IEEE transactions on biomedical circuits and systems最新文献

Regulating 3D Magnetic Flux Density for Stable Wireless Power Transfer in a Compact Planar Charger for Capsule Endoscopy. 用于胶囊内窥镜的紧凑平面充电器调节三维磁通密度以实现稳定的无线电力传输。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-06-19 DOI: 10.1109/TBCAS.2025.3581526

Heng Zhang, Zheng Li, Chi-Kwan Lee

{"title":"Regulating 3D Magnetic Flux Density for Stable Wireless Power Transfer in a Compact Planar Charger for Capsule Endoscopy.","authors":"Heng Zhang, Zheng Li, Chi-Kwan Lee","doi":"10.1109/TBCAS.2025.3581526","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3581526","url":null,"abstract":"Wireless charging for small electronic devices remains a significant challenge, especially for applications that demand high-performance operation, such as wearable electronics and medical devices. Many compact devices, including smart-watches and capsule endoscopes, often suffer from limited battery life and frequent recharging requirements. To address these issues, this paper proposes a compact, planar, omnidirectional wireless power transmitter implemented on a multilayer printed circuit board. The proposed design achieves stable wireless charging across varying positions and orientations while maintaining a portable form factor that enables convenient use in diverse settings. To mitigate control challenges arising from overlapping transmitter coils in the planar configuration, a current source inverter is integrated with an LCCL compensation network. Comprehensive mathematical modeling is developed to provide design insights, and the system performance is further validated through computer simulations. In addition, we propose a robust wireless charging algorithm that maintains stable performance under arbitrary spatial positions and orientations, as evidenced by experimental tests demonstrating a mean receiving current fluctuation of only 2.16 mA. Moreover, in capsule endoscopy scenarios, the system achieved an effective charging performance with a maximum transmission power of 1904.4 mW, underscoring its competitiveness with current state-of-the-art designs.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334645","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}

引用次数: 0

A Fast Electrochemical Impedance Spectroscopy with A Square Wave as Excitation Signal for Impedance-based Biomedical Applications. 基于阻抗的生物医学应用中以方波为激励信号的快速电化学阻抗谱。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-06-18 DOI: 10.1109/TBCAS.2025.3579698

Zhongzheng Wang, Han Shao, Alan O Riordan, Javier Higes-Marquez, Ivan O Connell, Daniel O Hare

{"title":"A Fast Electrochemical Impedance Spectroscopy with A Square Wave as Excitation Signal for Impedance-based Biomedical Applications.","authors":"Zhongzheng Wang, Han Shao, Alan O Riordan, Javier Higes-Marquez, Ivan O Connell, Daniel O Hare","doi":"10.1109/TBCAS.2025.3579698","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3579698","url":null,"abstract":"This paper introduces a fast, high-accuracy methodology for conducting Electrochemical Impedance Spectroscopy (EIS) based on Fast Fourier Transform (FFT), to meet the requirements of portable, real-time biomedical impedance-based detections with Ultra-Microband (UMB) sensor. Instead of using white noise-like wideband signals as in conventional FFT-based EIS, the proposed method uses a square wave as the excitation signal, which achieves a fast, accurate EIS measurement, but no longer requires complex circuits like high-resolution DACs or frequency mixers for the signal generation. This work starts with the theoretical justification for treating the sensor as a Linear Time-Invariant (LTI), then the practical linear region for operating the sensor as an LTI system is experimentally verified and determined, which enables the capacity of employing the harmonics of a square wave for EIS measurements. A dynamic model of the charge-transfer resistance together with an approximated of the Constant Phase Element (CPE) are implemented with Verilog-A for simulations, and a circuit consisting of a control amplifier and a Trans-Impedance Amplifier (TIA) is designed and fabricated with 65 nm CMOS for validating its on-chip feasibility. This work shortens the EIS measurement time by 91.7% in a frequency sweep range from 0.5 Hz to 500 Hz, with only 2.73% average Mean Absolute Percentage Error (MAPE), compared to a commercial electrochemical instrument AutoLab, with five pre-modified electrodes across four different concentrations of Ferrocene Carboxylic Acid (FcCOOH), demonstrating this method is suitable for portable, real-time label-free EIS biomedical detections and applications.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328223","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}

引用次数: 0

MulPi: A Multi-class and Patient-independent Epileptic Seizure Classifier with Co-designed Input-stationary Computing-in-SRAM. MulPi：一种多类别、独立于患者的癫痫发作分类器，在sram中协同设计输入静止计算。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-06-13 DOI: 10.1109/TBCAS.2025.3579273

Bokyung Kim, Qijia Huang, Brady Taylor, Qilin Zheng, Jonathan Ku, Yiran Chen, Hai Li

{"title":"MulPi: A Multi-class and Patient-independent Epileptic Seizure Classifier with Co-designed Input-stationary Computing-in-SRAM.","authors":"Bokyung Kim, Qijia Huang, Brady Taylor, Qilin Zheng, Jonathan Ku, Yiran Chen, Hai Li","doi":"10.1109/TBCAS.2025.3579273","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3579273","url":null,"abstract":"Unprovoked seizures have threatened epilepsy patients over 70 million. Automated classification to detect and predict seizures could bring seizure-free lives to epilepsy patients, delivering them from fatal danger and increasing the quality of life. Authentic detection and prediction of seizures require 1) multi-class (Mul) and 2) patient-independent (Pi) classification. Recent implementable chips for seizure classification rarely satisfy the two requirements due to restricted resources in small chips; therefore, high efficiency is imperative along with accuracy. This paper introduces an efficient MulPi chip, fabricated for the first time to simultaneously fulfill multi-class and patient independence, based on a co-design approach. We develop a 5-layer convolutional neural network (CNN), MulPiCNN, with advanced training techniques for lightness and accuracy. At the hardware level, our SRAM-based chip leverages computingin- memory (CIM) for efficiency. The fabricated MulPi chip is distinguished from prior CIMs in two folds, namely ISRW-CIM: a) input-stationary (IS) CIM for resource-saving, and b) rowwise (RW) computing to address a challenge of SRAM CIM, empowered by our novel 2T-Hadamard product unit (HPU). MulPi outperforms state-of-the-art chips with 98.5% sensitivity and 99.2% specificity, classifying in 0.12s and 0.348mm2.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289777","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}

引用次数: 0

Implantable Cardiovascular Biopotential Acquisition and Stimulation Circuit with Body-Channel Communication for Transcatheter Leadless Pacemaker. 经导管无铅起搏器体内通道通信的植入式心血管生物电位获取和刺激电路。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-06-12 DOI: 10.1109/TBCAS.2025.3579065

Manhyuck Choi, Byeongseol Kim, Sangmin Lee, Kyounghwan Kim, Mookyoung Yoo, Jihyang Wi, Gibae Nam, Minhyeok Son, Inju Yoo, Joonsung Bae, Hyoungho Ko

{"title":"Implantable Cardiovascular Biopotential Acquisition and Stimulation Circuit with Body-Channel Communication for Transcatheter Leadless Pacemaker.","authors":"Manhyuck Choi, Byeongseol Kim, Sangmin Lee, Kyounghwan Kim, Mookyoung Yoo, Jihyang Wi, Gibae Nam, Minhyeok Son, Inju Yoo, Joonsung Bae, Hyoungho Ko","doi":"10.1109/TBCAS.2025.3579065","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3579065","url":null,"abstract":"This paper presents an implantable cardiovascular biopotential acquisition and stimulation circuit with body-channel (BC) data communication and power transfer capabilities for a transcatheter leadless pacemaker. The power and size requirements of leadless pacemakers, specifically for implantable electronics and minimally-invasive transcatheter delivery, are highly challenging. To reduce size, electrocardiogram (ECG) sensing, pacing, timing and control logic, and body- coupled wireless transceivers are integrated into a single chip. The ECG sensing channel is designed using a current-reused current-feedback instrumentation amplifier to reduce power consumption. The pacing circuit is implemented using a switched-capacitor stimulator with passive discharge for high stimulation efficiency. The pacemaker utilizes BC communication instead of RF communication to achieve low power consumption. The measured input-referred noise of the sensing channel is 3.69 μVRMS, and the power consumption ranges from 4.5 to 19.4 μW. The downlink and uplink speeds of BC communication are 10 Mbps and 16 kbps, respectively. The internal rechargeable battery is properly charged when a 600 mVPP, 20 MHz input signal is applied. The leadless pacemaker prototype is implemented with a small size of 5.89 mm and 26.5 mm in diameter and length, respectively. The performance of the leadless pacemaker prototype is evaluated through in vivo experiments using swine.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287652","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}

引用次数: 0

System-on-Chip for Flow Cytometry with Impedance Measurement and Integrated Real-time Size Classification. 片上系统流式细胞仪与阻抗测量和集成的实时尺寸分类。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-06-04 DOI: 10.1109/TBCAS.2025.3576317

Tzu-Hsuan Chou, Siyuan Yu, Calder Wilson, Jacob Dawes, Jaehyeong Park, Louis Marun, Matthew L Johnston

{"title":"System-on-Chip for Flow Cytometry with Impedance Measurement and Integrated Real-time Size Classification.","authors":"Tzu-Hsuan Chou, Siyuan Yu, Calder Wilson, Jacob Dawes, Jaehyeong Park, Louis Marun, Matthew L Johnston","doi":"10.1109/TBCAS.2025.3576317","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3576317","url":null,"abstract":"This paper presents an impedance measurement system-on-chip (SoC) for flow cytometry (i.e. cell counting) applications. A source-differential, three-electrode sensing scheme is used in a microfluidic flow cell for particle detection. At the front-end, a lock-in amplifier architecture is used, including a high-gain TIA with 60MHz bandwidth, passive mixers, and lowpass filters. The ac sensor signal is demodulated to extract inphase (I) and quadrature (Q) baseband components to measure complex impedance. At the back-end, the SoC includes an 8-bit level-crossing ADC (LCADC) for digitizing I/Q signals, followed by real-time digital feature extraction and linear classification for real-time cell size determination. The SoC was fabricated in a 180nm CMOS process. A measured prototype IC achieves 733 fA/$sqrt {Hz}$ noise floor and 23 pArms input-referred noise from 1-1 kHz. Combined with a microfludic flow cell, polymer beads in solution were used as cell surrogates to demonstrate particle counting. Measured results for particle diameters of 10 μm, 6 μm, 4.5 μm and 3 μm are shown. Following offline training, the SoC demonstrated on-chip classification of 4.5 μm and 6 μm beads with a prediction accuracy of 86.16% with pre-recorded data, and 73.6% while performing real-time inline classification.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144228062","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}

引用次数: 0

Wearable Epilepsy Seizure Detection on FPGA with Spiking Neural Networks. 基于脉冲神经网络的FPGA可穿戴癫痫发作检测。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-05-30 DOI: 10.1109/TBCAS.2025.3575327

Paola Busia, Gianluca Leone, Andrea Matticola, Luigi Raffo, Paolo Meloni

{"title":"Wearable Epilepsy Seizure Detection on FPGA with Spiking Neural Networks.","authors":"Paola Busia, Gianluca Leone, Andrea Matticola, Luigi Raffo, Paolo Meloni","doi":"10.1109/TBCAS.2025.3575327","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3575327","url":null,"abstract":"The development of epilepsy monitoring solutions suitable for everyday use is a very challenging task, where different constraints should be combined, resulting from the required accuracy standards, the unobtrusiveness of the monitoring device, and the efficiency of real-time operation. Considering the time-varying nature of the electroencephalography signal (EEG), Spiking Neural Networks (SNNs) represent a promising solution to model the evolution of the brain state based on the history of the previously processed signal. This work proposes an extremely lightweight SNN-based seizure detection solution, utilizing a simple encoding scheme to ensure high levels of sparsity. Despite the reduced complexity, the model provides a detection performance comparable with the state-of-the-art SNN-based approaches on the evaluated data from the CHB-MIT dataset, reaching a 96% area under the curve (AUC) and allowing 99.3% average accuracy, with the detection of 100% of the examined seizure events and a false alarm rate of 0.3 false positives per hour. The suitability for real-time inference execution on wearable monitoring devices was assessed on SYNtzulu, demonstrating 0.5 μs inference time with 4.55 nJ energy consumption.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188720","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}

引用次数: 0

A 96 dB Input Dynamic Range Galvanic Skin Response Readout IC with 3.5 pArms Input-Referred Noise for Mental Stress Monitoring. 一种96 dB输入动态范围皮肤电响应读出IC， 3.5 pArms输入参考噪声，用于精神压力监测。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-05-26 DOI: 10.1109/TBCAS.2025.3573614

Yi-Jie Lin, Lin Chou, Kun-Ju Tsai, Yu-Te Liao

引用次数: 0

A Wearable Ultra-Low-Power System for EEG-based Speech-Imagery Interfaces. 基于脑电图语音图像接口的可穿戴超低功耗系统。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-05-23 DOI: 10.1109/TBCAS.2025.3573027

Thorir Mar Ingolfsson, Victor Kartsch, Luca Benini, Andrea Cossettini

{"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":"https://doi.org/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 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.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-23","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}

引用次数: 0

A 0.48° Phase Error 91.1dB SNR Bioimpedance Measurement IC for Monitoring Cardiopulmonary Diseases. 用于心肺疾病监测的0.48°相位误差91.1dB信噪比生物阻抗测量芯片。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-05-21 DOI: 10.1109/TBCAS.2025.3572374

Jiarun Yuan, Yanxing Suo, Qiao Cai, Hui Wang, Yongfu Li, Yong Lian, Yang Zhao

{"title":"A 0.48° Phase Error 91.1dB 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":"https://doi.org/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 0.73mm2 and consumes between 52.7 to 97.5μA current from a 1.8V supply. The CG achieves 74.1dB SFDR and -70dB THD at 15.5kHz with a 50μApk stimulation current. The chip achieves $2 text{m} Omega / sqrt{} Hz$ input-referred impedance noise at 1Hz, 91.1dB SNR (BW=4Hz), $36 text{k} Omega$ input range and less than 0.48° phase error (0-90°, 1-20kHz). 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":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","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}

引用次数: 0

Energy-Efficient Adaptive Neural Stimulator with Waveform Prediction by Sub-Threshold Interrogation of the Electrode-Tissue Interface. 基于电极-组织界面亚阈值查询的高能效自适应神经刺激器。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-05-21 DOI: 10.1109/TBCAS.2025.3570264

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

{"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":"https://doi.org/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 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.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","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}

引用次数: 0