The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals
Abstract
Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for multi-directional arm reaching tasks in three-dimensional (3D) environments. We designed and implemented an experimental environment in which electroencephalogram (EEG) signals can be acquired for movement execution and imagery. Fifteen subjects participated in our experiments. We proposed a multi-directional convolution neural network-bidirectional long short-term memory network (MDCBN)-based deep learning framework. The decoding performances for six directions in 3D space were measured by the correlation coefficient (CC) and the normalized root mean square error (NRMSE) between predicted and baseline velocity profiles. The grand-averaged CCs of multi-direction were 0.47 and 0.45 for the execution and imagery sessions, respectively, across all subjects. The NRMSE values were below 0.2 for both sessions. Furthermore, in this study, the proposed MDCBN was evaluated by two online experiments for real-time robotic arm control, and the grand-averaged success rates were approximately 0.60 (±0.14) and 0.43 (±0.09), respectively. Hence, we demonstrate the feasibility of intuitive robotic arm control based on EEG signals for real-world environments.
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Cites
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115 · cited 1×
“Table II shows the decoding performance comparison on the test data using the proposed MDCBN method and conventional methods: multiple linear regression (MLR) [57], KRR [9], LSTM [58], ShallowConvNet [56], and EEG-Net [59].”
MethodDeep learning with convolutional neural networks for EEG decoding and visualization
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23730 · Cited by 3,751
Deep learning for electroencephalogram (EEG) classification tasks: a review
Craik, A., He, Y., & Contreras-Vidal, J. L. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab0ab5 · Cited by 1,758
A comprehensive review of EEG-based brain–computer interface paradigms
Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., & Zhao, X. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaf12e · Cited by 946
A novel deep learning approach for classification of EEG motor imagery signals
Tabar, Y. R., & Halici, U. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2560/14/1/016003 · Cited by 859
Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks
Sakhavi, S., Guan, C., & Yan, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2789927 · Cited by 656
Emotion Recognition based on EEG using LSTM Recurrent Neural Network
Alhagry, S., Aly, A., & A., R. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.081046 · Cited by 645
Tsiouris, Κ. Μ., Pezoulas, V. C., Zervakis, M., Konitsiotis, S., Koutsouris, D. D., & Fotiadis, D. I. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.019 · Cited by 632
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
Lee, M.-H., Kwon, O.-Y., Kim, Y.-J., Kim, H.-K., Lee, Y.-E., Williamson, J., Fazli, S., & Lee, S.-W. (2019) · GigaScience · doi:10.1093/gigascience/giz002 · Cited by 590
Self-citationNoninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks
Meng, J., Zhang, S., Bekyo, A., Olsoe, J., Baxter, B., & He, B. (2016) · Scientific Reports · doi:10.1038/srep38565 · Cited by 484 · cited 3×
“For example, Meng et al. [6] and Edelman et al. [36] demonstrated the feasibility of a robotic arm that performed reach-and-grasp tasks in a real-time environment.”
Background
Cited by
Artificial intelligence for the metaverse: A survey
Huynh-The, T., Pham, Q.-V., Pham, X.-Q., Nguyen, T. T., Han, Z., & Kim, D.-S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105581 · Cited by 710
Self-citationBenefits of deep learning classification of continuous noninvasive brain–computer interface control
Stieger, J. R., Engel, S. A., Suma, D., & He, B. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac0584 · Cited by 55
Metadata
- Authors
- Jeong, J.-H., Shim, K.-H., Kim, D.-J., & Lee, S.-W.
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2020-05
- Volume
- 28
- Issue
- 5
- Pages
- 1226-1238
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W3012521722
- Cited by
- 292
- References
- 66
- FWCI
- 24.54
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jeong, J.-H., Shim, K.-H., Kim, D.-J., & Lee, S.-W. (2020). Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/tnsre.2020.2981659
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