  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.
