cs.LGApr 5, 2026

Convolutional Neural Network and Adversarial Autoencoder in EEG images classification

Authors: Albert Nasybullin, Semen Kurkin

Organizations: Faculty of Computer Science and Engineering, Innopolis University, Innopolis, Russia · Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

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.

Figures & tables

Fig. 1: Left hand-related EEG topography in Mu frequency band.
Fig. 2: Right hand-related EEG topography in Mu frequency band.
Fig. 3: The CNN’s accuracy is satisfying. The network reaches accuracy over 90 %\% even on small-scale data.
Fig. 4: The Confusion Matrix of Adversarial Autoencoder. 1 = class ”left hand”, 0 = class ”right hand”.
Fig. 5: Original Image after the augmentation is on a left. Generated Image is on a right. Step 0.
Fig. 6: Original Image after the augmentation is on a left. Generated Image is on a right. Step 150.
More in this topic

EEG Decoding

Electroencephalography (EEG) decoding maps brain-signal patterns to mental states, stimuli, speech, or intended actions.

See all 208 papers

Explore similar work

CardsList

  1. How Long short-term memory artificial neural network, synthetic data, and fine-tuning improve the classification of raw EEG data

    Apr 5, 2026Albert Nasybullin, Vladimir Maksimenko, Semen KurkinEEG DecodingTime Series Classification

    Laboratory of Data Analysis and Bioinformatics, Center for Artificial Intelligence, Innopolis University, Innopolis, Russia · Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia

  2. EEG Decoding Using CNN and LSTM Network

    Aug 13, 2026Athanasios KaragounisCross-Subject EEG DecodingMI Classification

    School of Science, Department of Digital Industry Technologies GR34400, Psachna, Greece

  3. Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets

    May 5, 2026Laurits Dixen, Stefan Heinrich, Paolo BurelliEfficient Neural Network InferenceMI Classification

    IT University of Copenhagen Denmark