This comprehensive paper examines artificial intelligence techniques for decoding and representing language from brain activity, as measured by electroencephalography (EEG), a common, non-invasive, and high-temporal-resolution method for recording neural activity from the brain during language processing. This technique faces several challenges in dealing with noise and complexity. It also reviews the significant progress in analyzing this signal using traditional machine learning techniques such as SVM, RF, as well as deep learning techniques like CNN, LSTM, and Transformers. This paper also discusses the main applications of these technologies in providing communication tools for people with disabilities, medical diagnosis and treatment, understanding linguistic perception, as well as the challenges related to data quality, cost, complexity, and ethical issues. It also offers promising future insights into the integration of multiple technologies, predicting neurological and cognitive conditions, and developing advanced brain-computer interfaces, paving the way for a deeper understanding of language processing mechanisms in the brain.
Sahar Zidan (Thu,) studied this question.
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