An Energy-Efficient CMOS Dual-Mode Array Architecture for High-Density ECoG-Based Brain-Machine Interfaces
An Energy-Efficient CMOS Dual-Mode Array Architecture for High-Density ECoG-Based Brain-Machine Interfaces
复制标题
DOI：
10.1109/tbcas.2019.2963302
复制
发表时间：
2020-04-01
影响因子：
5.1
通讯作者：
Heydari, Payam
中科院分区：
文献类型：
--
作者：
Malekzadeh-Arasteh, Omid;Pu, Haoran;Heydari, Payam
本文介绍了一种用于完全植入脑机接口系统的节能皮质电图 (ECoG) 阵列架构。介绍了一种新颖的双模式模拟信号处理方法，该方法可以从高频信号中提取神经特征。频带（80-160 Hz）在信号采集的早期阶段。最初，在全频段模式操作期间，暂时观察全频谱的大脑活动，以计算数字后端的特征权重。随后，这些权重被反馈到前端，系统恢复到基带模式以执行特征提取。该方法利用基于功率包络提取的独特优化信号路径，从而使模拟模块的功耗降低 1.72 倍，并为数字化和处理节省高达 50 倍的功耗（在本文中是在片外实现的）。包含 32 通道超低功耗信号采集前端的原型采用 180 nm CMOS 工艺制造，电源电压为 0.8 V。该芯片消耗 1.05 mu W（0.205 mu Wf 或仅特征提取）功率，每通道占用 0.245mm2 芯片面积。芯片测量显示出优于 76.5dB 的共模抑制比 (CMRR)、4.09 的噪声效率因数 (NEF) 和 10.04 的功率效率因数 (PEF)。 Invivo 人体测试已使用脑电图和 ECoG 信号进行，以验证与商业采集系统相比的性能和双模式操作。
This article presents an energy-efficient electrocorticography (ECoG) array architecture for fully-implantable brain machine interface systems. A novel dual-mode analog signal processing method is introduced that extracts neural features from high-. band (80-160 Hz) at the early stages of signal acquisition. Initially, brain activity across the full-spectrum is momentarily observed to compute the feature weights in the digital back-end during full-band mode operation. Subsequently, these weights are fed back to the front-end and the system reverts to base-band mode to perform feature extraction. This approach utilizes a distinct optimized signal pathway based on power envelope extraction, resulting in 1.72x power reduction in the analog blocks and up to 50xpotential power savings for digitization and processing (implemented off-chip in this article). A prototype incorporating a 32channel ultra-low power signal acquisition front-end is fabricated in 180 nm CMOS process with 0.8 V supply. This chip consumes 1.05 mu W (0.205 mu Wf or feature extraction only) power and occupies 0.245mm2 die area per channel. The chipmeasurement shows better than 76.5-dB common-mode rejection ratio (CMRR), 4.09 noise efficiency factor (NEF), and 10.04 power efficiency factor (PEF). Invivo human tests have been carried out with electroencephalography and ECoG signals to validate the performance and dual-mode operation in comparison to commercial acquisition systems.