Grigorii Rashkov

PhD Student

grigorii.rashkov@proton.me

Biography

Grigorii is a PhD student at University Tor Vergata. He holds a Master’s degree in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology (MIPT). He also conducted research at the Institute of Higher Nervous Activity and Neurophysiology (IHNA), Moscow, Russia, and Artificial Intelligence Research Institute (AIRI), Moscow, Russia.

His interests include neuroscience, deep learning and artificial intelligence.

His PhD project focuses on decoding semantic representations from brain activity.

Profiles

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Last articles (Scopus)

  • Towards neural foundation models for vision: Aligning EEG, MEG, and fMRI representations for decoding, encoding, and modality conversion; Information Fusion; February 2026; DOI: 10.1016/j.inffus.2025.103650

Last 5 articles (Scopus)

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Last 5 articles (PubMed)

  • NeuroFusion: A Unified Framework for Generalized Visual Stimulus Decoding from fMRI Across Datasets and Subjects

    Recent advancements in neural decoding have shown promising results in reconstructing visual experiences from brain activity. However, existing approaches focus primarily on decoding within a single dataset or subject, which limits generalization across various sources of neuroimaging. In this work, we propose a novel framework for the decoding of visual stimuli between subjects and between data sets, integrating neural recordings from multiple publicly available fMRI datasets. To address...

  • A modular semantic-structural pipeline for visual decoding from primate spiking data via selective temporal integration

    Characterizing the information content of intracortical signals during visual processing is a central challenge in systems neuroscience. We address the problem of decoding visual information from high-density intracortical recordings in primates, using the THINGS Ventral Stream Spiking Dataset. We systematically evaluate the effects of model architecture, training objectives, and data scaling on decoding performance. Results show that decoding accuracy is jointly driven by non-linearity and...

  • Cross-subject decoding of human neural data for speech brain computer interfaces

    Objective.Brain-to-text systems have recently achieved impressive performance when trained on single-participant data, but remain limited by uninvestigated cross-subject generalization.Approach.We present the first neural-to-phoneme decoder trained jointly on the two largest intracortical speech datasets (Willettet al2023Nature6201031-6; Cardet al2024New Engl. J. Med.391609-18), introducing day- and dataset-specific affine transforms to align neural activity into a shared space. Additionally, a...

  • R&B - rhythm and brain: Cross-subject decoding of music from human brain activity

    Music is a universal phenomenon that influences human experiences across cultures. We investigate whether music can be decoded from human brain activity measured with fMRI, by modeling mappings between neural data and latent representations of musical stimuli. Our approach integrates functional and anatomical alignment techniques to facilitate cross-subject decoding. Starting from the GTZan fMRI dataset, where five participants listened to 540 musical tracks from 10 genres, we used the CLAP...

  • Stimulation success!? Improved response inhibition performance after prefrontal single-site and condition-and-perturb transcranial magnetic stimulation

    In everyday behaviour, the ability to stop an already initiated action is critical for ensuring both your safety and that of others; for example, when stopping a reaching movement towards a hot stove-top after realising it is hot. Neuroscientific evidence points towards the critical role of several regions in the right prefrontal cortex in the coordination and execution of this response inhibition-specifically the right inferior frontal gyrus (rIFG) and the right dorsolateral prefrontal cortex...