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

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 ofbrainwave 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 brainwavesignals. 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 thevisible 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. Acomparison has been provided between the original CNN and the proposed CNN architecture on the same dataset. The results show that the proposed CNN model achieveshigher 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.

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

“…The use or not use of preprocessing before hDL-based BCI is still under debate, since the performance obtained is not clearly in favor of one of the two. For example, some papers [ 10 , 11 , 57 ] obtained good performance, 98.81%, 95.33% and 92%, respectively, even though they did not use any preprocessing step. However, Jeong and colleagues and Saidutta and colleagues [ 26 , 58 ], using automated and advanced preprocessing, reached a performance of 87% and 81%, respectively.…”

“…And the output of the filter is fed into an Age-Layered Population Structure (ALPS) Genetic Algorithm (GA). The results compared with five CNN-based algorithms resulting in 92% accuracy [ 57 ].…”

Brain Waves Signal Modeling for Object Classification Using Random Forest Method

“…Thus, based on the EEG signals, the study suggests the connection with the supervised methods for automatic classification of patients for supporting the medical analysis of dementia. As a connection between thinking about and seeing a shape, (K. Rostam et al, 2019) conducted a model on convolutional neutral network (CNN) to categorize a brainwave signal. The aim was to assess the activity of the suggested model.…”