Illustrative 1 s time-domain visualization over 11 channels based on feature-space reconstructions (blue) for an unhealthy segment (green) and a generated sample from (orange).

<p>The training target is the 88-D feature vector; time-domain plots are for qualitative context.</p>

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Bibliographic Details
Main Author:	Imad Eddine Tibermacine (22537816) (author)
Other Authors:	Samuele Russo (22488061) (author), Gianmarco Scarano (22537819) (author), Giancarlo Tedesco (22537822) (author), Abdelaziz Rabehi (22537825) (author), Amel Ali Alhussan (19704614) (author), Doaa Sami Khafaga (21463870) (author), Marwa M. Eid (11251630) (author), El-Sayed M. El-kenawy (14581088) (author), Christian Napoli (10517514) (author)
Published:	2025
Subjects:	Biochemistry Neuroscience Physiology Biological Sciences not elsewhere classified unseen test split trained cnn baselines suited feature extraction seven impaired participants offers great potential learn representations specific latent space coupled largest lyapunov exponent feature extraction technique distills distinct characteristics different health conditions diagnosing various conditions binary health label 11 scalp channels input feature vector conditional variational autoencoder cd ), kolmogorov robust eeg classification conditional input vector work could contribute div >< p proxy ), conditional vae conditional </ eeg signals eeg ). encoder input six descriptors sinai entropy rigorously evaluated results highlight personalized neurofeedback permutation entropy orthopedic impairment neurological function machine learning lle )- f4 ). extracted features extracted channel decoder enabled class labels achieve 93 93 outperforming 93 %,
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