Fetching the paper…
Reading the bibliography…
This paper explores the feasibility of employing EEG-based intention detection for real-time robot assistive control.
J. A. Pineda, B. Z. Allison, and A. Vankov, “The effects of self-movement, observation, and imagination on/spl mu/rhythms and readiness potentials (rp’s): toward a brain-computer interface (bci),” IEEE Transactions on Rehabilitation Engineering , vol. 8, no. 2, pp. 219–222, 2000
2000
Earlier work this paper cites.
D. Kulić and E. A. Croft, “Estimating intent for human-robot interaction,” Advanced Robotics , 2003. [Online]. Available: https://api.semanticscholar.org/CorpusID:1120056
2003
Earlier work this paper cites.
J. R. Millan and J. Mouriño, “Asynchronous bci and local neural classifiers: an overview of the adaptive brain interface project,” IEEE transactions on neural systems and rehabilitation engineering , vol. 11, no. 2, pp. 159–161, 2003
2003
Earlier work this paper cites.
Y. Wang, S. Gao, and X. Gao, “Common spatial pattern method for channel selelction in motor imagery based brain-computer interface,” in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference , 2005, pp. 5392–5395
2005
Earlier work this paper cites.
Y. Wang, S. Gao, and X. Gao, “Common spatial pattern method for channel selelction in motor imagery based brain-computer interface,” 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference , pp. 5392–5395, 2005. [Online]. Available: https://api.semanticscholar.org/CorpusID:20814659
2005
Earlier work this paper cites.
H. M. Lakany and B. A. Conway, “Understanding intention of movement from electroencephalograms,” Expert Systems , vol. 24, 2007. [Online]. Available: https://api.semanticscholar.org/CorpusID:5936372
2007
Earlier work this paper cites.
T. Kirschstein and R. Köhling, “What is the source of the eeg?” Clinical EEG and neuroscience , vol. 40, no. 3, pp. 146–149, 2009
2009
Earlier work this paper cites.
M. B. Khalid, N. I. Rao, I. Rizwan-i Haque, S. Munir, and F. Tahir, “Towards a brain computer interface using wavelet transform with averaged and time segmented adapted wavelets,” in 2009 2nd International Conference on Computer, Control and Communication , 2009, pp. 1–4
2009
Earlier work this paper cites.
A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, “Multiclass brain–computer interface classification by riemannian geometry,” IEEE Transactions on Biomedical Engineering , vol. 59, pp. 920–928, 2012. [Online]. Available: https://api.semanticscholar.org/CorpusID:423006
2012
Earlier work this paper cites.
——, “Classification of covariance matrices using a riemannian-based kernel for bci applications,” Neurocomputing , vol. 112, pp. 172–178, 2013. [Online]. Available: https://api.semanticscholar.org/CorpusID:13873072
2013
Earlier work this paper cites.
T. Li, J. Hong, J. Zhang, and F. Guo, “Brain–machine interface control of a manipulator using small-world neural network and shared control strategy,” Journal of neuroscience methods , vol. 224, pp. 26–38, 2014
2014
Earlier work this paper cites.
J. Meng, S. Zhang, A. Bekyo, J. Olsoe, B. Baxter, and B. He, “Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks,” Scientific Reports , vol. 6, no. 1, p. 38565, Dec. 2016. [Online]. Available: https://doi.org/10.1038/srep38565
2016
Earlier work this paper cites.
D. A. Andreou and R. Poli, “Comparing eeg, its time-derivative and their joint use as features in a bci for 2-d pointer control,” 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) , pp. 5853–5856, 2016. [Online]. Available: https://api.semanticscholar.org/CorpusID:172677
2016
Earlier work this paper cites.
J. Andreu-Perez, F. Cao, H. Hagras, and G.-Z. Yang, “A self-adaptive online brain–machine interface of a humanoid robot through a general type-2 fuzzy inference system,” IEEE Transactions on Fuzzy Systems , vol. 26, no. 1, pp. 101–116, 2016
2016
Earlier work this paper cites.
Z. Tang, S. Sun, S. Zhang, Y. Chen, C. Li, and S. Chen, “A brain-machine interface based on erd/ers for an upper-limb exoskeleton control,” Sensors , vol. 16, no. 12, p. 2050, 2016
2016
Earlier work this paper cites.
Y. Athavale and S. Krishnan, “Biosignal monitoring using wearables: Observations and opportunities,” Biomedical Signal Processing and Control , vol. 38, pp. 22–33, 2017. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1746809417300617
2017
Earlier work this paper cites.
F. Yger, M. Berar, and F. Lotte, “Riemannian approaches in brain-computer interfaces: A review,” IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 25, no. 10, pp. 1753–1762, 2017
2017
Earlier work this paper cites.
M. Congedo, A. Barachant, and R. Bhatia, “Riemannian geometry for eeg-based brain-computer interfaces; a primer and a review,” 2017. [Online]. Available: https://api.semanticscholar.org/CorpusID:13857467
2017
Cited alongside, same era.
S. Bhattacharyya, A. Konar, and D. Tibarewala, “Motor imagery and error related potential induced position control of a robotic arm,” IEEE/CAA Journal of Automatica Sinica , vol. 4, no. 4, pp. 639–650, 2017
2017
Cited alongside, same era.
D. Liu, W. Chen, Z. Pei, and J. Wang, “A brain-controlled lower-limb exoskeleton for human gait training,” Review of Scientific Instruments , vol. 88, no. 10, 2017
2017
Cited alongside, same era.
G. Kucukyildiz, H. Ocak, S. Karakaya, and O. Sayli, “Design and implementation of a multi sensor based brain computer interface for a robotic wheelchair,” Journal of Intelligent & Robotic Systems , vol. 87, pp. 247–263, 2017
2017
Cited alongside, same era.
M. Menceloglu, M. Grabowecky, and S. Suzuki, “Spectral-power associations reflect amplitude modulation and within-frequency interactions on the sub-second timescale and cross-frequency interactions on the seconds timescale,” PLoS ONE , vol. 15, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:214419358
2020
Later among the works it cites.
E. Travers, N. Khalighinejad, A. Schurger, and P. Haggard, “Do readiness potentials happen all the time?” NeuroImage , vol. 206, p. 116286, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1053811919308778
2020
Later among the works it cites.
X. Tang, W. Li, X. Li, W. Ma, and X. Dang, “Motor imagery eeg recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network,” Expert Systems with Applications , vol. 149, p. 113285, 2020
2020
Later among the works it cites.
J. Choi, K. T. Kim, J. H. Jeong, L. Kim, S. J. Lee, and H. Kim, “Developing a motor imagery-based real-time asynchronous hybrid bci controller for a lower-limb exoskeleton,” Sensors , vol. 20, no. 24, p. 7309, 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Sciutti, M. Mara, V. Tagliasco, and G. Sandini, “Humanizing human-robot interaction: On the importance of mutual understanding,” IEEE Technology and Society Magazine , vol. 37, pp. 22–29, 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:3812272
2018
Cited alongside, same era.
L. Junwei, S. Ramkumar, G. Emayavaramban, M. Thilagaraj, V. Muneeswaran, M. P. Rajasekaran, V. Venkataraman, A. F. Hussein et al. , “Brain computer interface for neurodegenerative person using electroencephalogram,” IEEE Access , vol. 7, pp. 2439–2452, 2018
2018
Cited alongside, same era.
C. H. Nguyen and P. K. Artemiadis, “Eeg feature descriptors and discriminant analysis under riemannian manifold perspective,” Neurocomputing , vol. 275, pp. 1871–1883, 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:894293
2018
Cited alongside, same era.
B. Schölkopf, A. J. Smola, and F. Bach, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond . The MIT Press, 2018
2018
Cited alongside, same era.
W. Wen, R. Minohara, S. Hamasaki, T. Maeda, Q. An, Y. Tamura, H. Yamakawa, A. Yamashita, and H. Asama, “The readiness potential reflects the reliability of action consequence,” Scientific Reports , vol. 8, 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:51940807
2018
Cited alongside, same era.
P. L. C. Rodrigues, C. Jutten, and M. Congedo, “Riemannian procrustes analysis: Transfer learning for brain–computer interfaces,” IEEE Transactions on Biomedical Engineering , vol. 66, pp. 2390–2401, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:58642888
2019
Cited alongside, same era.
C. M. Michel and D. Brunet, “Eeg source imaging: A practical review of the analysis steps,” Frontiers in Neurology , vol. 10, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:93003798
2019
Cited alongside, same era.
L. Bi, A. G. Feleke, and C. Guan, “A review on emg-based motor intention prediction of continuous human upper limb motion for human-robot collaboration,” Biomedical Signal Processing and Control , vol. 51, pp. 113–127, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1746809419300473
2019
Cited alongside, same era.
2020
Later among the works it cites.
F. P. Kalaganis, N. A. Laskaris, E. Chatzilari, S. Nikolopoulos, and I. Kompatsiaris, “A riemannian geometry approach to reduced and discriminative covariance estimation in brain computer interfaces,” IEEE Transactions on Biomedical Engineering , vol. 67, pp. 245–255, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:122544415
2020
Later among the works it cites.
D. Esposito, J. Centracchio, E. Andreozzi, G. D. Gargiulo, G. R. Naik, and P. Bifulco, “Biosignal-Based Human-Machine interfaces for assistance and rehabilitation: A survey,” Sensors (Basel) , vol. 21, no. 20, Oct. 2021
2021
Later among the works it cites.
A. Schurger, P. B. Hu, J. Pak, and A. L. Roskies, “What is the readiness potential?” Trends in Cognitive Sciences , vol. 25, no. 7, pp. 558–570, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1364661321000930
2021
Later among the works it cites.
A. Schurger, P. Hu, J. Pak, and A. L. Roskies, “What is the readiness potential?” Trends in cognitive sciences , vol. 25, pp. 558 – 570, 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:233471654
2021
Later among the works it cites.
P. Batres-Mendoza, E. I. Guerra-Hernandez, A. Espinal, E. Pérez-Careta, and H. Rostro-Gonzalez, “Biologically-inspired legged robot locomotion controlled with a bci by means of cognitive monitoring,” IEEE Access , vol. 9, pp. 35 766–35 777, 2021
2021
Later among the works it cites.
K. Värbu, M. Naveed, and Y. Muhammad, “Past, present, and future of eeg-based bci applications,” Sensors (Basel, Switzerland) , vol. 22, 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:248775078
2022
Later among the works it cites.
A. Dillen, D. Steckelmacher, K. Efthymiadis, K. Langlois, A. De Beir, U. Marusic, B. Vanderborght, A. Nowé, R. Meeusen, F. Ghaffari, O. Romain, and K. De Pauw, “Deep learning for biosignal control: insights from basic to real-time methods with recommendations,” J Neural Eng , vol. 19, no. 1, Feb. 2022
2022
Later among the works it cites.
J. Ying, Q. Wei, and X. Zhou, “Riemannian geometry-based transfer learning for reducing training time in c-VEP BCIs,” Scientific Reports , vol. 12, no. 1, p. 9818, Jun. 2022. [Online]. Available: https://doi.org/10.1038/s41598-022-14026-y
2022
Later among the works it cites.
G. Chen, H. S. Helm, K. Lytvynets, W. Yang, and C. E. Priebe, “Mental state classification using multi-graph features,” Frontiers in Human Neuroscience , vol. 16, 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:247187688
2022
Later among the works it cites.
C. Tang, Z. Xu, E. Occhipinti, W. Yi, M. Xu, S. Kumar, G. S. Virk, S. Gao, and L. G. Occhipinti, “From brain to movement: Wearables-based motion intention prediction across the human nervous system,” Nano Energy , vol. 115, p. 108712, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2211285523005499
2023
Later among the works it cites.
R. Zhang, S. Lee, M. Hwang, A. Hiranaka, C. Wang, W. Ai, J. J. R. Tan, S. Gupta, Y. Hao, G. Levine, R. Gao, A. Norcia, L. Fei-Fei, and J. Wu, “Noir: Neural signal operated intelligent robots for everyday activities,” in 7th Annual Conference on Robot Learning , 2023
2023
Later among the works it cites.
F. S. Racz, R. Fakhreddine, S. Kumar, and J. Del R. Millan, “Riemannian geometry-based detection of slow cortical potentials during movement preparation,” in 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) , 2023, pp. 1–5
2023
Later among the works it cites.
EEG System for Clinical and Research Use — Bittium NeurOne. Bittium. Accessed 2023-03-06. [Online]. Available: https://www.bittium.com/medical/bittium-neuron
2023
Later among the works it cites.