A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System With Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting

• Published in: IEEE transactions on biomedical circuits and systems Vol. 18; no. 4; pp. 908 - 922
• Main Authors: Fathy, Nader Sherif Kassem, Vatsyayan, Ritwik, Bourhis, Andrew M., Dayeh, Shadi A., Mercier, Patrick P.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Action potential; Adaptive filters; Brain-computer interfaces; Demodulation; Digital filters; digitally assisted sign-sign least mean square (LMS) adaptive filter (AF); electrocorticography (ECoG); Electrophysiological recording; Impedance; impedance boosting; implantable brain-machine interface (BMI); Implants; Input impedance; Land mobile satellite service; Least mean squares; Least mean squares algorithm; Microelectrodes; Modules; Multi-channel neural recording system; Multiplexing; Nanorods; Noise prediction; Noise reduction; Recording; Remodulation; Thin films; Time Division Multiple Access; time-division multiple access (TDMA)
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2024.3366649

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4<inline-formula><tex-math notation="LaTeX"> \times </tex-math></inline-formula> and 4.3<inline-formula><tex-math notation="LaTeX"> \times </tex-math></inline-formula> in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39<inline-formula><tex-math notation="LaTeX"> \times </tex-math></inline-formula> with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6<inline-formula><tex-math notation="LaTeX"> \times </tex-math></inline-formula> and 2.8<inline-formula><tex-math notation="LaTeX"> \times </tex-math></inline-formula> savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1<inline-formula><tex-math notation="LaTeX"> \times </tex-math></inline-formula> in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm 2 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μV rms and 2.4 μV rms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.