  The ability to perceive and recognize objects is fundamental for the
interaction with the external environment. Studies that investigate them and
their relationship with brain activity changes have been increasing due to the
possible application in an intuitive brain-machine interface (BMI). In
addition, the distinctive patterns when presenting different visual stimuli
that make data differentiable enough to be classified have been studied.
However, reported classification accuracy still low or employed techniques for
obtaining brain signals are impractical to use in real environments. In this
study, we aim to decode electroencephalography (EEG) signals depending on the
provided visual stimulus. Subjects were presented with 72 photographs belonging
to 6 different semantic categories. We classified 6 categories and 72 exemplars
according to visual stimuli using EEG signals. In order to achieve a high
classification accuracy, we proposed an attention driven convolutional neural
network and compared our results with conventional methods used for classifying
EEG signals. We reported an accuracy of 50.37% and 26.75% for 6-class and
72-class, respectively. These results statistically outperformed other
conventional methods. This was possible because of the application of the
attention network using human visual pathways. Our findings showed that EEG
signals are possible to differentiate when subjects are presented with visual
stimulus of different semantic categories and at an exemplar-level with a high
classification accuracy; this demonstrates its viability to be applied it in a
real-world BMI.
