Using Brain-Computer Interface and Artificial Intelligence Algorithms for Language Learning

The research paper aims to introduce a methodology and platform that integrates a brain computer interface (BCI) and artificial intelligence (AI) to improve the process of learning a foreign language. Through the use of a BCI, our solution can accurately gauge the bioelectrical activity of the user's brain to determine whether the user has learned a piece of knowledge, allowing for a more personalized and efficient learning experience. The application's AI model, trained via back-propagation through time (BPTT) to handle temporal dependencies in the data, was optimized using the Huber loss function and the "Adam" optimizer, though it faced challenges of overtraining due to limited data. In this paper, we present a novel approach that leverages the synergy between BCIs and AI to offer a more effective and personalized language learning experience. We detail the process, from the initial design to the deployment of the platform, including the creation of a custom AI model trained on brainwave data. Our proposed data collection and cleanup process was also presented.