1) Brain Machine Interfaces
Brain machine interfaces have been a subject of active research because of their potential for a wide range of applications once the technology reaches maturity. Developing technologies in BMIs is an essential step to ultimately help overcome neuromuscular disabilities and treat neurological disorders. One key challenge in BMIs from a signal processing perspective is to obtain a robust and reliable neural decoder, which can characterize the electrical activity of groups of neurons to control external devices, for instance, a robotic arm or a computer cursor. We develop neural decoders in a brain machine interface architecture based on reinforcement learning (RLBMI).
Related Publications
- B. R. Thapa, D. Restrepo Tangarife, and J. Bae. “Kernel Temporal Differences for EEG-based Reinforcement Learning Brain Machine Interfaces.” Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), 2022, pp.3327-3333, doi: 10.1109/EMBC48229.2022.9871862.
- P. Awasthi, T. H. Lin, J. Bae, L. E. Miller, and Z. C. Danziger. “Validation of a non-invasive, real-time, human-in-the-loop model of intracortical brain-computer interfaces.” Journal of Neural Engineering, 2022, doi: 10.1088/1741-2552/ac97c3.
- B. Girdler, W. Caldbeck, and J. Bae. “Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review.” Frontiers in Systems Neuroscience, 16, 2022, doi=10.3389/fnsys.2022.836778.
- W. Plucknett, L. G. Sanchez Giraldo, and J. Bae. “Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces.” Frontiers Human Neuroscience, 16, 2022, doi=10.3389/fnhum.2022.902183.
- J. Fugal, J. Bae*, and H. A. Poonawala*. “On the Impact of Gravity Compensation on Reinforcement Learning in Goal-Reaching Tasks for Robotic Manipulators.” Robotics, 2021, 10(1), 46, https://doi.org/10.3390/robotics10010046. (* corresponding authors)
- J. Bae, L. G. Sanchez Giraldo, E. A. Pohlmeyer, J. T. Francis, J. C. Sanchez, and J. C. Principe. “Kernel Temporal Differences for Neural Decoding.” Computational Intelligence and Neuroscience, 2015, Article ID 481375, pp. 1-17, https://doi.org/10.1155/2015/481375.
- J. Bae, L. G. Sanchez Giraldo, J. T. Francis, and J. C. Principe. “Correntropy Kernel Temporal Differences for Reinforcement Learning Brain Machine Interfaces.” The International Joint Conference on Neural Networks (IJCNN), 2014, pp. 2713-2717, doi: 10.1109/IJCNN.2014.6889958.
- A. J. Brockmeier, L. G. Sanchez Giraldo, M. S. Emigh, J. Bae, J. S. Choi, J. T. Francis, and J. C. Principe. “Information-Theoretic Metric Learning: 2-D Linear Projections of Neural Data for Visualization.” Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), 2013, pp. 5586-5589, doi:10.1109/EMBC.2013.6610816.
- J. Bae, L. G. Sanchez Giraldo, E. A. Pohlmeyer, J. C. Sanchez, and J. C. Principe. “A New Method of Concurrently Visualizing States, Values, and Actions in Reinforcement based Brain Machine Interfaces.” Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), 2013, pp. 5402-5405, doi:10.1109/EMBC.2013.6610770.
2) EEG Analysis and Source Imaging
EEG source imaging (ESI) provides functional images of the whole brain with an exquisite temporal resolution based on high-resolution EEG recordings. Particularly in focal epilepsy, which is a well-known neurological disorder accompanied with recurrent and unprovoked seizures, ESI allows a better understanding of the electrical substrates of pathological events and abnormalities associated with brain network activity. We investigate EEG analysis methods to extract meaningful features to help ESI and to better understand underlying mechanisms on ESI-based biomarkers.
Related Publications
- J. Bae, J. Clay, B. R. Thapa, D. Powell, B. Krishnan, A. Koupparis, M. Bensalem Owen, and F. Raslau. “Process of equipment setup and artifact removal for simultaneous EEG-fMRI recordings for clinical review of interictal period in epilepsy.” Journal of Visualized Experiments, 2023, (196), e64919, doi:10.3791/64919.
- A. Deshmukh, J. Leichner, J. Bae, Y. Song, P. A. Valdes-Hernandez, W. C. Lin, and J. J. Riera. “Histological Characterization of the Irritative Zones in Focal Cortical Dysplasia Using a Preclinical Rat Model.” Frontiers Cellular Neuroscience, eCollection 2018, doi: 10.3389/fncel.2018.00052, 2018.
- P. A. Valdes-Hernandez, J. Bae, Y. Song, A. Sumiyoshi, E. Aubert-Vazquez, and J. J. Riera. “Validating Non-invasive EEG Source Imaging Using Optimal Electrode Configurations on a Representative Rat Head Model.” Brain topography, 2019, 32(4), pp. 599-624, doi: 10.1007/s10548-016-0484-4.
- Y. Song, R. A. Torres, S. Garcia, Y. Frometa, J. Bae, A. Deshmukh, W. Lin, Y. Zheng, and J. J. Riera. “Dysfunction of Neurovascular/Metabolic Coupling in Chronic Focal Epilepsy.” IEEE Transactions on Biomedical Engineering, 2016, 63(1), pp.97-110, doi: 10.1109/TBME.2015.2461496.
- J. Bae, A. Deshmukh, Y. Song, and J. Riera. “Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings.” Journal of Visualized Experiments, (100), e52700, doi:10.3791/52700, 2015.