X to Text Typing Interface

X-to-text (X2T) interfaces aim to translate diverse user inputs, such as eye gaze, brain activity, or even handwriting, into typed text, enabling communication for individuals with motor impairments or in specialized contexts. Current research focuses on improving accuracy and speed through adaptive algorithms, leveraging transformer language models and Bayesian methods to predict user intent and learn from user feedback (like backspaces), often incorporating online learning techniques. This field is significant for its potential to revolutionize assistive technologies and human-computer interaction, offering more accessible and efficient communication for a wide range of users.