Combining Robotic Vision with Non-Invasive Brain-Computer Interfaces Development and Future Directions
DOI:
https://doi.org/10.54097/62nyvg37Keywords:
Brain-computer interface (BCI), robotic vison, robotic arm.Abstract
The profound growth in brain-computer interface (BCI) technology has brought forth novel interdisciplinary applications, particularly in the realm of robotics. This review delves into the challenges of using non-invasive BCIs for precise robotic arm operations, with an emphasis on the transformative potential of integrating BCI with machine vision. Based on some problems in BCI applications, this paper emphasizes the use of machine vision combined with BCI signals to help non-invasive brain-computer interfaces improve the correctness and efficiency. Through detailed analysis, it is demonstrated how computer vision complements BCI, ameliorating issues like inherent noise and decoding inefficiencies associated with non-invasive BCIs. Moreover, the discussion touches upon the enhanced user experience offered by this integrated approach, and the potential it holds for addressing the compatibility challenges in BCIs. Although the combination of the two technologies can solve mutual problems, there are also certain limitations. For example, two different data may be more complex, so there are higher requirements for algorithms and processing methods. In addition, the development of a combination of two devices The system will have higher overhead, making it impossible for the average person to afford the cost of using it at the same time。As BCI technology continues its upward trajectory, this synthesis with robotic vision heralds a new era of harmonized human-machine interactions.
Downloads
References
H. Kwon, C. Hwang and S. Jo, "Vision Combined with MI-Based BCI in Soft Robotic Glove Control," 2022 10th International Winter Conference on Brain-Computer Interface (BCI), Gangwon-do, Korea, Republic of, 2022, 1-5.
S. Du, F. Wang, G. Zhou, J. Li, L. Yang and D. Wang, "Vision-Based Robotic Manipulation of Intelligent Wheelchair with Human-Computer Shared Control," 2021 33rd Chinese Control and Decision Conference (CCDC), Kunming, China, 2021, 3252-3257.
A. J. Abougarair, H. M. Gnan, A. Oun and S. O. Elwarshfani, "Implementation of a Brain-Computer Interface for Robotic Arm Control," 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, Tripoli, Libya, 2021, 58-63.
Y. Xu, H. Zhang, L. Cao, X. Shu and D. Zhang, "A Shared Control Strategy for Reach and Grasp of Multiple Objects Using Robot Vision and Noninvasive Brain–Computer Interface," in IEEE Transactions on Automation Science and Engineering, 2022, 19(1), 360-372.
H. Cheng, M. Wang, C. Ma and C. Yu, "A Review of Brain Information Processing for Robot Control," 2022 7th International Conference on Image, Vision and Computing (ICIVC), Xi’an, China, 2022, 866-871.
E. A. Pohlmeyer, D. C. Jangraw, J. Wang, S. -F. Chang and P. Sajda, "Combining computer and human vision into a BCI: Can the whole be greater than the sum of its parts?," 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, 2010, 138-141.
E. Lavely, G. Meltzner and R. Thompson, "Integrating human and computer vision with EEG toward the control of a prosthetic arm," 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Boston, MA, USA, 2012, 179-180.
X. Chen, B. Zhao and X. Gao, "Noninvasive Brain-computer Interface Based High-level Control of a Robotic Arm for Pick and Place Tasks," 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Huangshan, China, 2018, 1193-1197.
Y. Zhou, Z. Lu and Y. Li, "Robot control with multitasking of brain-computer interface," 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, Singapore, 2022, 155-160.
H. Li and H. Guo, "Design of Bionic Robotic Hand Gesture Recognition System Based on Machine Vision," 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA), Changchun, China, 2022, 960-964.
Z. Zhang and H. Shang, "Low-cost Solution for Vision-based Robotic Grasping," 2021 International Conference on Networking Systems of AI (INSAI), Shanghai, China, 2021, 54-61.
J. Wang, H. Xu and Z. Chen, "Vision-Based Conveyor Belt Workpiece Grabbing Using the SCARA Robotic Arm," 2022 International Conference on Machine Learning, Control, and Robotics (MLCR), Suzhou, China, 2022, 172-176.
A. Shahzad, X. Gao, A. Yasin, K. Javed and S. M. Anwar, "A Vision-Based Path Planning and Object Tracking Framework for 6-DOF Robotic Manipulator," in IEEE Access, 2020, 8, 203158-203167.
S. Ma, T. Tang, H. You, Y. Zhao, X. Ma and J. Wang, "An Robotic Arm System for Automatic Welding of Bars Based on Image Denoising," 2021 3rd International Conference on Robotics and Computer Vision (ICRCV), Beijing, China, 2021, 40-44.
A. Wong, Y. Wu, S. Abbasi, S. Nair, Y. Chen and M. J. Shafiee, "Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge," 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, BC, Canada, 2023, 2293-2297.
S. Torres Alvarado, M. G. Borja Borja and K. Borja Torres, "Object Distance Estimation from a Binocular Vision System for Robotic Applications Using Artificial Neural Networks," 2022 10th International Conference on Control, Mechatronics and Automation (ICCMA), Belval, Luxembourg, 2022, 19-23.
T. J. Son, A. H. Abu Hassan and M. H. Jairan, "Optimized Robot Mapping and Obstacle Avoidance using Stereo Vision," 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia, 2021, 279-284.
J. Ai, J. Meng, X. Mai and X. Zhu, "BCI Control of a Robotic Arm Based on SSVEP With Moving Stimuli for Reach and Grasp Tasks," in IEEE Journal of Biomedical and Health Informatics, 2023, 27(8), 3818-3829.
X. Chen, B. Zhao and X. Gao, "Noninvasive Brain-computer Interface Based High-level Control of a Robotic Arm for Pick and Place Tasks," 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Huangshan, China, 2018, 1193-1197.
S. Jo and J. W. Choi, "Effective motor imagery training with visual feedback for non-invasive brain computer interface," 2018 6th International Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South), 2018, 1-4.
S. N. Rao, S. B. Prapulla, G. Shobha, S. Hariprasad, M. Gupta and S. A. Reddy, "Using virtual reality to boost the effectiveness of brain-computer interface applications," 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), Bengaluru, India, 2019, 1-5.
X. Zhang et al., "Brain Computer Interface for the Hand Function Restoration," 2021 9th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea (South), 2021, 1-6.
J. -H. Jeong, J. -H. Cho, B. -H. Lee and S. -W. Lee, "Real-Time Deep Neurolinguistic Learning Enhances Noninvasive Neural Language Decoding for Brain–Machine Interaction," in IEEE Transactions on Cybernetics, 2022.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







