Deep Learning – Based EEG Communication Aid For Paralyzed Patients

Electroencephalography (EEG)-based brain–computer interface (BCI) systems are increasingly used as assistive tools for individuals with severe motor impairments, enabling interaction without physical movement. However, reliable interpretation of EEG signals remains challenging due to their low signal-to-noise ratio, non-linearity, and variability across individuals. Existing approaches typically focus on single tasks such as communication, emotion recognition, or pain detection, limiting their practical applicability. This paper presents an integrated EEG-based framework that combines communication, emotion analysis, and pain severity detection within a unified system. The proposed approach employs a deep learning architecture to extract spatial and temporal features from EEG signals for effective classification of user intent and physiological states. A structured preprocessing pipeline, including filtering, normalization, and segmentation, is used to enhance signal quality. A multitask classification strategy is further adopted to improve efficiency and maintain consistent performance across tasks. The system is designed for real-time assistive applications, providing interpretable outputs for caregivers and healthcare professionals. By integrating multiple functionalities into a single model, the proposed framework improves usability, reduces system complexity, and supports the development of practical EEG-based assistive technologies.