Classification of Brainwave Signals Based on Hybrid Deep Learning and an Evolutionary Algorithm

TL;DR

This paper introduces a hybrid deep learning approach using a CNN model optimized with an evolutionary algorithm to improve brainwave signal classification accuracy from EEG data, distinguishing between visible and invisible modes.

Contribution

The paper proposes a novel CNN architecture optimized with an evolutionary algorithm for enhanced EEG signal classification accuracy.

Findings

Proposed CNN outperforms standard CNN in accuracy.

Achieved 92% accuracy in classifying visible color mode.

Demonstrated effectiveness of hybrid deep learning for EEG analysis.

Abstract

Brainwave signals are read through Electroencephalogram (EEG) devices. These signals are generated from an active brain based on brain activities and thoughts. The classification of brainwave signals is a challenging task due to its non-stationary nature. To address the issue, this paper proposes a Convolutional Neural Network (CNN) model to classify brainwave signals. In order to evaluate the performance of the proposed model a dataset is developed by recording brainwave signals for two conditions, which are visible and invisible. In the visible mode, the human subjects focus on the color and shape presented. Meanwhile, in the invisible mode, the subjects think about specific colors or shapes with closed eyes. A comparison has been provided between the original CNN and the proposed CNN architecture on the same dataset. The results show that the proposed CNN model achieves higher classification accuracy as compared to the standard CNN. The best accuracy rate achieved when the proposed CNN is applied on the visible color mode is 92%. In the future, improvements on the proposed CNN will be able to classify raw EEG signals in an efficient way.