جستجوی استاد راهنمای دانشگاهی

Leigh Hochbergمشاهده پروفایل

استاد

• Brain-Computer Interfaces
• Neurotechnology
• ALS
• +۵ مورد دیگر

Leigh Hochberg is the L. Herbert Ballou University Professor of Engineering and Professor of Brain Science at Brown University. He co-directs the Carney Institute for Brain Science and leads the BrainGate clinical trial, focusing on restoring communication and mobility for individuals with paralysis through brain-computer interfaces (BCIs) and neurotechnology research. PhD and MD from Emory University (1999) BS in Engineering from Brown University (1990) Research Interests span ALS , paralysis , spinal cord injury , and stroke , with a translational focus on BCI-enabled communication and neuroprosthetic limb control . His work bridges human intracortical neurophysiology and clinical neurorehabilitation . Publication Trends (15 most recent) highlight advancements in invasive BCI systems , neural decoding , and neuroprosthetic control (2015-2022). Key subfields include motor cortex dynamics , seizure prediction , speech decoding , and real-time neural signal processing . Scientific Awards: Israel Brain Technologies International B.R.A.I.N. Prize (2013) CERF Prize in Medical Engineering (2022) Paul B. Magnuson Award (2022) Grants include funding from the NIH BRAIN Initiative, Department of Veterans Affairs, and National Institute on Deafness and Other Communication Disorders. He collaborates extensively with John Donoghue , John Simeral , and David Borton in neurotechnology development. Labs & Teams include the BrainGate consortium and the Warren Alpert Medical School Faculty , integrating engineering , neuroscience , and clinical neurology disciplines.

Franck Amadieuمشاهده پروفایل

استاد

• Cognitive Psychology
• Human-Computer Interaction
• Educational Technology
• +۳ مورد دیگر

Franck Amadieu is a Professor at the Cognition, Languages, Ergonomics (CLLE) research unit of the University of Toulouse - Jean Jaurès . His work bridges cognitive psychology and human-computer interaction, focusing on how digital technologies (hypertexts, animations, tablets) influence learning processes and information retrieval. He examines cognitive load, prior knowledge effects, and user experience in multimedia learning environments. Research Trends: His recent publications analyze adolescents' evaluation of online video sources, disorientation in hypermedia navigation, and the acceptance of brain-computer interfaces (BCIs) for stroke rehabilitation. Key themes include critical thinking development, technology acceptance models, and cognitive resource allocation during digital learning. Collaborative Networks: He collaborates with institutions like ENAC (Aeronautical Computer Human Interaction) , LII (Interactive Computer Team) , and ACHIL . His interdisciplinary approach connects psychology, education, and neuroergonomics to optimize technology design for diverse populations.

Ozcan Ozdamarمشاهده پروفایل

استاد پژوهش

• Biomedical Engineering
• Neuroscience
• Visual Evoked Potentials
• +۵ مورد دیگر

Ozcan Ozdamar is a Research Professor in the Department of Biomedical Engineering at the College of Engineering, University of Miami, with additional affiliations in Otolaryngology and Neuroscience departments, reflecting his interdisciplinary research approach. Dr. Ozdamar's research spans several key areas in neural signal processing: Visual Evoked Potentials (VEPs) and Pattern Electroretinography (PERG) Brain-Computer Interface (BCI) development Auditory processing and binaural hearing Signal processing techniques for neural response extraction Neurophysiological assessment methods His methodological innovations focus on improving signal-to-noise ratios and developing more efficient data acquisition protocols. Recent work demonstrates continued productivity with publications in high-impact journals such as Journal of Vision, Journal of the Acoustical Society of America, and Translational Vision Science & Technology. His research shows consistent methodological innovation across visual and auditory neuroscience domains. Dr. Ozdamar has developed novel approaches including: Deconvolution methods for simultaneous extraction of neural responses Quasi-steady-state stimulation paradigms Advanced techniques for measuring full-range auditory event-related potentials Next-generation PERG methods with expanded dynamic range His work bridges fundamental neuroscience with practical clinical applications, particularly for diagnosing conditions affecting visual and auditory pathways, and developing assistive technologies for individuals with disabilities.

• Pediatric Brain-Computer Interfaces
• Brain-Computer Interfaces
• Signal Processing
• +۳ مورد دیگر

Eli Kinney-Lang serves as an Azrieli Accelerator Professor within the Department of Biomedical Engineering at the Schulich School of Engineering, University of Calgary. They also hold appointments as Engineering Core Director and Child Health and Wellness Researcher at the Alberta Children's Hospital Research Institute, Owerko Centre. Their primary research focus is developing inclusive brain-computer interface systems for pediatric applications. Educational background: PhD in Digital Signal Processing (Brain-computer Interfaces), University of Edinburgh, UK BSc in Mathematics, University of Wyoming, USA Kinney-Lang's research specializes in Pediatric Brain-Computer Interfaces, addressing the critical gap in BCI technology for children with complex needs. They investigate adaptations across the entire BCI pipeline - from signal acquisition and processing to machine learning classification and feedback - ensuring inclusivity for diverse pediatric populations. This work bridges biomedical engineering, child health, and assistive technology development. They participate in multiple university strategic initiatives including Brain and Mental Health (2015-2021), Child Health and Wellness (2020-2025), Engineering Solutions for Health (2015-2021), and Human Dynamics in a Changing World (2015-2021). Award: Azrieli Accelerator Professorship in Inclusive Engineering Technologies for Neurodevelopment Kinney-Lang directs the BCI4Kids initiative, which develops brain-computer interface technologies specifically for children. Their work integrates engineering solutions with child health research to create accessible neurotechnology, supported by their dual affiliations with the university's engineering school and children's hospital research institute.

Girijesh Prasadمشاهده پروفایل

استاد

• Intelligent Systems
• Brain-Computer Interfaces (BCI)
• Neuro-rehabilitation
• +۳ مورد دیگر

Prof. Girijesh Prasad is a leading academic in Intelligent Systems at Ulster University , UK, where he directs the Northern Ireland Functional Brain Mapping (NIFBM) facility at the Intelligent Systems Research Centre (ISRC). He leads the Cognitive Neuroscience and Neurotechnology research team and has supervised 22 PhD students. B.Tech in Electrical Engineering (1987), M.Tech in Computer Science (1992), PhD in Electrical Engineering (1997) Chartered Engineer, Fellow of IET & Higher Education Academy, Senior IEEE Member His research spans brain-computer interfaces , neuro-rehabilitation , machine learning , and computational modeling , with recent work focusing on quantum classifiers for EEG data, MEG-based MCI detection, and semiconductor production optimization. His 2025 publications highlight advances in quantum-enhanced ML and neuroimaging analysis . 2017 Senior Distinguished Research Fellow at Ulster University 2015 IEEE Best Poster Award recipient 2011 IJCNN Best Paper Award winner Prof. Prasad has secured over £10M in grants from agencies like UKRI, Royal Society, and Invest Northern Ireland, with active projects in smart manufacturing , stroke rehabilitation , and blockchain-based e-healthcare (2024-2026). His lab develops cutting-edge systems integrating robotic exoskeletons and neurofeedback protocols for clinical applications.

Mahsa Shoaranمشاهده پروفایل

استادیار

• Neural interfaces
• Neuroprosthetics
• Analog and mixed-signal integrated circuits
• +۵ مورد دیگر

Mahsa Shoaran is a Tenure-Track Assistant Professor at EPFL (École Polytechnique Fédérale de Lausanne) jointly appointed at the Center for Neuroprosthetics and the Institute of Electrical Engineering . She is also the founding director of the Integrated Neurotechnologies Laboratory (INL) and contributes to several doctoral programs and teaching missions across EPFL’s School of Engineering (STI). Education: PhD in Electrical Engineering, EPFL (2015) M.Sc. & B.Sc., Sharif University of Technology Postdoctoral Fellow, California Institute of Technology (Caltech) (2015-2017) Former Assistant Professor, School of Electrical and Computer Engineering, Cornell University (2017-2019) Research Focus: Dr. Shoaran’s work sits at the convergence of integrated circuit design , machine learning , and neuroscience . Her group develops ultra-low-power, miniaturized system-on-chips (SoCs) capable of real-time neural recording , pathology detection , and closed-loop therapeutic intervention such as adaptive neurostimulation. Machine-learning algorithms running on-chip enable precise symptom detection in neurological and psychiatric disorders, while advanced circuit techniques guarantee energy efficiency suitable for long-term implantable or wearable neural interfaces. Scientific Awards & Grants: ERC Starting Grant 2021 Google Faculty Research Award in Machine Learning 2019 Swiss NSF Postdoctoral Fellowships NSF Award for Young Professionals – Smart & Connected Health MIT EECS Rising Star 2015 Doctoral Advising & Service: She currently supervises 14 PhD students in the Integrated Neurotechnologies Laboratory. Additionally, she serves on the PhD program committees for Electrical Engineering (EDEE), Microsystems & Microelectronics (EDMI), and contributes to the Neuro-X and SEL teaching programs. She is a Technical Program Committee member for IEEE CICC and serves on the Student Research Preview committee for ISSCC. Laboratory & Collaborative Teams: The Integrated Neurotechnologies Laboratory (INL) at EPFL Campus Biotech in Geneva hosts her interdisciplinary team of circuit designers, machine-learning researchers, and neuroscientists. The lab collaborates closely with clinicians to translate innovations into real-world neuroprosthetic and diagnostic devices.

Fabien Lotteمشاهده پروفایل

دانشگاهی

• Brain-Computer Interfaces
• Machine Learning
• Neuroscience
• +۳ مورد دیگر

Fabien Lotte is a Senior Researcher (Directeur de Recherche DR2) at Inria, affiliated with the University of Bordeaux. He leads the Potioc team, focusing on Brain-Computer Interfaces (BCI) and related technologies. Research Interests: Brain-Computer Interfaces (BCI) for motor imagery and neurofeedback Machine learning on Riemannian manifolds for EEG analysis Reproducibility in neural engineering research Passive BCI for cognitive/affective state estimation Neuroergonomics and adaptive systems Scientific Contributions: His recent work explores Riemannian geometry for BCI, including feature fusion, visualization techniques, and performance prediction via median nerve stimulation. He has developed open-source tools like BioPyC and contributed to large datasets for BCI reproducibility. Scientific Awards: 2023 : Nature Mentorship Award (Mid-Career Category) 2023 : Lovelace-Babbage Prize from French Academy of Science Advising and Collaborations: Supervised multiple PhD students (Léa Pillette, Jelena Mladenovic, David Trocellier) and postdocs. Leads ANR projects (STIM-BCI, BCI4IA) and ERC-funded initiatives (BrainConquest, SPEARS).

Venkata Sriram Siddhardh Nadendlaمشاهده پروفایل

استادیار

• Cyber-physical-human systems
• Human-system interaction
• Statistical inference
• +۵ مورد دیگر

Dr. Venkata Sriram Siddhardh Nadendla serves as an Assistant Professor in the Department of Computer Science within the College of Engineering at Missouri University of Science and Technology. His research bridges theoretical foundations in information theory with practical applications in cyber-physical-human systems across critical infrastructure domains. His educational background includes: PhD in Electrical Engineering and Computer Science from Syracuse University MS from Louisiana State University BE from SCSVMV University, India Dr. Nadendla's research spans cyber-physical-human systems with particular emphasis on human-system interaction , statistical inference , and multi-agent decision making . His work integrates machine learning, game theory, and behavioral decision theory to address challenges in transportation systems, mining safety, and cybersecurity. Recent publications show growing focus on fairness in AI systems and neuromorphic computing applications. Analysis of his 15 most recent publications reveals concentration in transportation systems (20%), cybersecurity (25%), human-AI interaction (30%), and healthcare applications (10%), with remaining work spanning mining safety and theoretical decision frameworks. His research demonstrates consistent progression from theoretical foundations toward practical implementations in critical infrastructure. Dr. Nadendla is actively affiliated with the Center for Intelligent Infrastructure at Missouri S&T, where he contributes to developing smart infrastructure solutions. His work uniquely combines quantum cognition models with traditional game theory approaches to model human decision-making in complex systems.

• Brain-Computer Interface
• Brain Imaging
• Machine Learning
• +۲ مورد دیگر

Amad Zafar is a Research Professor at Sejong University's Department of Artificial Intelligence and Robotics. He holds a PhD in Intelligent Control & Automation from Pusan National University (2019) and has previously held academic positions at the University of Wah and University of Lahore Islamabad Campus. Education: B.Sc., University of Wah, Pakistan (2009) MS., University of Wah, Pakistan (2013) Ph.D., Pusan National University, South Korea (2019) His research focuses on Brain-Computer Interfaces , Brain Imaging , Machine Learning , Intelligent Systems , and Battery Technology . His notable achievements include developing a Fast fNIRS-based BCI , Battery SOC/RUL Prediction , and Hotspot Detection in Solar Panels . Recent publications highlight trends in medical imaging analysis (diabetic retinopathy, skin lesions, breast cancer), deep learning frameworks for healthcare, reinforcement learning for traffic optimization, and hybrid neural networks for brain signal processing. All works emphasize optimization techniques and intelligent system design. Professional experience spans from Lab Engineer (2010-2013) to Research Professor (2022-Present) . Collaborations with institutions in South Korea, Pakistan, and international researchers are evident through co-authorships. Research outputs (87 total) demonstrate sustained contributions across biomedical engineering, energy systems, and AI since 2016.

Pietro Aricoمشاهده پروفایل

استادیار

• Brain-Computer Interfaces (BCI)
• Bioengineering
• Signal Processing
• +۱۲ مورد دیگر

Pietro Arico is an Assistant Professor at the Department of Computer, Control and Management Engineering, Sapienza University of Rome, and Chief Technology Officer at BrainSigns. He holds a PhD in Bioengineering from University of Bologna (2014) and an M.Sc. in Biomedical Engineering from Sapienza University of Rome (2010). Biomedical Engineering Education Expertise in Brain-Computer Interfaces (BCI) Specializes in biosignal processing and machine learning His research focuses on: Passive BCI Systems: Real-time assessment of mental states (workload, attention, stress) using EEG, ECG, and other biosignals in operational environments like driving and aviation. Signal Processing Innovation: Development of algorithms for artifact removal and feature extraction in non-laboratory settings. Hybrid BCI Applications: Integration of EEG and EMG for rehabilitation systems targeting post-stroke patients. Recent work trends emphasize: Real-time mental workload monitoring in clinical and automotive contexts. Artifact correction techniques for EEG data in naturalistic environments. Neurophysiological characterization of mixed reality embodiment. Scientific awards include: 2024 - MINDTOOTH paper selected for AIIC National Conference 2014 - Award 'Massimo Grattarola' for BCI research 2014 - Scientific Prize 'I Guidoniani' for mental load studies 2013 - Finalist for Scientific Award 'I Guidoniani' He teaches Biosignal Processing , Biological System Models , and Biosignal Processing and Electromagnetic Fields in Sapienza's MSc programs in Biomedical Engineering and Medicine and Surgery HT.

Jlenia Toppiمشاهده پروفایل

دانشیار

• Biomedical Signal Processing
• EEG Signal Processing
• Brain Connectivity
• +۶ مورد دیگر

Jlenia Toppi is an Associate Professor at the Department of Computer, Control and Management Engineering, Sapienza University of Rome. With a background in Biomedical Engineering (B.Sc. and M.Sc. summa cum laude from University of Rome, Ph.D. from University of Bologna), she leads research at the Neuroelectrical Imaging and BCI Laboratory, IRCCS Fondazione Santa Lucia, focusing on EEG signal processing, brain connectivity modeling, and graph theory applications in cognitive and clinical neuroscience. Education: B.Sc. Clinical Engineering (2006), M.Sc. Biomedical Engineering (2009), Ph.D. Biomedical Engineering (2013) Awards: IEEE EMBC Student Paper Finalist (2012), Young Bioingegneria Award (2012), IEEE EMBS Best Poster (2011) Her research develops advanced EEG methodologies for brain mapping and dynamic connectivity estimation , applied to disorders of consciousness, stroke rehabilitation, and social neuroscience. She pioneered hyperscanning EEG for interpersonal brain-to-brain analysis and contributed to BCI systems like RECOM and The Promotoer. Recent publications highlight her work on hybrid BCI rehabilitation , muscle synergy extraction , and multi-modal assessment of therapeutic interventions . She serves as editor for Computation and Mathematical Methods in Medicine and collaborates with international projects including Horizon 2020.

• Artificial Intelligence
• Brain-Computer Interfaces
• Fuzzy Neural Networks
• +۸ مورد دیگر

Distinguished Professor Chin-Teng Lin is a leading academic at the University of Technology Sydney (UTS) , where he serves as Co-Director of the Australian AI Institute (AAII) and Director of the Computational Intelligence and Brain Computer Interface Lab . With a career spanning decades, he has pioneered advancements in artificial intelligence (AI) and brain-computer interfaces (BCI) , focusing on human-machine collaboration, wearable EEG systems, and neuroergonomics. School of Computer Science, UTS Co-Director, Australian AI Institute Director, Computational Intelligence and BCI Lab Lin’s research interests are deeply rooted in machine-intelligent systems , cognitive neuroscience , and human-centric AI . He has developed groundbreaking technologies like fuzzy neural networks (FNNs) in 1992, which revolutionized AI by integrating human-like reasoning. His work extends to multi-agent reinforcement learning for cybersecurity, wearable EEG devices for real-world applications, and neurofeedback interventions for chronic pain management. His research outputs include over 950 peer-reviewed publications , with a focus on deep learning , transformer models , and clustering algorithms . Articles like the MGRW-Transformer (2025) and Autonomous Clustering (2025) highlight his leadership in interpretable AI and parameter-free methods. Scientific awards and recognitions include: IEEE Fellow (2005) IFSA Fellow (2012) IEEE Fuzzy Systems Pioneer Award (2017) Outstanding Achievement Award, Asia Pacific Neural Network Assembly Lin has supervised 72 PhD candidates , 30 postdoctoral fellows , and 237 research Masters students since 1992, mentoring notable alumni such as Dr. Zehong Cao (ARC DECRA Fellow) and Prof. Chia-Feng Juang (IEEE Fellow). His funding portfolio includes $10M+ from the US Army Research Lab , $3.8M from the Australian Defence Innovation Hub , and $30.2M in industry collaborations .

• Machine Learning
• Artificial Intelligence
• Data Analytics
• +۹ مورد دیگر

Dr. Mukesh Prasad is an Associate Professor at the School of Computer Science , University of Technology Sydney (UTS). With expertise in Machine Learning , Artificial Intelligence , and Computer Vision , his research addresses applications in healthcare, biomedical science, and smart infrastructure. He holds a Ph.D. in Computer Science from National Chiao Tung University, Taiwan, and an M.S. in Computer and Systems Sciences from Jawaharlal Nehru University, India. Key research areas: Machine Learning, AI, Brain-Computer Interfaces, IoT, and Evolutionary Computation Industry experience: Principal Engineer at TSMC (2016-2017), Postdoctoral Researcher at National Chiao Tung University Dr. Prasad has secured competitive grants for AI applications in disaster response, conversational agents, and medical diagnostics. His work has been published in high-impact venues like IEEE , ACM Transactions , and Springer Nature , with over 200 peer-reviewed papers. He serves on editorial boards for journals including Frontiers in Neurorobotics and ACM Computing Surveys . Scientific Awards: Vice Chancellor Teaching and Learning Citation Award (2019) Alumni Fellowship for Ph.D. (2014) Golden Bamboo NCTU Fellowship (2010) Professional Members: IEEE (2011), ACM (2019)

• Brain-Computer Interface
• Stroke Rehabilitation
• Robotic Engineering
• +۳ مورد دیگر

Dr. Khairul Azlan A Rahman serves as Executive – Engineering at University of Wollongong Malaysia while maintaining active research roles as a part-time project researcher at GIRA and MENTARI research departments. His career bridges academic research and practical engineering applications in biomedical technology. His educational foundation includes a PhD in Electrical & Electronic Engineering from Universiti Tun Hussein Onn Malaysia (2020), Master in Engineering (Robotic) from UTHM (2011), Bachelor in Engineering (Power) from Universiti Teknologi Malaysia (2008), and Diploma in Electrical Engineering (Mechatronic) from UTM (2005). Dr. Rahman's research centers on Brain-Computer Interface (BCI) and Functional Electrical Stimulation (FES) systems for stroke and paraplegic rehabilitation. His work develops innovative control mechanisms that translate brain signals into physical movement assistance, creating pathways for patients to regain mobility through neurotechnology. This research sits at the critical intersection of biomedical engineering, robotics, and clinical rehabilitation, with strong emphasis on practical implementation of assistive devices. His scientific contributions have been recognized through multiple international awards: Special Award and Silver Medal from Saudi Arabia at IPITEx 2020 Silver Medal at Bangkok International Intellectual Property Exposition (IPITEx 2020) Gold Medals at NIICe2019 and iMIT 2019 Gold Medal at International Research and Innovation Symposium (RiSE 2018) Dr. Rahman actively contributes to academic development through postgraduate supervision, having served as Co-Supervisor for Medina exchange students at UTHM. His consultancy projects, including the Mechanical Robot Development for Ducting Cleaning initiative, demonstrate his ability to translate research into industrial applications. Current research activities continue through his part-time roles at GIRA and MENTARI where he explores next-generation rehabilitation technologies while maintaining freelance work in robotics and programming.

• Brain-Computer Interfaces
• Biosignal Processing
• Assistive Robotics
• +۳ مورد دیگر

Gabriel Pires is an Adjunct Professor in the Department of Electrical Engineering at the Polytechnic Institute of Tomar (Portugal) and Integrated Researcher at the Institute of Systems and Robotics - University of Coimbra. He coordinates Lab. VITA.IPT (Life Assisted by Intelligent Environments) and leads the Brain-Computer Interface research group in the Human-Centered Mobile Robotics Lab. His cross-disciplinary affiliations include collaboration with the Coimbra Institute for Biomedical Imaging and Translational Research. Dr. Pires specializes in EEG-based brain-computer interfaces and biosignal processing, aiming to enhance BCI reliability for clinical rehabilitation and assistive technologies. His research spans: BCI usability enhancement Wearable biosensors development Telehealth platforms Human-robot interaction Machine learning for medical applications He has supervised over 70 students and researchers, and manages multiple national/EU projects including: BCI4ALL (€1.8M FEDER, 2025-2028) FLEXEMG (wearable muscle sensors) UpReGain (stroke rehabilitation) POWERAUT (autism intervention) Honors include the IEEE Transactions Best Paper Award (2022) and Fraunhofer Portugal Challenge.