This study presents a novel deep learning approach for EEG signal classification using a residual architecture, utilizing the EEG Bonn dataset. The EEG Bonn dataset is a well-known and widely used dataset in the field of EEG research, containing signals from a diverse range of neurological conditions. This robust dataset enabled the training and validation of the proposed model across various conditions, thereby contributing to its remarkable performance metrics, including state-of-the-art validation accuracy, precision, recall, and F1-score. The model’s robustness, as demonstrated by its consistent performance across different scenarios within the EEG Bonn dataset, signifies its potential for real-world applications such as seizure detection and brain-computer interfaces. A comparative analysis between the performance of the proposed model and previous studies on the same dataset underscores its superior accuracy and robustness. The results contribute to the growing body of evidence supporting the use of deep learning techniques in EEG signal analysis, using well-established datasets like EEG Bonn. They also suggest that the residual architecture could be beneficial in other signal classification tasks. The implications of the findings are discussed, emphasizing the potential of the model in advancing our understanding of the brain and developing new diagnostic and therapeutic tools for neurological disorders. Recommendations for future research include evaluating the model on larger and more diverse datasets, exploring different architectural modifications, optimizing the model for real-time applications, and investigating its interpretability. In conclusion, this study, which successfully employs the EEG Bonn dataset, represents a significant advancement in the field of EEG signal classification and opens up many exciting opportunities for future research.