A self-supervised masked autoencoder pre-training on multiple public EEG datasets demonstrates that TF-JointMAE effectively improves the robustness of EEG representations, providing potential auxiliary support for AD-spectrum classification and clinical decision-making.
Existing EEG-based methods have been constrained by limited availability of labeled data, hand-crafted features, poor spatio-temporal modeling, sub-optimal cross-hardware performance, and lack of interpretability due to being expensive and intrusive. To address these limitations, this study introduces innovative neural...
Syeda Shamaila Zareen, Nada Alzaben, Usman Ahmad et al.· Frontiers in Neuroinformatic...· 0 citations
It is demonstrated that deep latent representations extract clinically relevant signatures from noisy signals, enabling precise, rapid, and data-efficient diagnosis.
The proposed intelligent EEG diagnostic framework shows potential for deployment in primary healthcare institutions and may provide theoretical support for addressing the growing challenges of AD diagnosis and treatment in the context of global population aging.
The proposed DBTF-Net leverages temporal and time-frequency information in EEG signals and provides classification of AD and FTD, and visualization analysis indicates that the model attends to disease-relevant discriminative patterns in time-frequency representations, enhancing the interpretability of its classificatio...
Xiao-Li Yang, Xiao Li, Chen-Chen Wang et al.· Journal of Alzheimer's Disea...· 0 citations
A domain-informed heterogeneous ensemble framework incorporating dynamic neural reactivity and hemispheric asymmetry metrics from 19-channel EEG recordings acquired from 88 participants indicates that the integration of dynamic state-transition measures with structural asymmetry proxies enhances electrophysiological di...
Fawad Muhammad, I. Usmani, M. Aamir et al.· Frontiers in Neuroinformatic...· 0 citations
Delta2Gamma, a self-supervised framework that learns EEG representations from unlabeled data by contrasting augmented views of each signal by decomposing every recording into the five canonical neural rhythms, separates Alzheimer's disease from cognitively normal controls with 92.4\% accuracy.
Chanwoo Park, Chanwoo Kim· 0 citations
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