Integrated Circuits for Next-Generation Neural Implants

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About 3.6 million Canadians are affected by neurological conditions, such as epilepsy and Parkinson's disease. Neural implants are emerging medical devices that can treat neurological conditions by providing therapeutic stimulations (similar to a heart pacemaker) to a patient in response to the real-time detection of irregular biomarkers in the neural signals (e.g., seizures). Neural implants can achieve superior clinical efficacy over conventional medications, especially in treating intractable symptoms. Integrated circuits (ICs) have been developed for integrating neural implants into miniature chips, which can greatly improve performance and reduce device size (thus reducing surgical invasiveness). Although IC designs for neural implants have advanced significantly in recent years, three major limitations exist. Firstly, neural interfacing in most designs is limited to electrical signals alone, missing key biomarkers of irregular neural states in chemical signals (e.g., dopamine levels). Secondly, neural signal processing algorithms are often oversimplified during IC integration, rendering poor performance in detecting biomarkers for stimulation. Although machine learning algorithms have shown advantages in offline studies, they are not adopted due to the lack of energy-efficient IC implementation. Thirdly, electrodes implanted in different brain regions are often connected to a centralized IC through long wires, causing signal contamination and risks of wire displacement. Our long-term goal is to develop ICs for next-generation neural implants with paradigm shifts in neural interfacing modalities (from electrical signal alone to multi-modal), signal processing capabilities (from coarse detection to machine learning), and system topology (from centralized to distributed). Specifically, we have three short-term objectives: (1) develop novel multi-modal neural interfacing ICs to capture comprehensive neural activities for improved diagnosis; (2) develop energy-efficient ICs to support high performance biomarker detection by machine learning; (3) develop a low-power, short-latency wireless network to enable a distributed neural implant system eliminating long wires. IC designs with fundamental innovations are proposed to achieve these goals. This research program will advance the performance, safety, and reliability of neural implants by novel IC designs. These ICs can be fabricated at low costs and enable treatments that can benefit a large patient population, especially those disadvantaged due to income and healthcare inequalities. The advanced IC design techniques can also be applied in a wide range of applications, providing great opportunities for entrepreneurship and technology transfer. Furthermore, this program will provide HQP with multi-disciplinary training, including IC designs for neural interfacing, machine learning, and wireless communication.