Pub Date : 2025-03-26DOI: 10.1109/TBCAS.2025.3573614
Yi-Jie Lin;Lin Chou;Kun-Ju Tsai;Yu-Te Liao
This paper presents a low-noise, low-power galvanic skin response (GSR) sensing circuit capable of simultaneously measuring skin conductance level (SCL) and skin conductance response (SCR) for psychological stress monitoring. The circuit incorporates second-order sub-ten-hertz filters that suppresses out-of-band interference and a programmable gain amplifier (PGA) to accommodate signals of varying magnitudes. Additionally, a dynamic range adjustment mechanism optimizes the primary amplifier’s operating range based on real-time SCL readings. The design achieves a 96.4 dB dynamic range with an input-referred noise of only 3.47 pArms within 0.5–5 Hz under optimal conditions. These advancements significantly enhance measurement accuracy and robustness for wearable stress monitoring and real-time biofeedback applications.
{"title":"A 96 dB Input Dynamic Range Galvanic Skin Response Readout IC With 3.5 pArms Input-Referred Noise for Mental Stress Monitoring","authors":"Yi-Jie Lin;Lin Chou;Kun-Ju Tsai;Yu-Te Liao","doi":"10.1109/TBCAS.2025.3573614","DOIUrl":"10.1109/TBCAS.2025.3573614","url":null,"abstract":"This paper presents a low-noise, low-power galvanic skin response (GSR) sensing circuit capable of simultaneously measuring skin conductance level (SCL) and skin conductance response (SCR) for psychological stress monitoring. The circuit incorporates second-order sub-ten-hertz filters that suppresses out-of-band interference and a programmable gain amplifier (PGA) to accommodate signals of varying magnitudes. Additionally, a dynamic range adjustment mechanism optimizes the primary amplifier’s operating range based on real-time SCL readings. The design achieves a 96.4 dB dynamic range with an input-referred noise of only 3.47 pA<sub>rms</sub> within 0.5–5 Hz under optimal conditions. These advancements significantly enhance measurement accuracy and robustness for wearable stress monitoring and real-time biofeedback applications.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"897-907"},"PeriodicalIF":4.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144153009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-23DOI: 10.1109/TBCAS.2025.3573027
Thorir Mar Ingolfsson;Victor Kartsch;Luca Benini;Andrea Cossettini
Speech imagery—the process of mentally simulating speech without vocalization—is a promising approach for brain-computer interfaces (BCIs), enabling assistive communication for individuals with speech impairments or to enhance privacy. However, existing EEG-based speech imagery systems remain impractical for use outside specialized laboratories due to their reliance on high-channel-count and resource-intensive machine learning models running on external computing platforms. In this work, we present the first end-to-end demonstration of EEG-based speech imagery decoding on a low-channel, ultra-low-power wearable device. Building on our previous work on vowel imagery, we introduce an extended framework leveraging the BioGAP platform and VowelNet, a lightweight neural network optimized for embedded speech imagery classification. In particular, we demonstrate state-of-the-art accuracy in the classification of an expanded vocabulary comprising vowels, commands, and rest states (13 classes) with a subject-specific training approach, achieving up to 50.0% for one subject (42.8% average) in multi-class classification. We deploy our model on an embedded biosignal acquisition and processing platform (BioGAP), based on the GAP9 processor, for real-time inference with minimal power consumption (25.93 mW). Our system achieves continuous execution for more than 21 hours on a small LiPo battery while maintaining classification latencies of 40.9 ms. Finally, we also explore the benefits of applying Continual Learning techniques to progressively improve the system’s performance throughout its operational lifetime, and we demonstrate that electrodes located on the temporal area contribute the most to the overall accuracy. This work marks a significant step toward practical, real-time, and unobtrusive speech imagery BCIs, unlocking new opportunities for covert communication and assistive technologies.
{"title":"A Wearable Ultra-Low-Power System for EEG-Based Speech-Imagery Interfaces","authors":"Thorir Mar Ingolfsson;Victor Kartsch;Luca Benini;Andrea Cossettini","doi":"10.1109/TBCAS.2025.3573027","DOIUrl":"10.1109/TBCAS.2025.3573027","url":null,"abstract":"Speech imagery—the process of mentally simulating speech without vocalization—is a promising approach for brain-computer interfaces (BCIs), enabling assistive communication for individuals with speech impairments or to enhance privacy. However, existing EEG-based speech imagery systems remain impractical for use outside specialized laboratories due to their reliance on high-channel-count and resource-intensive machine learning models running on external computing platforms. In this work, we present the first end-to-end demonstration of EEG-based speech imagery decoding on a low-channel, ultra-low-power wearable device. Building on our previous work on vowel imagery, we introduce an extended framework leveraging the BioGAP platform and <sc>VowelNet</small>, a lightweight neural network optimized for embedded speech imagery classification. In particular, we demonstrate state-of-the-art accuracy in the classification of an expanded vocabulary comprising vowels, commands, and rest states (13 classes) with a subject-specific training approach, achieving up to 50.0% for one subject (42.8% average) in multi-class classification. We deploy our model on an embedded biosignal acquisition and processing platform (BioGAP), based on the GAP9 processor, for real-time inference with minimal power consumption (25.93 mW). Our system achieves continuous execution for more than 21 hours on a small LiPo battery while maintaining classification latencies of 40.9 ms. Finally, we also explore the benefits of applying Continual Learning techniques to progressively improve the system’s performance throughout its operational lifetime, and we demonstrate that electrodes located on the temporal area contribute the most to the overall accuracy. This work marks a significant step toward practical, real-time, and unobtrusive speech imagery BCIs, unlocking new opportunities for covert communication and assistive technologies.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 4","pages":"743-755"},"PeriodicalIF":4.9,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144133226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a low-power and low phase error bioimpedance (BioZ) measurement IC designed for monitoring cardiopulmonary diseases. To compensate for the phase shift introduced along the signal path by current generator (CG), electrodes and sensor analog front-end (AFE), a novel phase shift calibration logic is proposed. Utilizing this calibration logic, a single-channel in-phase demodulation-based impedance measurement scheme is developed. A noise shaping pseudo-sine wave CG with data-weighted averaging (DWA) is used to minimize modulation harmonics. Fabricated in a 0.18µm CMOS process, the chip occupies 0.73 mm2 and consumes between 52.7 to 97.5 µA current from a 1.8V supply. The CG achieves 74.1 dB SFDR and −70dB THD at 15.5 kHz with a 50µApk stimulation current. The chip achieves 2mΩ/√Hz input-referred impedance noise at 1Hz, 91.1 dB SNR (BW = 4 Hz), 36 kΩ input range and less than 0.48$^{circ}$ phase error (0 − 90$^{circ}$, 1 – 20 kHz). On-body BioZ experiments using a 4-electrode configuration demonstrate clear recordings of Impedance Cardiography (ICG) and respiration signals.
{"title":"A 0.48$^{circ}$ Phase Error 91.1 dB SNR Bioimpedance Measurement IC for Monitoring Cardiopulmonary Diseases","authors":"Jiarun Yuan;Yanxing Suo;Qiao Cai;Hui Wang;Yongfu Li;Yong Lian;Yang Zhao","doi":"10.1109/TBCAS.2025.3572374","DOIUrl":"10.1109/TBCAS.2025.3572374","url":null,"abstract":"This article presents a low-power and low phase error bioimpedance (BioZ) measurement IC designed for monitoring cardiopulmonary diseases. To compensate for the phase shift introduced along the signal path by current generator (CG), electrodes and sensor analog front-end (AFE), a novel phase shift calibration logic is proposed. Utilizing this calibration logic, a single-channel in-phase demodulation-based impedance measurement scheme is developed. A noise shaping pseudo-sine wave CG with data-weighted averaging (DWA) is used to minimize modulation harmonics. Fabricated in a 0.18µm CMOS process, the chip occupies 0.73 mm2 and consumes between 52.7 to 97.5 µA current from a 1.8V supply. The CG achieves 74.1 dB SFDR and −70dB THD at 15.5 kHz with a 50µApk stimulation current. The chip achieves 2mΩ/√Hz input-referred impedance noise at 1Hz, 91.1 dB SNR (BW = 4 Hz), 36 kΩ input range and less than 0.48$^{circ}$ phase error (0 − 90$^{circ}$, 1 – 20 kHz). On-body BioZ experiments using a 4-electrode configuration demonstrate clear recordings of Impedance Cardiography (ICG) and respiration signals.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"908-919"},"PeriodicalIF":4.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents an implantable low-power neural stimulator that generates electrical stimulation pulses based on subject-specific edge-learning of electrode-tissue voltage profiles. The system deploys a low-magnitude constant-current stimulation pulse to create a training dataset, which is subsequently utilized to predict the desired electrode voltage waveforms for higher magnitudes of constant-current stimulation. The predicted waveform dataset has been used to control a custom switched-capacitor output stage, thereby avoiding Vdriver_transistor· Istimulation power loss as in the conventional neural stimulator drivers. The proposed system incorporates on-chip learning and prediction implemented within an ultra-low-power microcontroller, which has been optimized for memory and power-constrained implantable environments. The stimulator output stage reduces power loss by up to 20% as compared to dynamic power supply scaling method, and consumes up to 3.63× lower as compared to conventional constant-current output stages. The intelligent neural interface system has been powered by a wireless inductive energy transfer link and is remotely controlled through a WiFi-based internet network. A custom-developed application interface, compatible with both mobile devices and personal computers, facilitates secure remote adjustments of stimulation parameters. The proposed system has been validated through a combination of in vivo rat peripheral nerve stimulation, in vitro saline tests, and benchtop experiments. These results collectively demonstrate the potential to advance future neural implant technologies by enabling intelligence, safety, energy efficiency, and remotely controllable neural organ modulation.
{"title":"Energy-Efficient Adaptive Neural Stimulator With Waveform Prediction by Sub-Threshold Interrogation of the Electrode-Tissue Interface","authors":"Sudip Nag;Aryasree Remadevi;Jin Che;Matvii Prytula;Hanzhang Xing;Hanrui Xing;Xiaoxuan Xiao;Andreas Constas-Malvanets;Hengjia Zhang;Yinghe Sun;Joshua Olorocisimo;Jose Zariffa;Roman Genov","doi":"10.1109/TBCAS.2025.3570264","DOIUrl":"10.1109/TBCAS.2025.3570264","url":null,"abstract":"This paper presents an implantable low-power neural stimulator that generates electrical stimulation pulses based on subject-specific edge-learning of electrode-tissue voltage profiles. The system deploys a low-magnitude constant-current stimulation pulse to create a training dataset, which is subsequently utilized to predict the desired electrode voltage waveforms for higher magnitudes of constant-current stimulation. The predicted waveform dataset has been used to control a custom switched-capacitor output stage, thereby avoiding <italic>V</i><sub>driver_transistor</sub> <italic>· I</i><sub>stimulation</sub> power loss as in the conventional neural stimulator drivers. The proposed system incorporates on-chip learning and prediction implemented within an ultra-low-power microcontroller, which has been optimized for memory and power-constrained implantable environments. The stimulator output stage reduces power loss by up to 20% as compared to dynamic power supply scaling method, and consumes up to 3.63<italic>×</i> lower as compared to conventional constant-current output stages. The intelligent neural interface system has been powered by a wireless inductive energy transfer link and is remotely controlled through a WiFi-based internet network. A custom-developed application interface, compatible with both mobile devices and personal computers, facilitates secure remote adjustments of stimulation parameters. The proposed system has been validated through a combination of <italic>in vivo</i> rat peripheral nerve stimulation, <italic>in vitro</i> saline tests, and benchtop experiments. These results collectively demonstrate the potential to advance future neural implant technologies by enabling intelligence, safety, energy efficiency, and remotely controllable neural organ modulation.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 6","pages":"1142-1159"},"PeriodicalIF":4.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-09DOI: 10.1109/TBCAS.2025.3568754
Asish Koruprolu;Tyler Hack;Omid Ghadami;Aditi Jain;Drew A. Hall
Continuous health monitoring by placing sensors onto and into the human body has emerged as a pivotal approach in healthcare. This paper delves into the vast array of opportunities presented by instrumenting the body using wearable, ingestible, injectable, and implantable sensors. These sensors enable the continuous monitoring of vital signs, biomarkers, and other crucial health metrics, thus assessing an individual’s physiological state. This comprehensive health data empowers healthcare providers and individuals alike to make informed decisions and take timely action. Moreover, integrating sensors into the human body enables personalized medicine, enhances disease detection and management, and offers possibilities for proactive health interventions and preventive care to improve overall well-being.
{"title":"From Wearables to Implantables: Harnessing Sensor Technologies for Continuous Health Monitoring","authors":"Asish Koruprolu;Tyler Hack;Omid Ghadami;Aditi Jain;Drew A. Hall","doi":"10.1109/TBCAS.2025.3568754","DOIUrl":"10.1109/TBCAS.2025.3568754","url":null,"abstract":"Continuous health monitoring by placing sensors onto and into the human body has emerged as a pivotal approach in healthcare. This paper delves into the vast array of opportunities presented by instrumenting the body using wearable, ingestible, injectable, and implantable sensors. These sensors enable the continuous monitoring of vital signs, biomarkers, and other crucial health metrics, thus assessing an individual’s physiological state. This comprehensive health data empowers healthcare providers and individuals alike to make informed decisions and take timely action. Moreover, integrating sensors into the human body enables personalized medicine, enhances disease detection and management, and offers possibilities for proactive health interventions and preventive care to improve overall well-being.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"852-875"},"PeriodicalIF":4.9,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the practical implementations of wearable sensors, motion artifacts with large amplitudes often cause signal chain saturation, significantly degrading biopotential signal integrity. Similarly, rapid stimulation artifacts are inevitable during closed-loop brain stimulation therapy, posing additional challenges for real-time signal acquisition. To address motion and stimulation artifacts with amplitudes reaching hundreds of mV while minimizing information loss, a sensor interface with high input range and fast artifacts recovery capability is essential. This paper presents a continuous-time track-and-zoom (CT-TAZ) technique designed to handle large artifacts events without saturation. The proposed system achieves a 3.6V/1.8V differential-mode/common-mode full-scale input range. Fabricated in a 180nm CMOS process, the prototype chip occupies an area of 0.694mm2 and consumes 12/32.6/51.6$mu$W for recordings without/with single-end/with differential rail-to-rail artifacts. The system demonstrates an average artifacts recovery time of 65.3 $mu$s under 3.6V stimulation artifacts, achieving an average artifacts recovery speed of 52.3mVpp/μs, which is 2.25× larger input range and 3× faster recovery compared to the state-of-the-art.
{"title":"Artifact-Tolerant Electrophysiological Sensor Interface With 3.6V/1.8V DM/CM Input Range and 52.3mVpp/${mu}$s Recovery Using Asynchronous Signal Folding","authors":"Qiao Cai;Xinzi Xu;Yanxing Suo;Guanghua Qian;Yongfu Li;Guoxing Wang;Yong Lian;Yang Zhao","doi":"10.1109/TBCAS.2025.3567524","DOIUrl":"10.1109/TBCAS.2025.3567524","url":null,"abstract":"In the practical implementations of wearable sensors, motion artifacts with large amplitudes often cause signal chain saturation, significantly degrading biopotential signal integrity. Similarly, rapid stimulation artifacts are inevitable during closed-loop brain stimulation therapy, posing additional challenges for real-time signal acquisition. To address motion and stimulation artifacts with amplitudes reaching hundreds of mV while minimizing information loss, a sensor interface with high input range and fast artifacts recovery capability is essential. This paper presents a continuous-time track-and-zoom (CT-TAZ) technique designed to handle large artifacts events without saturation. The proposed system achieves a 3.6V/1.8V differential-mode/common-mode full-scale input range. Fabricated in a 180nm CMOS process, the prototype chip occupies an area of 0.694mm<sup>2</sup> and consumes 12/32.6/51.6<inline-formula><tex-math>$mu$</tex-math></inline-formula>W for recordings without/with single-end/with differential rail-to-rail artifacts. The system demonstrates an average artifacts recovery time of 65.3 <inline-formula><tex-math>$mu$</tex-math></inline-formula>s under 3.6V stimulation artifacts, achieving an average artifacts recovery speed of 52.3mV<sub>pp</sub>/μs, which is 2.25× larger input range and 3× faster recovery compared to the state-of-the-art.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 6","pages":"1160-1174"},"PeriodicalIF":4.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1109/TBCAS.2025.3538049
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TBCAS.2025.3538049","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3538049","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10880491","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1109/TBCAS.2025.3538047
{"title":"IEEE Transactions on Biomedical Circuits and Systems Publication Information","authors":"","doi":"10.1109/TBCAS.2025.3538047","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3538047","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10880493","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1109/TBCAS.2024.3411913
Jing Liang;Yuanqi Hu
In [1], in section III.E of the article, we calculate the equivalent tunnelling current according to equation (4) by using the value of Cg, eff as 1.679 fF, which is about 4.6 times smaller than the correct value. This leads to the wrong equivalent impedance value obtained in the final Fig. 10 is about 4.6 times larger than the correct value, and the equivalent impedance should be about 2.2 PΩ at this size, so according to the basis of the above, the article should be corrected as follows:
{"title":"Erratum to “Design of an Extreme Low Cutoff Frequency Highpass Frontend for CMOS ISFET via Direct Tunneling Principle”","authors":"Jing Liang;Yuanqi Hu","doi":"10.1109/TBCAS.2024.3411913","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3411913","url":null,"abstract":"In [1], in section III.E of the article, we calculate the equivalent tunnelling current according to equation (4) by using the value of Cg, eff as 1.679 fF, which is about 4.6 times smaller than the correct value. This leads to the wrong equivalent impedance value obtained in the final Fig. 10 is about 4.6 times larger than the correct value, and the equivalent impedance should be about 2.2 PΩ at this size, so according to the basis of the above, the article should be corrected as follows:","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"238-238"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10880511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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