Brain Machine Interface
The parallel recordings from large neuron populations in the sensory cortex and primary motor cortex reveal the rich information encoded into neural signals, and guide research in restoring cognitive and motor behaviors. In such devices, the quality of information relies on the density and resolution of neural signals being measured (1). The current state-of-the-art brain-machine interface (BMI) is capable of hundreds of simultaneous electrocorticography (ECoG) recordings and was successfully used for alleviating gait deficits in primates with spinal cord injury (2–4). However, the throughput remains insufficient to be clinically relevant and significant improvements in the recording throughput are required for BMIs to help severely disabled patients to fully regain mobility or other impaired functions (5). Restoring limb movements may require a BMI to monitor 5,000 – 10,000 neurons simultaneously (6), whereas producing full-body movements may require 100,000 neural measurements (5). In recent years, many researchers are leading the BMI development to increase the throughput beyond 1000-ch recordings (2, 3, 7–10).
The goal of our work is to develop a new approach to eliminate massive external wire connections and packaging complexity, and build a strong foundation that will lead future BMI developments beyond 1000-ch parallel recording capabilities. Our concept mostly relies on common fabrication techniques used in CMOS foundries, and thus will have a short development phase as well as a low cost for manufacturing. The CMOS technology, used to fabricate integrated circuits, has been proven to produce low-cost and robust devices with extremely high-throughput ca-pabilities in various biomedical application, including next-generation sequencing (NGS) (11), single-cell am-perometry (12, 13), and subcellular neural recordings (14). If the wafer-scale manufacturing is adapted, the es-timated fabrication cost for each 1000-ch neural interface system is 150 USD, allowing a widespread use of the neural interface implant to paralyzed patients in need of assistive prosthesis.
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12. White, K.A., G. Mulberry, and B.N. Kim. 2017. Multifunctional High-Throughput Single-Cell Analysis using Reconfigurable Amplifier Array. Biophys. J. 112: 461a.
13. Kim, B.N., A.D. Herbst, S.J. Kim, B.A. Minch, and M. Lindau. 2013. Parallel recording of neurotransmitters release from chromaffin cells using a 10×10 CMOS IC potentiostat array with on-chip working electrodes. Biosens. Bioelectron. 41: 736–744.
14. Müller, J., M. Ballini, P. Livi, Y. Chen, M. Radivojevic, A. Shadmani, V. Viswam, I.L. Jones, M. Fiscella, R. Diggelmann, A. Stettler, U. Frey, D.J. Bakkum, A. Hierlemann, D. Schmitt-Landsiedel, R. Thewes, G.M. Church, and K.P. Kording. 2015. High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels. Lab Chip. 15: 2767–2780.