Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation

被引：0

作者：

Lakshminarayanan, Kishor ^{[1
]}

Ramu, Vadivelan ^{[1
]}

Shah, Rakshit ^{[2
]}

Sunny, Md Samiul Haque ^{[3
]}

Madathil, Deepa ^{[4
]}

Brahmi, Brahim ^{[5
]}

Wang, Inga ^{[6
]}

Fareh, Raouf ^{[7
]}

Rahman, Mohammad Habibur ^{[3
]}

机构：

[1] Department of Sensors and Biomedical Tech, School of Electronics Engineering, Vellore Institute of Technology University, Tamil Nadu, Vellore

[2] Department of Orthopaedic Surgery, University of Arizona, Tucson, AZ

[3] Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI

[4] Jindal Institute of Behavioural Sciences, O.P. Jindal Global University, Haryana

[5] Electrical Engineering, Collège Ahuntsic, Montreal, QC

[6] Department of Occupational Science & Technology, University of Wisconsin-Milwaukee, Milwaukee, WI

[7] Department of Electrical and Computer Engineering, University of Sharjah, Sharjah

来源：

PeerJ Computer Science | 2024年 / 10卷

关键词：

Brain-computer interface; EEG; Motor imagery; Rehabilitation;

D O I：

10.7717/PEERJ-CS.2174

中图分类号：

学科分类号：

摘要：

Background. The current study explores the integration of a motor imagery (MI)- based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients. Methods. We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured via a gel-free cap, processed through various stages including signal verification, training, and testing. The training involved MI tasks with concurrent vibrotactile stimulation, utilizing common spatial pattern (CSP) training and linear discriminant analysis (LDA) for signal classification. The testing stage introduced a real-time feedback system and a virtual game environment where participants controlled a virtual iTbot robot. Results. Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals. Discussion. The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions. © 2024 Lakshminarayanan et al.

引用

共 42 条

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