HAI & Wu Tsai Neuro Partnership Grant Recipients 2022
The HAI & Wu Tsai Neuro Partnership Grants are designed to support researchers who aim to transform brain health, counter disease, and develop AI technologies inspired by the versatility and depth of human intelligence.
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Stroke is the third-leading cause of death and disability combined in the world. The estimated global cost of stroke is over US$721 billion—0.66% of the global GDP. The primary method to induce motor recovery in stroke patients involves active motor training via physical and occupational therapies; however, these treatments are unsatisfactory. Robotics rehabilitation with brain-computer interface (BCI) and virtual reality (VR) can improve the efficacy of therapy as it will involve the active participation of patients’ brains during rehabilitation sessions. Several such systems have been developed; however, the underlying hardware and signal processing algorithms remain challenging. To address these challenges, we propose a radical solution of combining brain-computer interface and augmented reality (AR) into a single rehabilitation platform. We propose to use steady-state visual evoked potentials (SSVEPs) as inputs to the BCI and action observation (AO) implemented via AR-based visual feedback to overcome major limitations of current BCI-based approaches. The proposed BCI-AR-based rehabilitation system has the potential to revolutionize future stroke treatment both in clinics and at home.
Name
Role
School
Department
Main PI
School of Engineering
Electrical Engineering
Co-PI
School of Engineering
Mechanical Engineering
Co-PI
School of Medicine
Neurology
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An average adult speaks approximately 16,000 words per day, using verbal communication to build and maintain relationships, meet basic needs, navigate safely, and work. Approximately 10% of the US adult population reports a communication disorder, with severe disease preventing vocalized speech altogether. Although Augmentative and Alternative Communication technology is often used by people with communication disorders. Existing systems are strongly limited in performance, inhibiting participation in spoken conversation, and creating an urgent need for improvement. Our approach uses grids of high-density surface electromyography (HD-sEMG) channels, embedded on a soft, conformable substrate, to enable close adhesion to the face during speech production and widespread coverage of articulator muscles. This enables us to infer wearer intentions with high accuracy. By combining novel materials science with modern machine learning, we aim to push HD-sEMG capabilities significantly beyond prior work and enable new forms of human-computer interaction.
Name
Role
School
Department
Main PI
School of Engineering
Chemical Engineering
Co-PI
School of Medicine
Neurobiology
Co-PI
School of Engineering
Electrical Engineering
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An assembly of neurons encodes information in a sequence of spikes. Axons from this assembly deliver its spike sequence to a short stretch of dendrite. How this stretch decodes the encoded information is unknown. This project will mine a microscale reconstruction of a millimeter-cube of brain tissue for anatomical signatures of sequence-decoding. These signatures were predicted by a computational model of a dendrite developed by us. It responds only when a sequence’s spikes activate its synapses consecutively, from one end of the stretch to the other. It makes a testable prediction: When branches of axons carrying a sequence contact two stretches of dendrite, they will synapse onto those stretches in the same order. Confirming this prediction will unravel how axon branches and dendrite stretches are organized at the microscale. That would reveal how biological neural nets operate with far fewer signals than artificial neural nets. This sparse signaling saves energy. That would enable AI chips to become 3D—like the brain.
Name
Role
School
Department
Main PI
School of Engineering
Bioengineering
Co-PI
School of Medicine
Ophthalmology
One additional project to be announced soon.