I envision a future where biomedical data science, machine learning, and artificial intelligence are leveraged to revolutionize healthcare. I aim to:
Vision Statement
My research vision is to pioneer the development of advanced neurotechnology solutions that bridge the gap between neural activity and real-world applications. I envision a future where brain-computer interfaces (BCIs) are seamlessly integrated into daily life, offering unprecedented levels of control and interaction for individuals with disabilities and enhancing human-computer interaction for all.
Innovative Neural Decoding Algorithms My research will continue to push the boundaries of neural decoding by developing sophisticated algorithms that can accurately interpret and predict neural signals. Leveraging multiway and tensor-based methods, I aim to capture the complex, multilinear relationships inherent in brain activity. By integrating advanced machine learning techniques, such as deep learning and recursive tensor decomposition, I seek to enhance the precision and reliability of BCIs.
Unlock new insights into higher-level operation of the brain Through decoding neural data, new insights can be gained in the higher-level operation of the brain. Tools such as electroencephalography (EEG) and (high density) electrocorticography (ECoG) can give new insights into brain functions. Of particular interest is the motor cortex and unlocking more insight into fine and complex motor movement.
Real-time Applications and Clinical Integration A primary goal of my research is to translate theoretical advancements into practical, real-time applications. This involves optimizing algorithms to reduce training times and computational demands, making them viable for clinical settings. I envision BCIs that can be rapidly deployed in neurorehabilitation, enabling stroke patients and amputees to regain motor functions through intuitive and responsive neural interfaces.
Interdisciplinary Collaboration Recognizing the multidisciplinary nature of neurotechnology, my research will foster collaboration across neuroscience, engineering, computer science, and clinical disciplines. By working closely with clinicians, engineers, and fellow researchers, I aim to ensure that our technological innovations meet real-world needs and are grounded in a deep understanding of neural physiology.
Ethical and Accessible Neurotechnology As we develop more advanced BCIs, it is crucial to address the ethical implications and ensure these technologies are accessible to a wide range of users. My vision includes advocating for responsible research practices, equitable access to neurotechnology, and engaging with diverse communities to understand their needs and perspectives. This approach ensures that the benefits of BCIs are distributed fairly and ethically.
Educational Outreach and Knowledge Dissemination
Committed to the dissemination of knowledge, I will continue to publish my findings in leading journals and conferences, and develop open-source tools and frameworks that can be used by researchers and practitioners worldwide. By mentoring students and conducting workshops, I aim to inspire the next generation of scientists and engineers to explore the exciting field of neural interfaces and neurotechnology.
I have existing collaborations with UMC Utrecht Brain Center, Neurology and Neurosurgery, as well as Ghent University (UGent) Hospital, Neurology. I’m actively collaborating with Marc Van Hulle, Laboratory for Neuro- and Psychophysiology, KU Leuven as well as Yves Moreau, Dynamical Systems, Signal Processing and Data Analytics (STADIUS), KU Leuven.
Within my current Postdoc position, I’m involved with ELIXIR and FAIR.
ELIXIR Belgium or “ELIXIR infrastructure for Data and Services to strengthen Life Sciences Research Flanders” is an “International Research Infrastructure (IRI)” project of the “Flemish Research Foundation (FWO)”.
The Flanders Artificial Intelligence Research (FAIR) program is organized around four grand challenges and within this program, Liesbet M. Peeters acts as the use case lead of the Use Case multiple sclerosis (MS), which is part of Grand Challenge 1 (AI-Driven Data Science: Making Data Science Hybrid, Automated, Trusted and Actionable), Work Package 7 (use cases in Health). We aim to speed-up the identification of the right treatment for the right patient at the right time by improving the data management of data that is already collected and applying AI techniques on these datasets. I am taking the position as both the general scientific coordinator of this use case, as well as developing one of the Proof of Concepts (POCs) that make up the usecase.
Cardiovascular sciences
My goal is to become the main lab of expertise for BCIs within Belgium. While there are groups in Leuven, these groups are connected with the Faculty of Engineering and focus on more technical challenges and research such as the development of new electronics. My focus would be primarily on the developing new neural decoding algorithms in order to help patients with locked-in syndrome. My prior host lab for my PhD, under Marc Van Hulle, will be disappearing due to his retirement.
Through these efforts, I aim to lead pioneering research that transforms our understanding of brain function and translates these insights into practical technologies that enhance human capabilities and well-being.