AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This study presents a deep learning framework that leverages EEG-derived time–frequency images and convolutional neural networks (CNNs) for robust, non-invasive classification of dementia subtypes, and consistently outperforms all baseline methods across accuracy, precision, recall, and F1-score.
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...
The findings suggest that deep learning-driven EEG analysis may be a valuable tool for noninvasive and scalable cognitive health assessments.
An EEG-based Schizophrenia classification framework is proposed that transforms preprocessed EEG recordings into time-frequency representations using the Short-Time Fourier Transform and generated spectrogram images are classified using both conventional Machine Learning algorithms and Deep Learning models.
This survey provides a comprehensive synthesis of EEG-based dementia studies published between 2020 and 2025, with a primary focus on Alzheimer’s disease, frontotemporal dementia (FTD), mild cognitive impairment (MCI), and related dementia disorders.
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...
Resting-state electroencephalography (EEG) can capture the slowing of neural oscillations associated with Alzheimer’s disease (AD), but many machine-learning studies remain difficult to inspect, reproduce, or test. This study developed an interpretable, subject-level AD versus healthy-control classifier from the datase...
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