Abstract

This paper discusses the problem of automatically classifying visual stimuli (animate and inanimate objects filtered at high and low spatial frequencies) using an observer’s electroencephalogram. Classical machine-learning methods (a support-vector machine that employs, among other things, wavelet attributes) and convolutional and recurrent deep-learning neural networks were used for the classification. The recognition accuracy was analyzed as a function of the selected classification methods, the placement of the electrodes, the time intervals, and the problem to be solved. The results show that the classification accuracy is 79% for sharp/smeared images, 61% for animate/inanimate objects, and 50% for classifying four classes of images.

© 2018 Optical Society of America

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