Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People physical disabilities need some assistive to perform their daily tasks. In these devices, higher latency factors be addressed appropriately. Therefore, main goal this research is implement a real-time BCI architecture minimum command actuation. The proposed capable communicate between different modules system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used transfer our implemented server propulsion. Think-Net Convolutional Neural Network (TN-CNN) recognize brain signals classify them into six primary mental states classification. Data collection responsibility central integrated load minimization. Testing deep learning model shows excellent results. integrity level was loss accurate commands mechanism. training testing results 99% 93% custom implementation based on TN-CNN. unit fewer errors, it will benefit working local cloud server.