EEG-to-text decoding technologies offer a promising direct communication channel for individuals with severe motor disabilities, though significant challenges remain in signal acquisition, inter-subject variability, and model accuracy.
The conversion of brain activity into text using electroencephalography (EEG) has gained significant traction in recent years. Many researchers are working to develop new models to decode EEG signals into text form. Although this area has shown promising developments, it still faces numerous challenges that necessitate further improvement. It is important to outline this area's recent developments and future research directions to provide a comprehensive understanding of the current state of technology, guide future research efforts, and enhance the effectiveness and accessibility of EEG-to-text systems. In this review article, we thoroughly summarize the progress in EEG-to-text conversion. First, we talk about how EEG-to-text technology has grown and what problems the field still faces. Second, we discuss existing techniques used in this field. This includes methods for collecting EEG data, the steps to process these signals, and the development of systems capable of translating these signals into coherent text. We conclude with potential future research directions, emphasizing the need for enhanced accuracy, reduced system constraints, and the exploration of novel applications across varied sectors. By addressing these aspects, this review aims to contribute to developing more accessible and effective brain–computer interface (BCI) technology for a broader user base.
Murad et al. (Tue,) conducted a review in Speech or motor disabilities. EEG-to-text decoding was evaluated. EEG-to-text decoding technologies offer a promising direct communication channel for individuals with severe motor disabilities, though significant challenges remain in signal acquisition, inter-subject variability, and model accuracy.