BIO启发的模糊推理系统——用于生理信号分析

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS
Ravi Suppiah, Noori Kim, Khalid Abidi, Anurag Sharma
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引用次数: 0

摘要

当一个人的神经肌肉系统受到伤害或疾病的影响时,日常生活活动(ADL),如握紧、转身和行走,都会受到损害。脑电图(EEG)和肌电图(EMG)是身体在嵌入受试者意图的神经肌肉活动中产生的生理信号,它们被用于脑机接口(BCI)或机器人康复系统。然而,现有的脑机接口或机器人康复系统使用信号分类技术的局限性,如:(1)缺少整个窗口内脑电图和肌电信号的时间相关性;(2)忽略了系统中不同传感器之间的相互关系。此外,典型的现有系统被设计为基于与某些动作相关的显性生理信号的存在而运行;(3)如果被试不能产生主导信号,其有效性将大大降低。提出了一种新的分类模型BIOFIS,该模型融合来自不同传感器的信号来生成通道间和通道内的关系。它通过长短期记忆(LSTM)块探索时间框架内信号的时间相关性。所提出的架构能够对全范围手臂运动的各种子集进行分类,这些动作包括向前、抓握和举起、降低和释放以及反转。该系统使用脑电数据对四向动作的准确率可达98.6%,使用肌电数据对四向动作的准确率可达97.18%。此外,即使没有主导信号,脑电数据的准确率得分为90.1%,肌电数据的准确率得分为85.2%。所提出的机制在基于脑电图/肌电图的医疗设备和康复行业的应用设计中显示出前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

BIO-inspired fuzzy inference system—For physiological signal analysis

BIO-inspired fuzzy inference system—For physiological signal analysis

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. Electroencephalography (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 explores 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.

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来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
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