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

作     者：Ali Usman Javed Ferzund Ahmad Shaf Muhammad Aamir Samar Alqhtani Khlood M.Mehdar Hanan Talal Halawani Hassan A.Alshamrani Abdullah A.Asiri Muhammad Irfan 

作者机构：Department of Computer ScienceCOMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information SystemsCollege of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy DepartmentMedicine CollegeNajran UniversityNajran61441Saudi Arabia Computer Science DepartmentCollege of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Radiological Sciences DepartmentCollege of Applied Medical Sciences and Information SystemsNajran UniversityNajran61441Kingdom of Saudi Arabia Electrical Engineering DepartmentCollege of EngineeringNajran UniversityNajran61441Saudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第8期

页      面：1355-1369页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0402[教育学-心理学(可授教育学、理学学位）] 0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education Kingdom of Saudi Arabia for funding this research through a project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University Kingdom of Saudi Arabia 

主　　题：Disable person electroencephalogram convolutional neural network brain signal classification 

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