Tinnitus is attributed by the perception of a sound without any physical source causing the symptom. Symptom profiles of tinnitus patients are characterized by a large heterogeneity, which is a major obstacle in developing general treatments for this chronic disorder. As tinnitus patients often report severe constraints in their daily life, the lack of general treatments constitutes such a challenge that patients crave for any kind of promising method to cope with their tinnitus, even if it is not based on evidence. Another drawback constitutes the lack of objective measurements to determine the individual symptoms of patients. Many data sources are therefore investigated to learn more about the heterogeneity of tinnitus patients in order to develop methods to measure the individual situation of patients more objectively. As research assumes that tinnitus is caused by processes in the brain, electroencephalography (EEG) data are heavily investigated by researchers. Following this, we address the question whether EEG data can be used to classify tinnitus using a deep neural network. For this purpose, we analyzed 16,780 raw EEG samples from 42 subjects (divided into tinnitus patients and control group), with a duration of one second per sample. Four different procedures (with or without noise reduction and down-sampling or up-sampling) for automated preprocessing were used and compared. Subsequently, a neural network was trained to classify whether a sample refers to a tinnitus patient or the control group. We obtain a maximum accuracy in the test set of 75.6% using noise reduction and down-sampling. Our findings highlight the potential of deep learning approaches to detect EEG patterns for tinnitus patients as they are difficult to be recognized by humans.