Conditional VAE for personalized neurofeedback in cognitive training

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, we explore the use o...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:PloS one Ročník 20; číslo 10; s. e0335364
Hlavní autori: Tibermacine, Imad Eddine, Russo, Samuele, Scarano, Gianmarco, Tedesco, Giancarlo, Rabehi, Abdelaziz, Alhussan, Amel Ali, Khafaga, Doaa Sami, Eid, Marwa M., El-kenawy, El-Sayed M., Napoli, Christian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 31.10.2025
Public Library of Science (PLoS)
Predmet:
Accuracy
Adult
Algorithms
Analysis
Biofeedback training
Brain research
Coders
Cognition & reasoning
Cognition - physiology
Cognitive Training
Datasets
Deep learning
EEG
Electroencephalography
Electroencephalography - methods
Entropy
Feature extraction
Feedback
Female
Fourier transforms
Fractals
Health aspects
Health care
Humans
Labels
Liapunov exponents
Machine Learning
Male
Methods
Middle Aged
Neural networks
Neurofeedback - methods
Neurological disorders
Neurophysiology
Orthopedics
Permutations
Physiology
Precision Medicine
Italy
ISSN:1932-6203, 1932-6203
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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, we explore the use of a Conditional Variational Autoencoder (CVAE) that injects a binary health label (healthy or orthopedic impairment) into both the encoder input and the latent space coupled with the extracted features, leveraging the conditional input vector to learn representations specific to different health conditions. Our study involved using two public OpenNeuro datasets [1,2]. From the healthy dataset we randomly selected seven subjects to match the seven impaired participants; in both sets we retained the same 11 scalp channels (C3, Cz, C4, FC3, FCz, FC4, CP3, CPz, CP4, F3, F4). Six descriptors-Short time Fourier Transform (STFT), Hurst Exponent (HE), Detrended Fluctuation Analysis (DFA), Correlation Dimension (CD), Kolmogorov-Sinai Entropy (permutation entropy; KS-proxy), and the Largest Lyapunov Exponent (LLE)-are extracted channel-wise and concatenated to form the input feature vector, which distills distinct characteristics from the EEG signals. We rigorously evaluated the performance of our CVAE model in combination with each feature extraction technique. The conditional supply of class labels to both encoder and decoder enabled the CVAE to achieve 93% accuracy on the unseen test split of the dataset with precision of 93%, a recall of 93%, and an F1-score of 0.93 outperforming re-trained CNN baselines. These results highlight the promise of CVAEs and the significance of well-suited feature extraction for robust EEG classification. This work could contribute to the development of automated healthcare diagnostic tools.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0335364