The prediction of cognitive tasks from electroencephalography (EEG) signals have allowed to discriminate the cognitive states emitted by the subjects and to carry out robust monitoring of cognition; a fact that is associated with the attention and performance of an individual's behavior, allowing greater control in the experiments. The objective of this work is to perform the prediction of tasks in the function of the auditory steady-state response (ASSR). Twenty-two subjects underwent three types of tasks: counting, reading and rest, accompanied by a constant stimulus. Images were obtained from the Inter Trial phase coherence (ITPC) to train classification algorithms based on convolutional neural networks (CNN) in order to separate the tasks performed by the subjects. Performance evaluation of the classification algorithm shows very good separation between count, read and rest with an AUROC of 0.95. This is significantly better than a feedforward neural network and a pre-trained convolutional deep neural network.