Unsupervised Hybrid Deep Feature Encoder for Robust Feature Learning from Resting-State EEG Data

EEG classification is a challenging task due to the nonstationary nature of EEG data and the covariance shift induced by cross-subject variance. Recently, various machine learning and deep learning models have been developed to learn robust features for inter-subject EEG classification tasks. Howeve...

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Bibliographic Details
Published in2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2024; pp. 1 - 5
Main Authors Yue, Yuan, Deng, Jeremiah D., Chakraborti, Tapabrata, De Ridder, Dirk, Manning, Patrick
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2024
Subjects
Accuracy
Algorithms
Benchmark testing
Biological system modeling
Brain - physiology
Brain modeling
Cluster Analysis
Data models
Deep Learning
Electroencephalography
Electroencephalography - methods
Engineering in medicine and biology
Feature Encoder
Feature extraction
Feature Selection
Humans
Machine Learning
Representation learning
Rest - physiology
Resting-state EEG classification
Signal Processing, Computer-Assisted
Unsupervised Machine Learning
Variational Autoencoder
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Summary:EEG classification is a challenging task due to the nonstationary nature of EEG data and the covariance shift induced by cross-subject variance. Recently, various machine learning and deep learning models have been developed to learn robust features for inter-subject EEG classification tasks. However, current existing models are designed based on active task-related EEG, with a lack of investigation into learning robust feature representation from resting-state EEG data. Given the differences in the nature of brain activities captured by resting-state and active task-related EEG, existing models might not be applicable to resting-state EEG. This study proposed an unsupervised hybrid deep feature encoder to learn robust feature representation in resting-state EEG data. It involves using a Variational Autoencoder (VAE) to learn latent feature representation, followed by a further feature selection conducted through a non-task-related sample-level proximity classification using K-means clustering. We demonstrate the efficiency of our proposed model through significantly improved classification accuracies compared to benchmark models, as well as the high between-subject separability manifested by the learned feature representation.
ISSN:2694-0604
DOI:10.1109/EMBC53108.2024.10781741