An Energy-Efficient CMOS Dual-Mode Array Architecture for High-Density ECoG-Based Brain-Machine Interfaces.
In: IEEE transactions on biomedical circuits and systems, Jg. 14 (2020-04-01), Heft 2, S. 332-342
Online
academicJournal
Zugriff:
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- γ 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 1.72× power reduction in the analog blocks and up to 50× potential power savings for digitization and processing (implemented off-chip in this article). A prototype incorporating a 32-channel ultra-low power signal acquisition front-end is fabricated in 180 nm CMOS process with 0.8 V supply. This chip consumes 1.05 μW (0.205 μW for feature extraction only) power and occupies 0.245 [Formula: see text] die area per channel. The chip measurement shows better than 76.5-dB common-mode rejection ratio (CMRR), 4.09 noise efficiency factor (NEF), and 10.04 power efficiency factor (PEF). In-vivo 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.
Titel: |
An Energy-Efficient CMOS Dual-Mode Array Architecture for High-Density ECoG-Based Brain-Machine Interfaces.
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Autor/in / Beteiligte Person: | Malekzadeh-Arasteh, O ; Pu, H ; Lim, J ; Liu, CY ; Do, AH ; Nenadic, Z ; Heydari, P |
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Zeitschrift: | IEEE transactions on biomedical circuits and systems, Jg. 14 (2020-04-01), Heft 2, S. 332-342 |
Veröffentlichung: | New York, NY : IEEE, c2007-, 2020 |
Medientyp: | academicJournal |
ISSN: | 1940-9990 (electronic) |
DOI: | 10.1109/TBCAS.2019.2963302 |
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