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Convolutional Neural Network and Adversarial Autoencoder in EEG images classification

arXiv cs.LG / 4/7/2026

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Key Points

  • The paper applies computer-vision style neural networks to neuroscience EEG classification by converting pre-processed EEG signals into 2D EEG topograms.
  • It uses supervised and semi-supervised learning to classify different motor cortex activities related to hand movement.
  • The approach combines a convolutional neural network with an adversarial autoencoder to improve classification performance from EEG image-like representations.
  • The study targets practical challenges in EEG data analysis by leveraging 2D representations and neural-network pipelines tailored to motor cortex activity classification.

Abstract

In this paper, we consider applying computer vision algorithms for the classification problem one faces in neuroscience during EEG data analysis. Our approach is to apply a combination of computer vision and neural network methods to solve human brain activity classification problems during hand movement. We pre-processed raw EEG signals and generated 2D EEG topograms. Later, we developed supervised and semi-supervised neural networks to classify different motor cortex activities.

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