| Deep learning movement intent decoders trained with dataset aggregation for prosthetic limb control H Dantas, DJ Warren, SM Wendelken, TS Davis, GA Clark, VJ Mathews IEEE Transactions on Biomedical Engineering 66 (11), 3192-3203, 2019 | 71 | 2019 |
| Recording and decoding for neural prostheses DJ Warren, S Kellis, JG Nieveen, SM Wendelken, H Dantas, TS Davis, ... Proceedings of the IEEE 104 (2), 374-391, 2016 | 65 | 2016 |
| Neural decoding using a nonlinear generative model for brain-computer interface H Dantas, S Kellis, VJ Mathews, B Greger 2014 IEEE international conference on acoustics, speech and signal …, 2014 | 11 | 2014 |
| Shared prosthetic control based on multiple movement intent decoders H Dantas, TC Hansen, DJ Warren, VJ Mathews IEEE Transactions on Biomedical Engineering 68 (5), 1547-1556, 2020 | 9 | 2020 |
| Shared human-machine control for self-aware prostheses H Dantas, J Nieven, TS Davis, X Fu, GA Clark, DJ Warren, VJ Mathews 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 8 | 2018 |
| Neural decoding systems using markov decision processes H Dantas, VJ Mathews, SM Wendelken, TS Davis, GA Clark, DJ Warren 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 7 | 2017 |
| Interpreting volitional movement intent from biological signals: A review H Dantas, TC Hansen, DJ Warren, VJ Mathews IEEE Signal Processing Magazine 38 (4), 23-33, 2021 | 5 | 2021 |
| Semi-supervised adaptive learning for decoding movement intent from electromyograms H Dantas, VJ Mathews, D Warren 2019 27th European Signal Processing Conference (EUSIPCO), 1-5, 2019 | 5 | 2019 |
| Data-Driven Approaches For Decoding Volitional Movement Intent From Bioelectrical Signals H Cunha Dantas | | 2019 |
| Brain-machine interface using nonlinear Kalman filters and channel selection HC DANTAS | | 2015 |
V John MathewsProfessor of Electrical Engineering and Computer Science, Oregon State UniversityVerified email at oregonstate.edu
