusófona. He has contributed extensively to the analysis of electromyographic (EMG) patterns in golf swings, muscle activation during exercises, and musculoskeletal health in athletes. Doctorate in Human Motricity (2014) Master’s in Physical Education and School Sports (2006) Licentiate in Physiotherapy His research explores golf biomechanics , focusing on trunk muscle activation , lower limb EMG patterns , and shoulder musculature analysis . He investigates muscle coordination during sports movements, EMG onset detection methodologies, and exercise prescription for injury prevention. His work bridges neuromuscular activity with sports performance optimization . Key article trends include golf swing mechanics , muscle activation sequencing , and validation of sports-related surveys . He has published in journals like Journal of Electromyography and Kinesiology and Research Quarterly for Exercise and Sport , often comparing skill-level differences and exercise variations in muscle engagement. His work has been presented at conferences such as the European College of Sport Science (ECSS) and published in peer-reviewed journals. He utilizes EMG and kinematic analysis to study human movement in sports contexts, particularly golf.
Pamela Hardaker is an Associate Professor for Student Experience and Lecturer in Computer Technology at De Montfort University, within the Faculty of Computing, Engineering and Media and the School of Computer Science and Informatics. She holds a PhD in Computer Science (2019), an MSc in Intelligent Systems and Robotics with Distinction (2014), and a BSc in Information Technology (1995), all from DMU. BSc in Information Technology, De Montfort University – 1995 MSc in Intelligent Systems and Robotics, De Montfort University – 2014 PhD in Computer Science, De Montfort University – 2019 Her research centers on assistive technologies, particularly the application of computational intelligence and sensor data to improve the control of lower limb prostheses. She specializes in analyzing electromyographic (EMG) signals for walking state detection, aiming to enhance mobility for individuals with disabilities. Her work bridges intelligent systems, robotics, and real-world healthcare applications. Her recent publications show a consistent focus on using machine learning and signal processing techniques to interpret physiological signals for prosthetic control, demonstrating a strong interdisciplinary trend between computer science and biomedical engineering. She has received notable recognition through her High Flyers PhD Scholarship from DMU and is a Senior Fellow of the Higher Education Academy , reflecting excellence in both research and teaching. Pamela has taught various courses in computer technology and contributed to e-learning design. Her PhD was supported by internal university funding, indicating institutional recognition of her research potential. Though no advisees are listed, her work was supervised by senior faculty including Dr. Ben Passow and Professors David Elizondo, David Corne, and Martin Grootveldt. Her research was conducted within DMU’s academic environment, likely involving collaboration through the School of Computer Science and Informatics, though no formal lab or team name is specified.
Matthew Williams, PhD, is an Assistant Professor and Associate Chair in the Department of Biomedical Engineering at Case Western Reserve University's Case School of Engineering. He holds dual affiliations with the School of Engineering and the School of Medicine. His research focuses on neuroprosthetics, rehabilitation engineering, and biomedical standards education. Key areas include developing advanced prosthetic control systems, improving mobility for individuals with disabilities, and integrating standards into engineering curricula. Williams' work combines biomechanical analysis, neural signal processing, and clinical validation. He has pioneered methods for 4-DoF prosthetic hand control using synergy-inspired algorithms and explored metabolic efficiency in variable impedance prosthetic knees. His studies on stroke rehabilitation and upper limb support systems have advanced understanding of motor recovery pathways. Recent publications emphasize interdisciplinary approaches, including moot court exercises for standards education and co-curricular industry partnerships. His device development spans biomimetic prosthetic hands, head/neck EMG interfaces for tetraplegia patients, and gait analysis tools for amputee populations. Awards and recognitions are not explicitly listed in the provided materials, but his prolific publication record (2002-2024) indicates sustained research impact. Teaching responsibilities include biomedical engineering standards courses and co-curricular programs. Labs and teams associated with his work likely involve collaborations between engineering and medical disciplines, though specific lab names aren't mentioned. Current research trends focus on closing the gap between prosthetic innovation and clinical implementation through rigorous testing and user-centric design.
