[Direct Brain-controlled Multi-robot Cooperation Task]

Overview

Journal Sheng Wu Yi Xue Gong Cheng Xue Za Zhi

Specialty Biomedical Engineering

Date 2018 Dec 25

PMID 30583321

Citations 1

Authors

Chao Zhang

Xin Xiong

Hongjin Ren

Yunfa Fu

Affiliations

Soon will be listed here.

Abstract

Brain control is a new control method. The traditional brain-controlled robot is mainly used to control a single robot to accomplish a specific task. However, the brain-controlled multi-robot cooperation (MRC) task is a new topic to be studied. This paper presents an experimental research which received the "Innovation Creative Award" in the brain-computer interface (BCI) brain-controlled robot contest at the World Robot Contest. Two effective brain switches were set: total control brain switch and transfer switch, and BCI based steady-state visual evoked potentials (SSVEP) was adopted to navigate a humanoid robot and a mechanical arm to complete the cooperation task. Control test of 10 subjects showed that the excellent SSVEP-BCI can be used to achieve the MRC task by appropriately setting up the brain switches. This study is expected to provide inspiration for the future practical brain-controlled MRC task system.

Citing Articles

Augmented-reality based brain-computer interface of robot control.

Hu J Heliyon. 2024; 10(5):e26255.

PMID: 38449664 PMC: 10915352. DOI: 10.1016/j.heliyon.2024.e26255.

References

Blokland Y, Spyrou L, Thijssen D, Eijsvogels T, Colier W, Floor-Westerdijk M . Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia. IEEE Trans Neural Syst Rehabil Eng. 2014; 22(2):222-9. DOI: 10.1109/TNSRE.2013.2292995. View

Sengelmann M, Engel A, Maye A . Maximizing Information Transfer in SSVEP-Based Brain-Computer Interfaces. IEEE Trans Biomed Eng. 2017; 64(2):381-394. DOI: 10.1109/TBME.2016.2559527. View

Pan J, Li Y, Zhang R, Gu Z, Li F . Discrimination between control and idle states in asynchronous SSVEP-based brain switches: a pseudo-key-based approach. IEEE Trans Neural Syst Rehabil Eng. 2013; 21(3):435-43. DOI: 10.1109/TNSRE.2013.2253801. View

Lim J, Kim Y, Lee J, An K, Hwang H, Cha H . An emergency call system for patients in locked-in state using an SSVEP-based brain switch. Psychophysiology. 2017; 54(11):1632-1643. DOI: 10.1111/psyp.12916. View

Li S, Xiong X, Fu Y . [Control of intelligent car based on electroencephalogram and neurofeedback]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018; 35(1):15-24. PMC: 10307539. DOI: 10.7507/1001-5515.201612080. View

Zhao X, Zhao D, Wang X, Hou X . A SSVEP Stimuli Encoding Method Using Trinary Frequency-Shift Keying Encoded SSVEP (TFSK-SSVEP). Front Hum Neurosci. 2017; 11:278. PMC: 5454068. DOI: 10.3389/fnhum.2017.00278. View

Punsawad Y, Wongsawat Y . A multi-command SSVEP-based BCI system based on single flickering frequency half-field steady-state visual stimulation. Med Biol Eng Comput. 2016; 55(6):965-977. DOI: 10.1007/s11517-016-1560-3. View

Chang M, Lee J, Heo J, Park K . Eliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI. J Neurosci Methods. 2015; 258:104-13. DOI: 10.1016/j.jneumeth.2015.11.001. View

Jin J, Allison B, Sellers E, Brunner C, Horki P, Wang X . Optimized stimulus presentation patterns for an event-related potential EEG-based brain-computer interface. Med Biol Eng Comput. 2010; 49(2):181-91. DOI: 10.1007/s11517-010-0689-8. View

10.

McFarland D, Vaughan T . BCI in practice. Prog Brain Res. 2016; 228:389-404. DOI: 10.1016/bs.pbr.2016.06.005. View

11.

Bin G, Gao X, Yan Z, Hong B, Gao S . An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. J Neural Eng. 2009; 6(4):046002. DOI: 10.1088/1741-2560/6/4/046002. View

12.

He S, Zhang R, Wang Q, Chen Y, Yang T, Feng Z . A P300-Based Threshold-Free Brain Switch and Its Application in Wheelchair Control. IEEE Trans Neural Syst Rehabil Eng. 2016; 25(6):715-725. DOI: 10.1109/TNSRE.2016.2591012. View

13.

Spyrou L, Blokland Y, Farquhar J, Bruhn J . Optimal Multitrial Prediction Combination and Subject-Specific Adaptation for Minimal Training Brain Switch Designs. IEEE Trans Neural Syst Rehabil Eng. 2015; 24(6):700-9. DOI: 10.1109/TNSRE.2015.2494378. View

14.

Wang Y, Chen X, Gao X, Gao S . A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng. 2016; 25(10):1746-1752. DOI: 10.1109/TNSRE.2016.2627556. View

15.

Cao L, Ju Z, Li J, Jian R, Jiang C . Sequence detection analysis based on canonical correlation for steady-state visual evoked potential brain computer interfaces. J Neurosci Methods. 2015; 253:10-7. DOI: 10.1016/j.jneumeth.2015.05.014. View

16.

Li S, Fu Y, Yang Q, Liu C, Wun H . [Pretreatment Research Based on Left and Right Hand Motor Imagery for Single-channel Electroencephalogram]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018; 33(5):862-6. View

17.

Merino L, Nayak T, Hall G, Pack D, Huang Y . Detection of control or idle state with a likelihood ratio test in asynchronous SSVEP-based brain-computer interface systems. Annu Int Conf IEEE Eng Med Biol Soc. 2017; 2016:1568-1571. DOI: 10.1109/EMBC.2016.7591011. View

18.

Wu Z, Yao D . [Comparison of steady-state visually evoked potential evoked by different monochromatic light]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2008; 25(5):1021-4. View

19.

Zhang Y, Zhou G, Jin J, Wang X, Cichocki A . SSVEP recognition using common feature analysis in brain-computer interface. J Neurosci Methods. 2014; 244:8-15. DOI: 10.1016/j.jneumeth.2014.03.012. View