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Related Experiment Video

Updated: Nov 12, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Learning Invariant Representations from EEG via Adversarial Inference.

Ozan Özdenizci¹, Y E Wang², Toshiaki Koike-Akino²

¹Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA.

IEEE Access : Practical Innovations, Open Solutions

|March 22, 2021

Summary

This summary is machine-generated.

This study introduces adversarial inference to make electroencephalogram (EEG) decoding models invariant to user differences. This improves generalizability of deep learning models for brain-computer interfaces.

Keywords:

adversarial learning brain-computer interface deep neural networks electroencephalogram invariant representation motor imagery

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Area of Science:

Neuroscience
Machine Learning
Biomedical Engineering

Background:

Deep neural networks are emerging as EEG feature extractors.
Transfer learning in EEG models often assumes inherent subject/session invariance.
Achieving robust, generalizable EEG decoding across users remains a challenge.

Purpose of the Study:

To systematically improve the invariance of deep learning frameworks for EEG analysis during model training.
To develop representations invariant to inter-subject variability in EEG data.
To enhance the generalizability of EEG decoding models.

Main Methods:

Proposed an adversarial inference approach for learning invariant representations.
Utilized a publicly available motor imagery EEG dataset.
Applied state-of-the-art convolutional neural network (CNN) models within the adversarial framework.

Main Results:

Demonstrated successful cross-subject model transfer scenarios.
Presented neurophysiological interpretations of the learned network representations.
Showcased the effectiveness of adversarial inference in enhancing EEG model generalizability.

Conclusions:

Adversarial inference offers a systemic approach to achieve invariance in EEG deep learning.
The method enhances the transferability of decoding models across different subjects.
This work provides valuable insights for deep learning applications in EEG analysis.