Such distinctive 2D features pave the way to identify subjects from their EEG spectral characteristics in an unsupervised manner without any prior knowledge.
This work focuses on unsupervised subject identification using low dimensional data representation of frequency domain information lying in the EEG signals.
Oct 22, 2024 · In this work, EEG spectral features of different subjects are uniquely mapped into a 2D feature space. Such distinctive 2D features pave the ...
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EEG-based personal identification method using unsupervised feature ...
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Mar 12, 2020 · We found the variability in EEG data actually affected the personal features that were used for personal identification.
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This thesis investigates the use of electrical brain signals captured in Electroen- cephalography (EEG) as a parameter for a biometric system.
We propose the first fully-unsupervised deep learning method for seizure identification on raw EEG, using a variational autoencoder (VAE).
An unsupervised subject identification technique using EEG signals. J Birjandtalab, MB Pouyan, M Nourani. 2016 38th Annual International Conference of the IEEE ...
Mar 9, 2023 · As such, EEG, as well as other biomedical signals, can benefit enormously from modern signal-processing techniques and unsupervised learning for ...
The CNN with attention framework is also capable of identifying key time frames in the EEG signal in an unsupervised manner while the model learns to classify ...
The study presented in [33] uses an OC-SVM to extract unsupervised features of EEG signals and explore their robustness against intra-subject variability. ...