Rabindra Khadka

Rabindra Khadka, Pedro G Lind, Anis Yazidi et al.

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 clinical and research purposes. However, most existing frameworks for EEG data analysis are either focused on preprocessing techniques or deep learning model development, often overlooking the crucial need for structured documentation and model interpretability. In this paper, we introduce DREAMS (Deep REport for AI ModelS), a Python-based framework designed to generate automated model cards for deep learning models applied to EEG data. Unlike generic model reporting tools, DREAMS is specifically tailored for EEG-based deep learning applications, incorporating domain-specific metadata, preprocessing details, performance metrics, and uncertainty quantification. The framework seamlessly integrates with deep learning pipelines, providing structured YAML-based documentation. We evaluate DREAMS through two case studies: an EEG emotion classification task using the FACED dataset and a abnormal EEG classification task using the Temple Univeristy Hospital (TUH) Abnormal dataset. These evaluations demonstrate how the generated model card enhances transparency by documenting model performance, dataset biases, and interpretability limitations. Unlike existing model documentation approaches, DREAMS provides visualized performance metrics, dataset alignment details, and model uncertainty estimations, making it a valuable tool for researchers and clinicians working with EEG-based AI. The source code for DREAMS is open-source, facilitating broad adoption in healthcare AI, research, and ethical AI development.

Mohammad Mehedi Hasan, Pedro G. Lind, Hernando Ombao et al.

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 activity, but conventional methods struggle to capture its transient complexity. We present the \textbf{EEG Microstate Analysis Framework (EEG-MSAF)}, an end-to-end pipeline that leverages EEG microstates discrete, quasi-stable topographies to identify DEM-related biomarkers and distinguish DEM, mild cognitive impairment (MCI), and normal cognition (NC). EEG-MSAF comprises three stages: (1) automated microstate feature extraction, (2) classification with machine learning (ML), and (3) feature ranking using Shapley Additive Explanations (SHAP) to highlight key biomarkers. We evaluate on two EEG datasets: the public Chung-Ang University EEG (CAUEEG) dataset and a clinical cohort from Thessaloniki Hospital. Our framework demonstrates strong performance and generalizability. On CAUEEG, EEG-MSAF-SVM achieves \textbf{89\% $\pm$ 0.01 accuracy}, surpassing the deep learning baseline CEEDNET by \textbf{19.3\%}. On the Thessaloniki dataset, it reaches \textbf{95\% $\pm$ 0.01 accuracy}, comparable to EEGConvNeXt. SHAP analysis identifies mean correlation and occurrence as the most informative metrics: disruption of microstate C (salience/attention network) dominates DEM prediction, while microstate F, a novel default-mode pattern, emerges as a key early biomarker for both MCI and DEM. By combining accuracy, generalizability, and interpretability, EEG-MSAF advances EEG-based dementia diagnosis and sheds light on brain dynamics across the cognitive spectrum.