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Symbiosis of Accessible EEG and Powerful AI: New Prospects and Challenges for Brain-Derived Biomarkers in Medical Innovation
Location: Alte Kornkammer - 1/15/26, 2:30 PM - 1/15/26, 3:00 PM (Europe/Berlin) (30 minutes)
Symbiosis of Accessible EEG and Powerful AI: New Prospects and Challenges for Brain-Derived Biomarkers in Medical Innovation
Anton Filipchuk
Scientific Lead at Human Augmented Brain Systems (HABS)
Anton Filipchuk
Scientific Lead at Human Augmented Brain Systems (HABS)
Dr. Anton Filipchuk is a neuroscientist whose research focuses on brain electrophysiology and systems neuroscience. He earned his PhD in Neuroscience in 2011 at Aix-Marseille University (France), where he investigated early neuronal mechanisms of ALS using combined experimental and computational modeling approaches (e.g. Filipchuk et al, 2012, 2021). Over the course of his international research career - spanning institutions including CNRS, INSERM, Université Paris-Saclay (France), CSIC (Spain) and as a visiting faculty member at the University of Oregon (USA) - he has studied sensory systems across multiple levels of organization, from embryonic plasticity to conscious perception. His work has advanced research on thalamo-cortical dynamics and large-scale cortical activity using calcium imaging (e.g., Moreno-Juan, Filipchuk et al., 2017; Antón-Bolaños et al., 2019; Filipchuk et al., 2022). More recently, the team he supervised has adopted a multibiometric, EEG-based approach to investigate how virtual reality modulates brain heart interactions (Sezer et al., 2025), reflecting a shift in his research trajectory from fundamental mechanisms toward translational applications. Building on this expertise, Anton Filipchuk joined HABS (Human Augmented Brain System) as Scientific Lead. He oversees research on EEG and multibiometric biomarker development and contributes to translating academic findings into applied work in security, mobility, health, and customer experience.
EEG is an almost century-old technology living its revival today and leaving the walls of hospitals and laboratories. Why now? Two major tendencies have converged in recent years: the democratisation of wearable EEG hardware and the rise of AI models. The first gives access to brain data in many new contexts, from at-home recordings to measurements during everyday activities like driving or studying. The second changes how we can read EEG: non-linear and non-stationary patterns that were difficult to handle before are now becoming interpretable, giving hope to overcome old challenges of the field: noise vulnerability and strong inter-subject variability. 

At the same time, peripheral wearables are progressing fast, so multibiometric studies are becoming more and more common. EEG can now be combined with autonomic and other physiological signals for analyses that are more robust and closer to real life. This is why EEG-based multibiometric biomarkers are gaining interest, from drug development to consumer experience evaluation.

In one of our recent multibiometric studies, we combined EEG with autonomic physiology to understand who benefits from a VR-based intervention for real-life state anxiety, which included a relaxing immersive environment, a hypnotic script, and a breath-control exercise. After the intervention, all participants - independently of their anxiety score - showed increased heart rate variability (HRV). But only the responders displayed brain-to-heart connectivity, suggesting that central–peripheral correlates can capture meaningful change that a single modality would miss. 

These correlates also point to two concrete AI directions. First, models can be trained on simultaneous EEG and peripheral signals, then deployed “in the wild” using peripheral data only, while still being informed by the brain. Second, EEG can become a low-cost proxy for advanced imaging: models trained on concurrent EEG-fMRI/PET/MEG recordings can later estimate imaging-derived dynamics from EEG alone, making complex brain processes more accessible.