Per-channel features and dimensionalities (per 1 s window).

Per-channel features and dimensionalities (per 1 s window).

<p>Per-channel features and dimensionalities (per 1 s window).</p>

محفوظ في:
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 %,
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