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Research on Brain-Controlled Robotic Arm Based on Improved Faster-RCNN Target Detection Model

Authors:

Yunzhe LiAuthors Info & Claims

ICCPR '21: Proceedings of the 2021 10th International Conference on Computing and Pattern Recognition

Pages 136 - 141

https://doi.org/10.1145/3497623.3497645

Published: 04 February 2022 Publication History

Abstract

Aiming at the problem of poor target detection in brain-controlled system, which leads to limited applications, this paper proposes a brain-controlled robotic arm system based on an improved Faster-RCNN target detection model. Firstly, the dataset is obtained through data enhancement for improve the generalization ability of the model, meanwhile, the Faster-RCNN model is improved, which increases the recognition accuracy by 4.38% compared with the previous improvement. Then, the improved model is used to detect the type and position of targets in real time, and the Filter Bank Canonical Correlation Analysis (FBCCA) algorithm is used to extract the features of the Steady-State Visual Evoked Potential (SSVEP) paradigm Electroencephalogram (EEG) signal to control the robot arm to grasp the desired object. Finally, 12 subjects were recruited to test the system. The test results showed that the designed brain-controlled robotic arm grasping system based on the improved Faster-RCNN target detection had a grasping average accuracy of 92.33%, which meet the practical application requirements of medical auxiliary equipment, medical rehabilitation and other fields.

References

[1]

WOLPAW J R, WOLPAW E W. Brain-computer interfaces: Principles and practice[M]. 2012.

[2]

Burns A, Adeli H, Buford J A. Brain-computer interface after nervous system injury[J]. Neuroscientist, 2014, 20(6): 639-651.

[3]

Yahud S, Abu O. Prosthetic Hand for the Brain-computer Interface System[A]. IFMBE Proceedings, 2007(15): 643-646.

[4]

Zhang H, Dong E, Zhu L. Brain-controlled wheelchair system based on SSVEP[C], Chinese Automation Congress, 2020: 2108-2112

[5]

Iturrate I, Antelis J. Non-invasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation[J]. IEEE transactions on Robotics, 2009, 25(3): 614-627.

[6]

Farwell L A, Donchin E. Talking off the Top of Your Head: Toward a Mental Prosthesis Utilizing Event-Related brain Potentials[J]. Electroencephalography and Clinical Neurophysiology, 1988, 70(6) : 510-523.

[7]

Chen X, Wang Y, Nakanishi M, High−speed spelling with a noninvasive brain-computer interface[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(44) : E6058−E6067.

[8]

Allison BZ, Brunner C, Altstter C, A hybrid ERD/SSVEP BCI for continuous simultaneous two dimensional cursor control[J]. Journal of Neuroscience Methods, 2012, 209(2) : 299−307.

[9]

Chen X, Zhao B, Wang Y, et al. Control of a 7−DOF robotic arm system with an SSVEP-based BCI[J]. International Journal of Neural Systems, 2018, 28(8) : 1850018.

[10]

Hortal E, Ianez E, Ubeda A, . Combining a brain−machine interface and an electrooculography interface to perform pick and place tasks with a robotic arm [J]. Robotics and Autonomous Systems, 2015, 72: 181−188.

Digital Library

[11]

Xu Y. Research on Robotic Arm Shared Control Based on Brain-Computer Interface and Machine Vision [D]. Shanghai Jiaotong University, 2019.

[12]

LENC K, VEDALDI A RCNN minus R[J].Computer Science, 2015, 26(2) : 23 − 28

[13]

REN S, HE K, GIRSHICK R, Faster − RCNN: towards real time object detection with region proposal networks[C]. International Conference on Neural Information Processing Systems. MIT Press, 2015: 91 − 99.

[14]

REDMON J, DIVVALA S, GIRSHICK R, You only look once: unified, real-time object detection[C]. Computer Vision and Pattern Recognition. IEEE, 2016: 779 − 788.

[15]

LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multiBox detector[M]. Computer Vision − ECCV 2016. Springer International Publishing, 2016: 21 − 37.

[16]

JIA C, GAO X, HPNG B, Frequency and phase mixed coding in SSVEP-based brain-computer interface[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(1) : 200-206.

[17]

VIALATTE F B, MAURICE M, DAUWELS J, Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives{J}. Progress in Neurobiology, 2010, 90(4) : 418-438.

[18]

Ren S, He K, Girshick R, Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 39(6): 1137-1149.

Digital Library

[19]

Bin G, Gao X, Yan Z, An online multi−channel SSVEP based brain−computer interface using a canonical correlation analysis method[J]. Journal of Neural Engineering, 2009, 6 : 046002.

[20]

Chu Y, Zhao X, Zou Y A Comparative Study of Different Feature Extraction Methods for Motor Imagery EEG Decoding within the Same Upper Extremity[C]. Chinese Automation Congress, CAC 2018: 330-335

Cited By

Angulo Medina AAguilar Bonilla MRodríguez Giraldo IMontenegro Palacios JCáceres Gutiérrez DLiscano Y(2024)Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013–2023)Sensors10.3390/s2422712524:22(7125)Online publication date: 6-Nov-2024
https://doi.org/10.3390/s24227125

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Research on Brain-Controlled Robotic Arm Based on Improved Faster-RCNN Target Detection Model

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ICCPR '21: Proceedings of the 2021 10th International Conference on Computing and Pattern Recognition

October 2021

393 pages

ISBN:9781450390439

DOI:10.1145/3497623

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 04 February 2022

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National Equipment Pre-research Foundation
Major scientific and technological innovation projects of Shan Dong Province
National Natural Science Foundation of China

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ICCPR '21

ICCPR '21: 2021 10th International Conference on Computing and Pattern Recognition

October 15 - 17, 2021

Shanghai, China

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Cited By

Angulo Medina AAguilar Bonilla MRodríguez Giraldo IMontenegro Palacios JCáceres Gutiérrez DLiscano Y(2024)Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013–2023)Sensors10.3390/s2422712524:22(7125)Online publication date: 6-Nov-2024
https://doi.org/10.3390/s24227125

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