Lee Han-Sol, Eom Kyeongho, Park Minju, Ku Seung-Beom, Lee Kwonhong, Lee Hyung-Min
School of Electrical Engineering, Korea University, Seoul, South Korea.
Biomed Eng Lett. 2022 May 30;12(3):251-261. doi: 10.1007/s13534-022-00233-z. eCollection 2022 Aug.
Implantable medical devices capable of monitoring hundreds to thousands of electrodes have received great attention in biomedical applications for understanding of the brain function and to treat brain diseases such as epilepsy, dystonia, and Parkinson's disease. Non-invasive neural recording modalities such as fMRI and EEGs were widely used since the 1960s, but to acquire better information, invasive modalities gained popularity. Since such invasive neural recording system requires high efficiency and low power operation, they have been implemented as integrated circuits. Many techniques have been developed and applied when designing integrated high-density neural recording architecture for better performance, higher efficiency, and lower power consumption. This paper covers general knowledge of neural signals and frequently used neural recording architectures for monitoring neural activity. For neural recording architecture, various neural recording amplifier structures are covered. In addition, several neural processing techniques, which can optimize the neural recording system, are also discussed.
能够监测数百到数千个电极的可植入医疗设备在生物医学应用中备受关注,用于理解脑功能以及治疗癫痫、肌张力障碍和帕金森病等脑部疾病。自20世纪60年代以来,诸如功能磁共振成像(fMRI)和脑电图(EEGs)等非侵入性神经记录方式被广泛使用,但为了获取更好的信息,侵入性方式开始流行起来。由于这种侵入性神经记录系统需要高效和低功耗运行,它们已被实现为集成电路。在设计集成高密度神经记录架构以实现更好的性能、更高的效率和更低的功耗时,已经开发并应用了许多技术。本文涵盖了神经信号的一般知识以及用于监测神经活动的常用神经记录架构。对于神经记录架构,涵盖了各种神经记录放大器结构。此外,还讨论了几种可以优化神经记录系统的神经处理技术。