A shared robot control system combining augmented reality and motor imagery brain-computer interfaces with eye tracking

被引:1
作者
Dillen, Arnau [1 ,2 ,3 ]
Omidi, Mohsen [3 ,4 ]
Ghaffari, Fakhreddine [2 ]
Vanderborght, Bram [3 ,4 ]
Roelands, Bart [1 ,3 ]
Romain, Olivier [2 ]
Nowe, Ann [5 ]
De Pauw, Kevin [1 ,3 ]
机构
[1] Vrije Univ Brussel, Human Physiol & Sports Physiotherapy Res Grp, B-1050 Brussels, Belgium
[2] CY Cergy Paris Univ, Ecole Natl Super lElectron & Applicat ENSEA, Ctr Natl Rech Sci CNRS, Equipes Traitement Informat & Syst, F-95000 Cergy, France
[3] Vrije Univ Brussel, Brussels Human Robot Res Ctr BruBot, B-1050 Brussels, Belgium
[4] imec, B-1050 Brussels, Belgium
[5] Vrije Univ Brussel, Artificial Intelligence Res Grp, B-1050 Brussels, Belgium
关键词
shared robot control; brain-computer interface; augmented reality; motor imagery; electroencephalogram; user evaluation; eye tracking;
D O I
10.1088/1741-2552/ad7f8d
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Objective. Brain-computer interface (BCI) control systems monitor neural activity to detect the user's intentions, enabling device control through mental imagery. Despite their potential, decoding neural activity in real-world conditions poses significant challenges, making BCIs currently impractical compared to traditional interaction methods. This study introduces a novel motor imagery (MI) BCI control strategy for operating a physically assistive robotic arm, addressing the difficulties of MI decoding from electroencephalogram (EEG) signals, which are inherently non-stationary and vary across individuals. Approach. A proof-of-concept BCI control system was developed using commercially available hardware, integrating MI with eye tracking in an augmented reality (AR) user interface to facilitate a shared control approach. This system proposes actions based on the user's gaze, enabling selection through imagined movements. A user study was conducted to evaluate the system's usability, focusing on its effectiveness and efficiency. Main results. Participants performed tasks that simulated everyday activities with the robotic arm, demonstrating the shared control system's feasibility and practicality in real-world scenarios. Despite low online decoding performance (mean accuracy: 0.52 9, F1: 0.29, Cohen's Kappa: 0.12), participants achieved a mean success rate of 0.83 in the final phase of the user study when given 15 min to complete the evaluation tasks. The success rate dropped below 0.5 when a 5 min cutoff time was selected. Significance. These results indicate that integrating AR and eye tracking can significantly enhance the usability of BCI systems, despite the complexities of MI-EEG decoding. While efficiency is still low, the effectiveness of our approach was verified. This suggests that BCI systems have the potential to become a viable interaction modality for everyday applications in the future.
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页数:13
相关论文
共 44 条
[1]  
Allspaw J., 2023, Comparing Performance Between Different Implementations of ROS for Unity
[2]  
Breivold HP, 2007, EUROMICRO CONF PROC, P13
[3]  
Chitta S, 2016, STUD COMPUT INTELL, V625, P3
[4]   The neurophysiological basis of motor imagery [J].
Decety, J .
BEHAVIOURAL BRAIN RESEARCH, 1996, 77 (1-2) :45-52
[5]   Optimal sensor set for decoding motor imagery from EEG [J].
Dillen, Arnau ;
Ghaffari, Fakhreddine ;
Romain, Olivier ;
Vanderborght, Bram ;
Meeusen, Romain ;
Roelands, Bart ;
De Pauw, Kevin .
2023 11TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, NER, 2023,
[6]   Evaluating the Microsoft HoloLens through an augmented reality assembly application [J].
Evans, Gabriel ;
Miller, Jack ;
Pena, Mariangely Iglesias ;
MacAllister, Anastacia ;
Winer, Eliot .
DEGRADED ENVIRONMENTS: SENSING, PROCESSING, AND DISPLAY 2017, 2017, 10197
[7]   MEG and EEG data analysis with MNE-Python']Python [J].
Gramfort, Alexandre ;
Luessi, Martin ;
Larson, Eric ;
Engemann, Denis A. ;
Strohmeier, Daniel ;
Brodbeck, Christian ;
Goj, Roman ;
Jas, Mainak ;
Brooks, Teon ;
Parkkonen, Lauri ;
Haemaelaeinen, Matti .
FRONTIERS IN NEUROSCIENCE, 2013, 7
[8]   The Franka Emika Robot A Reference Platform for Robotics Research and Education [J].
Haddadin, Sami ;
Parusel, Sven ;
Johannsmeier, Lars ;
Golz, Saskia ;
Gabl, Simon ;
Walch, Florian ;
Sabaghian, Mohamadreza ;
Jaehne, Christoph ;
Hausperger, Lukas ;
Haddadin, Simon .
IEEE ROBOTICS & AUTOMATION MAGAZINE, 2022, 29 (02) :46-64
[9]   AutoML: A survey of the state-of-the-art [J].
He, Xin ;
Zhao, Kaiyong ;
Chu, Xiaowen .
KNOWLEDGE-BASED SYSTEMS, 2021, 212
[10]   A Review on Machine Learning for EEG Signal Processing in Bioengineering [J].
Hosseini, Mohammad-Parsa ;
Hosseini, Amin ;
Ahi, Kiarash .
IEEE REVIEWS IN BIOMEDICAL ENGINEERING, 2021, 14 :204-218