activity
20242026
most citedDREAMS: A python framework for Training Deep Learning Models on EEG Data with Model Card Reporting for Medical Applications

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Explainability of Complex AI Models with Correlation Impact Ratio

Poushali Sengupta, Rabindra Khadka, Sabita Maharjan +5

Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Common post hoc AI explainers, such as LIME…

eess.SP2025

EEG-MSAF: An Interpretable Microstate Framework uncovers Default-Mode Decoherence in Early Neurodegeneration

Mohammad Mehedi Hasan, Pedro G. Lind, Hernando Ombao +2

Dementia (DEM) is a growing global health challenge, underscoring the need for early and accurate diagnosis. Electroencephalography (EEG) provides a non-invasive window into brain…

cs.CV2025★ 1 cited

From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Saptiotemporal Dynamics in Brain Signal Analysis

Amirabbas Hojjati, Lu Li, Ibrahim Hameed +3

EEG signals capture brain activity with high temporal and low spatial resolution, supporting applications such as neurological diagnosis, cognitive monitoring, and brain-computer i…

cond-mat.stat-mech2025

The dynamical law behind eye movements: distinguishing between Lévy and intermittent strategies

Pedro Lencastre, Yurii Bystryk, Anis Yazidi +2

Foraging is a complex spatio-temporal process which is often described with stochastic models. Two particular ones, Lévy walks (LWs) and intermittent search (IS), became popular in…

cs.AI2024★ 3 cited

DREAMS: A python framework for Training Deep Learning Models on EEG Data with Model Card Reporting for Medical Applications

Rabindra Khadka, Pedro G Lind, Anis Yazidi +1

Electroencephalography (EEG) provides a non-invasive way to observe brain activity in real time. Deep learning has enhanced EEG analysis, enabling meaningful pattern detection for…

nlin.CG2024

A Sensitivity Analysis of Cellular Automata and Heterogeneous Topology Networks: Partially-Local Cellular Automata and Homogeneous Homogeneous Random Boolean Networks

Tom Eivind Glover, Ruben Jahren, Francesco Martinuzzi +2

Elementary Cellular Automata (ECA) are a well-studied computational universe that is, despite its simple configurations, capable of impressive computational variety. Harvesting thi…