Abstract: This survey paper explores the intersection of Artificial Intelligence (AI) and Brain-Computer Interface (BCI) technologies, with a specific focus on recent advancements in thought-to-text conversion systems. These systems, powered by Electroencephalography (EEG) signals, represent a groundbreaking leap in assistive technologies, particularly for individuals with speech impediments. By decoding and interpreting imagined speech, AI-BCI systems empower users facing profound disabilities, offering a voice through silent communication. The technical intricacies involve diverse datasets and a combination of signal processing, machine learning, and deep learning techniques. Beyond understanding the technical aspects, the paper emphasizes the importance of evaluating the functional efficacy of these systems. The ultimate success lies in the positive impact on users' lives, as AI-BCI systems redefine communication paradigms and break down barriers for individuals with unique challenges. In enabling silent communication, these technologies aim to create a world where everyone can freely express themselves.

Keywords: Brain-Computer Interface (BCI), Electroencephalography (EEG), Thought-to-text conversion, Speech impairment, Assistive technology, Signal processing, Machine learning, Deep learning, Natural Language Processing (NLP)

Cite:
Dr Vijayalaxmi Mekali, Anusha Phaniraj, Kartik Bhatt, Sahithi Bhashyam, Vipul Kant Tripathi,"A Survey of Artificial Intelligence (AI) and Brain Computer Interface Techniques (BCI) for Translating Brain Signals into Text", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 10, no. 12, pp. 27-37, 2023, Crossref https://doi.org/10.17148/IARJSET.2023.101204.

| DOI: 10.17148/IARJSET.2023.101204