BIO‐inspired fuzzy inference system—For physiological signal analysis  

作　　者：Ravi Suppiah Noori Kim Khalid Abidi Anurag Sharma 

机构地区：[1]Electrical and Electronic Engineering,Newcastle University upon Tyne,Newcastle upon Tyne,UK [2]School of Engineering Technology,Purdue University,West Lafayette,Indiana,USA [3]Electrical Power Engineering,Newcastle University in Singapore,Singapore,Singapore

出　　处：《IET Cyber-Systems and Robotics》2023年第3期24-36,共13页智能系统与机器人（英文）

摘　　要：When a person's neuromuscular system is affected by an injury or disease,Activities‐for‐Daily‐Living(ADL),such as gripping,turning,and walking,are impaired.Electroen-cephalography(EEG)and Electromyography(EMG)are physiological signals generated by a body during neuromuscular activities embedding the intentions of the subject,and they are used in Brain–Computer Interface(BCI)or robotic rehabilitation systems.However,existing BCI or robotic rehabilitation systems use signal classification technique limitations such as(1)missing temporal correlation of the EEG and EMG signals in the entire window and(2)overlooking the interrelationship between different sensors in the system.Furthermore,typical existing systems are designed to operate based on the presence of dominant physiological signals associated with certain actions;(3)their effectiveness will be greatly reduced if subjects are disabled in generating the dominant signals.A novel classification model,named BIOFIS is proposed,which fuses signals from different sensors to generate inter‐channel and intra‐channel relationships.It ex-plores the temporal correlation of the signals within a timeframe via a Long Short‐Term Memory(LSTM)block.The proposed architecture is able to classify the various subsets of a full‐range arm movement that performs actions such as forward,grip and raise,lower and release,and reverse.The system can achieve 98.6%accuracy for a 4‐way action using EEG data and 97.18%accuracy using EMG data.Moreover,even without the dominant signal,the accuracy scores were 90.1%for the EEG data and 85.2%for the EMG data.The proposed mechanism shows promise in the design of EEG/EMG‐based use in the medical device and rehabilitation industries.

关 键 词：artificial intelligence bio‐inspired robotics brain‐computer interface deep learning embedded system FUZZY 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]