The differential diagnosis between Alzheimer’s disease (AD) and frontotemporal dementia (FTD) presents a significant clinical challenge due to overlapping early-stage symptom profiles. Conventional resting-state EEG provides limited sensitivity to the impaired neural plasticity and lateralized cortical degeneration frequently observed in FTD.
We developed a domain-informed heterogeneous ensemble framework incorporating dynamic neural reactivity and hemispheric asymmetry metrics from 19-channel EEG recordings acquired from 88 participants (36 AD, 23 FTD, 29 cognitively normal controls) during resting-state and photic stimulation paradigms. A neural reactivity vector (V_diff) was derived to quantify state-dependent spectral transitions. The 1,014-dimensional feature space was reduced to 200 features via recursive feature elimination, prioritizing spectral power distributions, hemispheric asymmetry indices (HAI), and stimulation-induced reactivity parameters. A weighted ensemble of Extreme Gradient Boosting (XGBoost) and Random Forest classifiers was evaluated using a subject-aware 90/10 holdout split with internal five-fold cross-validation.
The optimized model achieved a multi-class segment-level accuracy of 95.63% on an independently held-out test partition of 1,281 segments, with an internal five-fold cross-validation mean of 0.9846 ± 0.003. FTD-specific precision reached 0.9907. SHAP analysis identified beta-band hemispheric asymmetry and alpha-band reactivity as the principal contributors to class separation.
These findings indicate that the integration of dynamic state-transition measures with structural asymmetry proxies enhances electrophysiological discrimination between dementia subtypes. The framework provides a computationally efficient and biologically interpretable alternative to deep learning–based methodologies for EEG-driven dementia classification.
Fawad Muhammad, I. Usmani, M. Aamir et al.· Frontiers in Neuroinformatic...· 0 citations
Background: Performance estimates in motor-imagery electroencephalography (MI-EEG) can depend strongly on how observations are partitioned for training, model selection, and testing. Random sample- or window-level splitting may place data from the same participant in different folds and therefore does not answer the same question as evaluation on previously unseen participants. Methods: We evaluated a previously developed Gramian angular field–phase-locking value (GAF–PLV) classifier on the retained full cohort (N=105) using binary left-versus-right MI and leave-one-subject-out cross-validation (LOSO). Separately, a predefined, outcome-independent subset (N=30) was used for a matched sensitivity analysis of eight classifiers, binary and four-class tasks, and three validation strategies: random five-fold cross-validation, LOSO, and nested LOSO with subject-grouped inner model selection. Results: In the full-cohort GAF–PLV analysis, mean accuracy was 58.07% ± 8.27% and Macro-F1 was 53.48% ± 11.19%, with substantial between-subject variability. In the predefined matched subset, performance estimates and numerical model rankings changed across validation strategies. For example, the numerically highest binary-accuracy model was ShallowConvNet under random five-fold cross-validation, DeepConvNet under LOSO, and ATCNet under nested LOSO. Conclusions: Random within-cohort classification and generalisation to previously unseen subjects are distinct evaluation targets. MI-EEG reports should state the cohort, partition unit, validation design, and model-selection procedure. Rankings in the multi-model analysis are conditional on the predefined 30-subject subset and common 0.4 s input setting and are not presented as definitive full-cohort or architecture-optimal rankings.
Wenwen Chang, Hesam Akbari, Muhammad Tariq Sadiq et al.· Brain Science· 0 citations
