5 papers
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…
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…
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…
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…
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 i…