EEG-to-Text Communication Using Deep Learning and Generative AI

The system segments the complex task of emotional interpretation into distinct processing stages: EEG signal acquisition, preprocessing and noise removal, deep learning-based feature extraction and classification, and natural language generation. This segmentation allows each component to be optimized independently, achieving high measurement precision through specialized algorithms while managing overall system complexity through modular architecture.

The patent introduces intermediate processing layers between raw EEG data and final interpretation results. A deep learning model acts as an intermediary to transform complex neural signals into meaningful emotional states, and a natural language generation system serves as another intermediary to convert these states into comprehensible text. These intermediaries bridge the gap between raw data and actionable insights, enhancing measurement precision without exposing the full complexity to end users.

The system performs preliminary actions by continuously preprocessing EEG data in real-time, including noise removal, artifact rejection, and feature extraction, before emotional analysis is actually needed. This allows the deep learning model to receive pre-processed, ready-to-analyze data, reducing computational burden during critical decision moments and enabling faster response times without excessive resource consumption during peak analysis periods.

The patent implements periodic processing cycles where EEG data is collected over specific time windows, processed through the deep learning model at regular intervals, and updated continuously. This periodic approach balances real-time responsiveness with computational efficiency, allowing the system to maintain accurate emotional state tracking while managing energy consumption through structured, interval-based processing rather than continuous full-scale analysis.