Encoder with input concatenation .

<div><p>Machine learning (ML) offers great potential in healthcare, especially in the analysis of complex physiological signals like electroencephalography (EEG). EEG recordings hold valuable insights into neurological function and can aid in diagnosing various conditions. In this work,...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Imad Eddine Tibermacine (22537816) (author)
مؤلفون آخرون:	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)
منشور في:	2025
الموضوعات:	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 %,
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Encoder with input concatenation .

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