A deep learning-based brain-computer interaction system for speech and motor impairment

Abstract

Some people may experience accidents, strokes, or diseases that lead to both motor and speech disabilities, making it difficult to communicate with others. Those with paralysis face daily challenges in meeting their basic needs, particularly if they have difficulty speaking. Individuals with dysarthria, amyotrophic lateral sclerosis, and similar conditions may find it challenging to understand speech.The proposed system for automatic recognition of daily basic needs aims to improve the quality of life for individuals suffering from dysarthria and quadriplegic paralysis. The system achieves this by recognizing and analyzing brain signals and converting them to either audible voice commands or texts that can be sent to a healthcare provider's mobile phone based on the system settings.The proposed system uses a convolutional neural network (CNN) model to detect event-related potentials (ERPs) within the EEG signal to select one of six basic daily needs while displaying their images randomly. Ten volunteers participated in this study, contributing to the creation of the dataset used for training, testing, and validation. The proposed approach achieved an accuracy of 78.41%.