ArticleIEEE access : practical innovations, open solutions2020
Learning Invariant Representations from EEG via Adversarial Inference.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 97 citations in OpenAlex.
- 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
- Transfer learning for EEG-based BCIs: a comparative evaluation and optimization of data alignment methods.Frontiers in systems neuroscience · 2026Article
- Representation Learning and Foundation Models for Electroencephalography Analyses: Current trends, fundamental insights, and future directions?IEEE signal processing magazine · 2025Article
- Improved BCI calibration in multimodal emotion recognition using heterogeneous adversarial transfer learning.PeerJ. Computer science · 2025Article
- Identifying relevant EEG channels for subject-independent emotion recognition using attention network layers.Frontiers in psychiatry · 2025Article
- Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study.JMIR mHealth and uHealth · 2024Observational
- Mental Workload Classification and Tasks Detection in Multitasking: Deep Learning Insights from EEG Study.Brain sciences · 2024Article
- EEG-driven automatic generation of emotive music based on transformer.Frontiers in neurorobotics · 2024Article
- Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study.JMIR mHealth and uHealth · 2023Observational
- Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces.Frontiers in computational neuroscience · 2023Article
- Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain-computer interface.Scientific reports · 2022Article
- Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control.Frontiers in human neuroscience · 2022Review
- Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.Brain computer interfaces (Abingdon, England) · 2022Article
- Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks.Information sciences · 2021Article
- Universal Physiological Representation Learning With Soft-Disentangled Rateless Autoencoders.IEEE journal of biomedical and health informatics · 2021Article
- EEG-based texture roughness classification in active tactile exploration with invariant representation learning networks.Biomedical signal processing and control · 2021Article
- A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces.Frontiers in human neuroscience · 2021Review
- Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.Sensors (Basel, Switzerland) · 2020Review
- Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction.IEEE signal processing letters · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Discovering and exploiting shared, invariant neural activity in electroencephalogram (EEG) based classification tasks is of significant interest for generalizability of decoding models across subjects or EEG recording sessions. While deep neural networks are recently emerging as generic EEG feature extractors, this transfer learning aspect usually relies on the prior assumption that deep networks naturally behave as subject- (or session-) invariant EEG feature extractors. We propose a further step towards invariance of EEG deep learning frameworks in a systemic way during model training. We introduce an adversarial inference approach to learn representations that are invariant to inter-subject variabilities within a discriminative setting. We perform experimental studies using a publicly available motor imagery EEG dataset, and state-of-the-art convolutional neural network based EEG decoding models within the proposed adversarial learning framework. We present our results in cross-subject model transfer scenarios, demonstrate neurophysiological interpretations of the learned networks, and discuss potential insights offered by adversarial inference to the growing field of deep learning for EEG.
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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.