ArticleIEEE signal processing letters2019
Adversarial Deep Learning in EEG Biometrics.
Article in IEEE signal processing letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions.Frontiers in human neuroscience · 2024Pooled it
- Extraction of brainprint by means of autoencoder with attention mechanism.Frontiers in computational neuroscience · 2026Article
- EEG-Based Personal Identification by Special Design Domain-Adaptive Autoencoder.Sensors (Basel, Switzerland) · 2025Article
- Threats and Mitigation Strategies for Electroencephalography-Based Person Authentication.International journal of telemedicine and applications · 2025Review
- Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number.Translational psychiatry · 2024Article
- KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals.Sensors (Basel, Switzerland) · 2023Article
- Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results.Sensors (Basel, Switzerland) · 2023Article
- A Personalized User Authentication System Based on EEG Signals.Sensors (Basel, Switzerland) · 2022Article
- An investigation of privacy preservation in deep learning-based eye-tracking.Biomedical engineering online · 2022Article
- EEG temporal-spatial transformer for person identification.Scientific reports · 2022Article
- Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey.Sensors (Basel, Switzerland) · 2022Review
- Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain-computer interface.Scientific reports · 2022Article
- On the Minimal Amount of EEG Data Required for Learning Distinctive Human Features for Task-Dependent Biometric Applications.Frontiers in neuroinformatics · 2022Article
- [Review on identity feature extraction methods based on electroencephalogram signals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Review
- EEG-based texture roughness classification in active tactile exploration with invariant representation learning networks.Biomedical signal processing and control · 2021Article
- The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG.Frontiers in human neuroscience · 2021Article
- Regularization of Deep Neural Networks for EEG Seizure Detection to Mitigate Overfitting.Proceedings : Annual International Computer Software and Applications Conference. COMPSAC · 2020Article
- Learning Invariant Representations from EEG via Adversarial Inference.IEEE access : practical innovations, open solutions · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG. Furthermore, recent methods have mostly trained and evaluated based on single session EEG data. We address this problem from an invariant representation learning perspective. We propose an adversarial inference approach to extend such deep learning models to learn session-invariant person-discriminative representations that can provide robustness in terms of longitudinal usability. Using adversarial learning within a deep convolutional network, we empirically assess and show improvements with our approach based on longitudinally collected EEG data for person identification from half-second EEG epochs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.