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Brainwave signals are read through Electroencephalogram (EEG) devices. Thesesignals are generated from an active brain based on brain activities andthoughts. The classification of brainwave signals is a challenging task due toits non-stationary nature. To address the issue, this paper proposes aConvolutional Neural Network (CNN) model to classify brainwave signals. Inorder to evaluate the performance of the proposed model a dataset is developedby recording brainwave signals for two conditions, which are visible andinvisible. In the visible mode, the human subjects focus on the color and shapepresented. Meanwhile, in the invisible mode, the subjects think about specificcolors or shapes with closed eyes. A comparison has been provided between theoriginal CNN and the proposed CNN architecture on the same dataset. The resultsshow that the proposed CNN model achieves higher classification accuracy ascompared to the standard CNN. The best accuracy rate achieved when the proposedCNN is applied on the visible color mode is 92%. In the future, improvements onthe proposed CNN will be able to classify raw EEG signals in an efficient way.

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