The article reviews the fourth Brain-Computer Interface (BCI) competition, focusing on the datasets and challenges presented. The competition aimed to advance BCI technology, particularly for motor and sensorimotor applications, and to explore new applications such as enhancing human performance and assessing subconscious perception. The relevance of past competitions is highlighted, including the impact on scientific citations and the development of effective methods like Common Spatial Pattern (CSP) analysis. The role of open data in fostering interdisciplinary research and the importance of proper data handling are also discussed. The article provides detailed descriptions of the datasets, including their motivation, materials, experimental protocols, and evaluation criteria. It concludes with an overview of the competition outcomes and a discussion on future directions. Key datasets include asynchronous motor imagery, continuous multi-class motor imagery, and MEG signals, each posing unique challenges and contributing to the advancement of BCI technology.The article reviews the fourth Brain-Computer Interface (BCI) competition, focusing on the datasets and challenges presented. The competition aimed to advance BCI technology, particularly for motor and sensorimotor applications, and to explore new applications such as enhancing human performance and assessing subconscious perception. The relevance of past competitions is highlighted, including the impact on scientific citations and the development of effective methods like Common Spatial Pattern (CSP) analysis. The role of open data in fostering interdisciplinary research and the importance of proper data handling are also discussed. The article provides detailed descriptions of the datasets, including their motivation, materials, experimental protocols, and evaluation criteria. It concludes with an overview of the competition outcomes and a discussion on future directions. Key datasets include asynchronous motor imagery, continuous multi-class motor imagery, and MEG signals, each posing unique challenges and contributing to the advancement of BCI technology.