Physical and engineering sciences in medicineJul 15, 2020

Using neural networks and genetic algorithms to classify visual images

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Updated Mar 28, 2026

Abstract

A 60% success rate was achieved in classifying visual imagery of four imagined objects and a state of relaxation.

Visual imagery can serve as a communication channel in Brain-Computer Interface (BCI) systems.
Neural networks were employed to classify signals generated during visual imagery.
The technique outperformed existing methods in visual imagery classification.
The objects classified included a tree, a dog, an airplane, and a house.

Simplified

Brain-Computer Interface (BCI) systems establish a channel for direct communication between the brain and the outside world without having to use the peripheral nervous system. While most BCI systems use evoked potentials and motor imagery, in the present work we present a technique that employs visual imagery. Our technique uses neural networks to classify the signals produced in visual imagery. To this end, we have used densely connected neural and convolutional networks, together with a genetic algorithm to find the best parameters for these networks. The results we obtained are a 60% success rate in the classification of four imagined objects (a tree, a dog, an airplane and a house) plus a state of relaxation, thus outperforming the state of the art in visual imagery classification.

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Using neural networks and genetic algorithms to classify visual images

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