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.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Bihui Yu,Sib... at arxiv.org 03-11-2024
https://arxiv.org/pdf/2312.07213.pdfDeeper Inquiries