TY - GEN
T1 - A Synchronous-Sampling Impedance-Readout IC with Baseline-Cancellation-Based Two-Step Conversion for Fast Neural Electrical Impedance Tomography
AU - Suh, Ji Hoon
AU - Choi, Haidam
AU - Jung, Yoontae
AU - Oh, Sein
AU - Cho, Hyungjoo
AU - Koo, Nahmil
AU - Kim, Seong Joong
AU - Bae, Chisung
AU - Ha, Sohmyung
AU - Je, Minkyu
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Electrical impedance tomography (EIT) is widely used for functional imaging of the bio-impedance of body parts for various applications, such as lung ventilation monitoring [1]. It was recently shown that 'fast neural EIT' with far enhanced temporal resolution (frame rate) can provide the neural activity monitoring and functional localization of the active peripheral nerve at the same time [2]. In an EIT system, to reconstruct an impedance tomography image (Fig. 1(a)), an AC current is injected from a current generator (ICG) into the target bioimpedance network ZBIO through an electrode pair (channel) in a rotational manner, while demodulating the voltages appeared at all the other channels. The I/Q demodulation is the most popular way to extract the resistance and reactance information of ZBIO [1]. For the neural EIT, however, this method cannot support a high enough frame rate, failing to acquire neural activities, mainly due to the downconversion to DC and low-pass filtering. As shown in Fig. 1(a), many cycles of the AC input signal are needed for the I and Q outputs to be well settled to their final values. A higher excitation frequency (fCG) can be used for faster settling in conventional applications, but in the neural EIT, fCG should be <20kHz for high SNR image acquisition [2]. Alternatively, peak detection can be used [3], but it needs a much faster sampling clock than fCG, consuming a large dynamic power in all the demodulation channels.
AB - Electrical impedance tomography (EIT) is widely used for functional imaging of the bio-impedance of body parts for various applications, such as lung ventilation monitoring [1]. It was recently shown that 'fast neural EIT' with far enhanced temporal resolution (frame rate) can provide the neural activity monitoring and functional localization of the active peripheral nerve at the same time [2]. In an EIT system, to reconstruct an impedance tomography image (Fig. 1(a)), an AC current is injected from a current generator (ICG) into the target bioimpedance network ZBIO through an electrode pair (channel) in a rotational manner, while demodulating the voltages appeared at all the other channels. The I/Q demodulation is the most popular way to extract the resistance and reactance information of ZBIO [1]. For the neural EIT, however, this method cannot support a high enough frame rate, failing to acquire neural activities, mainly due to the downconversion to DC and low-pass filtering. As shown in Fig. 1(a), many cycles of the AC input signal are needed for the I and Q outputs to be well settled to their final values. A higher excitation frequency (fCG) can be used for faster settling in conventional applications, but in the neural EIT, fCG should be <20kHz for high SNR image acquisition [2]. Alternatively, peak detection can be used [3], but it needs a much faster sampling clock than fCG, consuming a large dynamic power in all the demodulation channels.
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U2 - 10.1109/A-SSCC56115.2022.9980820
DO - 10.1109/A-SSCC56115.2022.9980820
M3 - Conference contribution
AN - SCOPUS:85146591018
T3 - 2022 IEEE Asian Solid-State Circuits Conference, A-SSCC 2022 - Proceedings
BT - 2022 IEEE Asian Solid-State Circuits Conference, A-SSCC 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Asian Solid-State Circuits Conference, A-SSCC 2022
Y2 - 6 November 2022 through 9 November 2022
ER -