On April 21, 2026, the Electronics Laboratory at FEEIT hosted a series of lectures regarding the latest advancements in neuroscience and biomedical engineering as part of the teaching activities for the [Biomedical Electronics](https://gitlab.com/feeit-freecourseware/biomedical-electronics) course. The event offered a unique blend of theoretical and practical insights into the application of modern engineering methods for the diagnosis and treatment of complex neurological and psychiatric conditions.
The first presentation was delivered by Prof. Dr. Silvana Markovska-Simoska from the Macedonian Academy of Sciences and Arts (MANU), who spoke about Electroencephalographic (EEG) profiles in children with autism spectrum disorders. She explained the methods for spectral analysis of EEG signals and the potential for these measurements to serve as clinical biomarkers for early diagnosis and treatment monitoring.
Dr. Spase Petkoski from the Institute of Systems Neurosciences in Marseille presented the development of a digital twin of the human brain. He clarified how biophysically interpretable brain network models enable more precise research into the mechanisms of neurodegeneration, opening new possibilities for applying these technologies in clinical diagnostics and therapy.
The lecture series concluded with M.Sc. Daniela Janeva, also from the Institute in Marseille and a FEEIT alumna, who focused on solving the mysteries of schizophrenia. Through the analysis of multi-modal recordings and the application of unsupervised learning methods, she presented ways to identify clinical subtypes of the disease—a crucial step toward creating individualized therapeutic strategies.
The lecture series was supported by the [FEEIT FabLab] (https://fablab.feit.ukim.edu.mk/) and the Macedonian section of IEEE through its chapters: the Joint Signal Processing and Engineering in Medicine and Biology Society (SP/EMB), the Joint Electron Devices, Instrumentation and Measurement, and Solid-State Circuits Society (ED/IM/SSC), as well as the Life Members Affinity Group (LM80077).
