Artur Petrosyan
- Doctoral Student:Faculty of Computer Science / School of Data Analysis and Artificial Intelligence
- Visiting Lecturer:Faculty of Computer Science / Big Data and Information Retrieval School
- Research Assistant:Institute for Cognitive Neuroscience / Centre for Bioelectric Interfaces
- Artur Petrosyan has been at HSE University since 2018.
Postgraduate Studies
3rd year of study
Approved topic of thesis: Modern methods of machine learning in the interpretation of electrical activity of the brain
Academic Supervisor: Ossadtchi, Alexey
Courses (2020/2021)
- Data Analysis in Python (Bachelor’s programme; Faculty of Economic Sciences; 2 year, 1, 2 module)Rus
- Data Analysis in Python (Bachelor’s programme; Faculty of Economic Sciences; 1 year, 3, 4 module)Rus
- Past Courses
Courses (2019/2020)
- Data Science (Bachelor’s programme; Faculty of Economic Sciences; 2 year, 3, 4 module)Rus
- Machine Learning (Bachelor’s programme; Faculty of Economic Sciences; 4 year, 1, 2 module)Rus
- Machine Learning (Bachelor’s programme; Faculty of Economic Sciences; 3 year, 1, 2 module)Rus
Courses (2017/2018)
Publications4
- Chapter Petrosyan A., Lebedev M., Ossadtchi A. Decoding Neural Signals with a Compact and Interpretable Convolutional Neural Network, in: Advances in Neural Computation, Machine Learning, and Cognitive Research IV. Selected Papers from the XXII International Conference on Neuroinformatics, October 12-16, 2020, Moscow, Russia. Springer, 2021. doi P. 420-428. doi
- Article Petrosyan A., Sinkin M., Lebedev M., Ossadtchi A. Decoding and interpreting cortical signals with a compact convolutional neural network // Journal of Neural Engineering. 2021. Vol. 18. No. 2. Article 026019. doi
- Chapter Petrosyan A., Lebedev M., Ossadtchi A. Linear Systems Theoretic Approach to Interpretation of Spatial and Temporal Weights in Compact CNNs: Monte-Carlo Study, in: Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 2020. Proceedings of the 11th Annual Meeting of the BICA Society. Springer, 2021. doi P. 365-370. doi
- Chapter Volkova K., Petrosyan A., Дубышкин И., Ossadtchi A. Decoding movement time-course from ecog using deep learning and implications for bidirectional brain-computer interfacing, in: Актуальные проблемы психологической науки: Сборник статей и выступлений международной научной конференции / Под общ. ред.: Е. С. Горбунова. Научно-инновационный центр, 2019. doi doi
Successful ECoG decoding in real time!
Today marks the successful completion of the first year of our research project funded by Megagrant #14.641.31.0003 "Bi-directional ECoG BCIs for control, stimulation and communication" (lead scientist: Mikhail Lebedev).
Our clinical studies
Megagrant #14.641.31.0000. Lead scientist: Mikhail Lebedev. Invasive neural interfaces involve the analysis of electrocortigocraphical (ECoG) data recorded from subdurally implanted electrode arrays. This can be done in patients who require implanting an electrode array for medical reasons, therefore ECoG recording sessions are conducted exclusively in clinical settings. Our Center collaborates with two clinics: Clinical Medical Center of the Yevdokimov Moscow State Medico-Dental University and the Polenov Russian Research Institute of Neurosurgery.
