Brain-computer interface for amyotrophic lateral sclerosis patients using deep learning network

作     者：Ramakrishnan, Jayabrabu Mavaluru, Dinesh Sivasakthivel, Ramkumar Alqahtani, Abdulrahman Saad Mubarakali, Azath Retnadhas, Mervin 

作者机构：Jazan Univ Coll Comp Sci & Informat Technol Jazan Saudi Arabia Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia Kalasalingam Acad Res & Educ Sch Comp Krishnankoil India Najran Univ Coll Comp Sci & Informat Syst Najran Saudi Arabia King Khalid Univ Coll Comp Sci Abha Saudi Arabia 

出 版 物：《NEURAL COMPUTING & APPLICATIONS》 (神经网络计算与应用)

年 卷 期：2022年第34卷第16期

页      面：13439-13453页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Electrooculography Amyotrophic lateral sclerosis Convolution neural network Cross power spectral density Human-computer interface Brain-computer interface 

摘      要：Individuals with Motor Neuron Disease were unable to move from one place to another because it gradually reduced all the voluntarily movement due to the degeneration of upper and lower motors neurons. The solution to this problem was to develop rehabilitating devices using biosignals. In this study, we have designed and developed electrooculogram-based wheelchair control using Cross Power Spectral Density. The convolution neural network to verify the performance and recognition accuracy of the wheelchair navigation in the indoor environment by using four trained users and four untrained users between the different age-groups and obtained the accuracy of 91.18% and 86.88% by using four fundamental tasks. From the indoor performance, the subject S4 from trained users outperforms all the trained subjects with an average classification accuracy of 93.51%. To verify the recognition accuracy, we conducted the online performance from the online performances subject S4 from trained subjects outperforms remaining trained subjects at the same time the subject S6 from untrained subjects outperforms all the untrained subjects. From the entire study, we analyzed that classification accuracy of subjects S4 was appreciated compared to other subjects. Through the research, we confirmed that the entire trained subject s performance was maximum compared to the untrained subjects in all the circumstances.