Brain-computer interface (BCI) decoding connects the human nervous world to the external world. People's brain signals to commands that computer devices can detect. In-depth study the performance of brain-computer interface systems has recently increased. In this article, we will systematically Investigate brain signal types for BCI and explore relevant in-depth study concepts for brain signal analysis. In this study, we have a comparison of different traditional classification algorithms new methods of in-depth study. We explore two different types Deep learning methods, i.e., traditional neural networks Architecture with Long Short term Memory and Repetitive Neural Networks. We check the classification Accuracy of Recent 5-Class Study-State Visual Evoked Opportunities dataset. The results demonstrate in-depth expertise learning methods compared to traditional taxonomy Algorithms.