Brain Waves-to-Text Neural Translation using Deep Learning and Natural Language Processing

Brain–Computer Interface (BCI) technology enables communication between the human brain and external computing devices without the use of traditional speech or physical interaction. One important application of this technology is converting brain signals into readable text, which can assist individuals who are unable to speak due to severe neurological disorders or physical disabilities.This research proposes a Brain Waves-to-Text neural translation system using deep learning and Natural Language Processing techniques. The system analyses Electroencephalography (EEG) signals captured from the brain and converts them into meaningful textual outputs. In the proposed approach, EEG signals are first preprocessed to remove noise and unwanted artifacts. A hybrid deep learning architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is used to extract spatial and temporal patterns from the brain signals. The CNN layers are used to learn spatial relationships between EEG channels, while the LSTM network captures the sequential patterns present in neural signals. Experimental analysis demonstrates that the proposed framework can successfully classify brain activity patterns and convert them into understandable text representations.