This review delves into the intersection of artificial intelligence with biomedicine, emphasizing brain-inspired computing models. It discusses the evolution of machine learning and deep learning models for human-computer interaction tasks, highlighting key technologies and challenges faced in brain signal decoding.
The content covers the importance of experimental design, EEG acquisition, eye-tracking acquisition, feature extraction algorithms, classification algorithms, and the utilization of public datasets like ZuCo. It also examines recent progress in EEG-to-text tasks using deep learning techniques to decode brain signals into text or speech.
Key points include the significance of stimuli control in experiments, the role of eye-tracking data in enhancing text decoding accuracy, and advancements in EEG-to-text translation models. The review showcases how researchers are bridging the gap between brain signals and natural language representations through innovative approaches.
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