1 citations · 1 across the 4 of their papers we have counts for
5 papers
Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning
Emanuele Balloni, Emanuele Frontoni, Chiara Matti +3
Decoding visual information from electroencephalography (EEG) has recently achieved promising results, primarily focusing on reconstructing two-dimensional (2D) images from brain a…
EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience
Emanuele Balloni, Emanuele Frontoni, Chiara Matti +3
Reconstructing visual stimuli from non-invasive electroencephalography (EEG) remains challenging due to its low spatial resolution and high noise, particularly under realistic low-…
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI
Xiaofeng Liu, Helen A. Shih, Fangxu Xing +3
Deep learning (DL) models for segmenting various anatomical structures have achieved great success via a static DL model that is trained in a single source domain. Yet, the static…
Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease
Alexandra-Maria Tautan, Elias Casula, Ilaria Borghi +7
Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work,…
Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification
Alexandra-Maria Tautan, Elias Casula, Ilaria Borghi +7
Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD). In…