Aim: Obsessive Compulsive Disorder (OCD) is a common psychiatric disorder that usually begins in adolescence. The fact that it is frequently seen together with other psychiatric disorders, its symptoms overlap with different mental illnesses, and the diagnosis is primarily based on clinical interviews and psychometric scales makes it difficult to diagnose obsessive-compulsive disorder. In this context, it is aimed to contribute to the objective diagnostic processes of OCD with biomarker and artificial intelligencesupported approaches. Materials and Methods: In this study, individuals diagnosed with OCD were classified from healthy individuals using two different hybrid deep learning models: Gated Recurrent Unit (GRU) and Transformer Encoder (TE) with one-dimensional convolutional neural networks (1DCNN), respectively. Results: In the 1DCNN-TE model, false negatives (11) and false positives (1) remain at low levels, while in the 1DCNN-GRU model, these values are 30 and 95, respectively. While the training and test accuracy of the 1DCNN-TE model is over 95%, the accuracy of the 1DCNN-GRU model has reached over 90%. While the training and test losses tend to decrease in both models, the fluctuations in the test loss are more pronounced in the 1DCNN-TE model. Conclusion: The results indicate that both deep learning models could classify OCD with high accuracy based on EEG signals and successfully learn discriminative features. However, the fluctuations observed in the test data and errors in detecting the control group have indicated limitations regarding the models’ generalizability and reliability on new data.