Center for Neurotechnology (CNT) member Amir Alimohammad, an associate professor of electrical and computer engineering at San Diego State University (SDSU), was recently awarded a three year, $320,000 grant from the National Science Foundation to develop a novel processor architecture for brain-computer interfaces. The award will be funding work with potential for profound and long-term human impact.
“The primary application of the energy-efficient brain-implantable neural signal processor in development is to improve the rehabilitation of people disabled by a variety of disorders such as stroke and spinal cord injuries. This has the potential to improve the quality of life for millions of patients,” Alimohammad said.
The research funded by this award will be transformative. Alimohammad’s research team is focusing on building a brain-implantable interface processor that can relax power requirements for wireless data transmission, while at the same time remain capable of controlling a complex external device, such as a brain-controlled prosthetic limb.
Alimohammad will be collaborating with CNT members Steve Perlmutter and Eberhard Fetz, physiology and biophysics professors at the University of Washington, who will help testing the processor in animal studies.
Addressing challenges to brain-computer interface development
Restoring a full range of movements for patients impacted by sensory and/or movement disorders generally requires taking neural recordings from relatively large populations of neurons in the brain. A long-standing challenge to neurotechnology development has been increasing the capacity of brain-computer interfaces to reliably and safely decode recorded neural data. Because of power requirements and the limitations of standard processor architectures, processing and transferring large amounts of neural data to and from the brain can cause the implanted processor to heat up, damaging delicate brain tissues and hindering real-time control of prosthetic devices.
In this collaborative research, a foundation will be laid for finding an optimal balance between real-time neural signal processing using a brain-implantable electronic device and wireless data transmission to a computer outside the body. Through optimizing this balance, the total power consumption and heat dissipation of the brain-implanted circuit will be minimized.
To help achieve the balance between real-time and external signal processing, Alimohammad is aiming to create a hybrid processor, one that is both software-programmable to efficiently execute neural signal processing algorithms and hardware-reconfigurable. The software programmability will support the continuing evolution of approaches and algorithmic improvements while using a smaller amount of chip silicon area. The processor also will use various dedicated hardware architectures, including artificial and spiking neural networks for area and energy-efficient processing of neural signals.
“Amir’s research on low-power implementation of artificial neural networks on-chip, particularly for implantable neural chips, is exciting work that brings together hardware engineers, brain scientists and neurophysiologists to solve an important problem,” said Sam Kassegne, CNT deputy director at SDSU and co-leader of the CNT’s Communication and Interface research thrust. “Amir has built a very good research team at SDSU, and his work has been impactful. His low-power chip-level implementation will help speed up adoption of brain-computer interfaces with more on-chip computational power.”