T&C Chen Brain-Machine Interface Center
The T&C Chen Brain-machine Interface Center, led by Richard Andersen, is advancing Caltech's work on a new generation of devices that can directly communicate with the brain. These neuroprosthetics enable people with paralysis to control robotic limbs and computer interfaces by simply thinking about moving. Similarly, these devices can stimulate the brain to restore the senses of touch and movement previously lost due to brain disease or injury. The Chen BMI Center not only provides the infrastructure and coordination for researchers to develop these devices, but also fosters a multi-disciplinary and comprehensive scientific environment for scientists.
Clinical Trials. The Chen Brain-Machine Interface (BMI) center supports researchers working on several projects that advance neuroprosthetic technology. In one FDA approved clinical study, researchers are comparing neural signals from different brain regions that contribute to planning and executing movements. These studies build on the foundational science of intention (Intention), cognition, and perception to provide new insights into human brain function that enable the next generation of neural prosthetic interfaces (Clinical trials). In another FDA approved clinical study, researchers at the Chen BMI Center aim to replace lost sensations by electrically stimulating the brain (Restoring sensation). This "bi-directional" brain-machine interface supplements the control of a robotic limb by delivering somatosensory feedback. This artificially induced sense of touch will allow more dexterous performance for patients controlling robotic limbs or exoskeletons.
New Technologies. The clinical and commercial success of brain-machine interfaces requires the development of novel technologies that are safer and more effective. To this end, the Chen BMI Center also supports the engineering, development, and study of several new technologies (New technologies). One such project is developing chronically-implantable chips that record and translate brain activity into assistive control signals. The integration of neural signals with state-of-the art exoskeletons and robotic limbs will expand the ways patients can benefit from assistive implants. Finally, to expand the population of people who can benefit from BMI technology, the Chen BMI center supports translational studies of new, less-invasive, high-performance technologies in animal models and humans. One project is currently exploring the feasibility of using a novel ultrasound-based neuroimaging technique to "read" movement intention signals in a minimally invasive manner.
Richard Andersen (Caltech)
Azita Emami (Caltech, https://www.mics.caltech.edu)
Mikhail Shapiro (Caltech, http://shapirolab.caltech.edu)
Nader Pouratian (UCLA, https://nbmrl.dgsom.ucla.edu/pages/)
Charles Liu and Brian Lee (USC, https://keck.usc.edu/neurorestoration-center/)
Payam Heydari (UC Irvine, https://engineering.uci.edu/users/payam-heydari)
Chivukula, S., C. Zhang, T. Aflalo, M. Jafari, K. Pejsa, N. Pouratian and R. A. Andersen (2020). "Neural encoding of felt and imagined touch within human posterior parietal cortex." bioRxiv: 2020.2007.2027.223636.
Aflalo, T., C. Zhang, E. R. Rosario, N. Pouratian, G. A. Orban and R. A. Andersen (2020). "A shared neural substrate for action verbs and observed actions in human posterior parietal cortex." bioRxiv: 2020.2004.2020.039529.
Jafari, M., T. N. Aflalo, S. Chivukula, S. S. Kellis, M. A. Salas, S. L. Norman, K. Pejsa, C. Y. Liu and R. A. Andersen (2020). "Neural correlates of cognitive motor signals in primary somatosensory cortex." bioRxiv: 2020.2004.2020.041269.
Zhang, C. Y., T. Aflalo, B. Revechkis, E. Rosario, D. Ouellette, N. Pouratian and R. A. Andersen (2020). "Preservation of Partially Mixed Selectivity in Human Posterior Parietal Cortex across Changes in Task Context." eNeuro 7(2).
Andersen, R. A., T. Aflalo and S. Kellis (2019). "From thought to action: The brain-machine interface in posterior parietal cortex." Proceedings of the National Academy of Sciences of the United States of America 116(52): 26274-26279.
Sakellaridi, S., V. N. Christopoulos, T. Aflalo, K. W. Pejsa, E. R. Rosario, D. Ouellette, N. Pouratian and R. A. Andersen (2019). "Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex." Neuron 102(3): 694-705
Rutishauser, U., T. Aflalo, E. R. Rosario, N. Pouratian and R. A. Andersen (2018). "Single-Neuron Representation of Memory Strength and Recognition Confidence in Left Human Posterior Parietal Cortex." Neuron 97(1): 209-220 e203.
Zhang, C. Y., T. Aflalo, B. Revechkis, E. R. Rosario, D. Ouellette, N. Pouratian and R. A. Andersen (2017). "Partially Mixed Selectivity in Human Posterior Parietal Association Cortex." Neuron 95(3): 697-708 e694.
Pu, H., J. Lim, S. Kellis, C. Y. Liu, R. A. Andersen, A. H. Do, P. Heydari and Z. Nenadic (2020). "Optimal artifact suppression in simultaneous electrocorticography stimulation and recording for bi-directional brain-computer interface applications." J Neural Eng 17(2): 026038.
Kramer, D. R., S. Kellis, M. Barbaro, M. A. Salas, G. Nune, C. Y. Liu, R. A. Andersen and B. Lee (2019). "Technical considerations for generating somatosensation via cortical stimulation in a closed-loop sensory/motor brain-computer interface system in humans." J Clin Neurosci 63: 116-121.
Salas, M. A., L. Bashford, S. Kellis, M. Jafari, H. Jo, D. Kramer, K. Shanfield, K. Pejsa, B. Lee, C. Y. Liu and R. A. Andersen (2018). "Proprioceptive and cutaneous sensations in humans elicited by intracortical microstimulation." Elife 7.
Lee, B., D. Kramer, M. A. Salas, S. Kellis, D. Brown, T. Dobreva, C. Klaes, C. Heck, C. Liu and R. A. Andersen (2018). "Engineering Artificial Somatosensation Through Cortical Stimulation in Humans." Frontiers in Systems Neuroscience 12.
Haghi, B. A., S. Kellis, S. Shah, M. Ashok, L. Bashford, D. Kramer, B. Lee, C. Liu, R. A. Andersen and A. Emami (2019). "Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces." NeurIPS.
Shah, S., B. Haghi, S. Kellis, L. Bashford, D. Kramer, B. Lee, C. Liu, R. Andersen, A. Emami and Ieee (2019). Decoding Kinematics from Human Parietal Cortex using Neural Networks. 9th IEEE/EMBS International Conference on Neural Engineering (NER), San Francisco, CA.
Norman, S. L., D. Maresca, V. N. Christopoulos, W. S. Griggs, C. Demene, M. Tanter, M. G. Shapiro and R. A. Andersen (2020). "Single Trial Decoding of Movement Intentions Using Functional Ultrasound Neuroimaging." bioRxiv: 2020.2005.2012.086132.
Reutskaja, E., A. Lindner, R. Nagel, R. A. Andersen and C. F. Camerer (2018). "Choice overload reduces neural signatures of choice set value in dorsal striatum and anterior cingulate cortex." Nature Human Behaviour 2(12): 925-935.
Christopoulos, V. N., I. Kagan and R. A. Andersen (2018). "Lateral intraparietal area (LIP) is largely effector-specific in free-choice decisions." Sci Rep 8(1): 8611.