IEEE transactions on biomedical circuits and systems最新文献

Wearable stimulator for upper and lower limb somatotopic sensory feedback restoration. 用于上肢和下肢体位感觉反馈恢复的穿戴式刺激器。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-09-09 DOI: 10.1109/TBCAS.2025.3607203

Roberto Paolini, Riccardo Collu, Laura Tullio, Andrea Demofonti, Alessia Scarpelli, Francesca Cordella, Massimo Barbaro, Loredana Zollo

{"title":"Wearable stimulator for upper and lower limb somatotopic sensory feedback restoration.","authors":"Roberto Paolini, Riccardo Collu, Laura Tullio, Andrea Demofonti, Alessia Scarpelli, Francesca Cordella, Massimo Barbaro, Loredana Zollo","doi":"10.1109/TBCAS.2025.3607203","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3607203","url":null,"abstract":"Neuroprostheses capable of providing Somatotopic Sensory Feedback (SSF) enables the restoration of tactile sensations in amputees, thereby enhancing prosthesis embodiment, object manipulation, balance and walking stability. Transcutaneous Electrical Nerve Stimulation (TENS) represents a primary noninvasive technique for eliciting somatotopic sensations. Devices commonly used to evaluate the effectiveness of TENS stimulation are often bulky and main powered. However, current portable TENS devices frequently fall short of key functional requirements, particularly in terms of stimulation parameter ranges that are insufficient to reliably evoke somatotopic sensations in either upper and lower limb applications. Moreover, they typically do not support real-time independent channels programming and wireless communication. This work introduces a compact, wearable stimulator, including its external casing, with a total weight of 64 g and dimensions of 70 % 40 % 35 mm, designed to deliver SSF in both upper and lower limb applications. The device was validated through bench testing and human trials involving 20 healthy participants, by comparing the intensity, qualitative characteristics, and referred area of the elicited sensations with those produced by a benchmark. The stimulator reliably delivered the required parameters on a skin-like capacitive-resistive load and elicited somatotopic sensations consistent with the benchmark device and prior somatotopic feedback studies. The proposed stimulator provides non-invasive somatotopic sensory feedback for both upper and lower limbs. Its portability and modular design address key limitations of current commercial and research-grade TENS systems, enabling future studies on the functional benefits of sensory feedback in prosthetic control.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145031543","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 Sparse-Integrated Filtering Residual Spiking Neural Network for High-Accuracy Spike Sorting and Co-optimization on Memristor Platforms. 忆阻器平台上高精度尖峰排序与协同优化的稀疏集成滤波残差尖峰神经网络。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-08-22 DOI: 10.1109/TBCAS.2025.3601403

Yiwen Zhu, Jingyi Chen, Lingli Cheng, Fangduo Zhu, Xumeng Zhang, Qi Liu

{"title":"A Sparse-Integrated Filtering Residual Spiking Neural Network for High-Accuracy Spike Sorting and Co-optimization on Memristor Platforms.","authors":"Yiwen Zhu, Jingyi Chen, Lingli Cheng, Fangduo Zhu, Xumeng Zhang, Qi Liu","doi":"10.1109/TBCAS.2025.3601403","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3601403","url":null,"abstract":"Brain-computer interfaces rely on precise decoding of neural signals, where spike sorting is a critical step to extract individual neuronal activities from complex neural data. This works presents a spiking neural network (SNN) framework for efficient spike sorting, named SIFT-RSNN. In the SIFT-RSNN, raw neural signals are encoded into spike trains using a threshold-based temporal encoding strategy, then a sparse-integrated filtering module refines misfiring spikes, enhancing data sparsity for pattern learning. The RSNN module with a membrane shortcut structure ensures efficient feature transfer and improves generalization performance of the overall system. The SIFT-RSNN achieves an accuracy of 96.2% and 99.6% on the Difficult1 and Difficult2 subset of Leicester dataset, surpassing state-of-the-art methods. Also, we conducted it on a compute-in-memory platform with 8k memristor cells utilizing quantization-free mapping method and propose two algorithm-hardware co-optimization strategies to mitigate non-ideal hardware effects: weight outlier pre-constraint (WOP) and noise adaptation training (NAT). After optimization, our algorithm continues to outperform existing spike sorting methods, achieving accuracies of 94.2% and 99.7%, while also demonstrating improved robustness. The memristor platform only exhibits a 2% and 1.5% accuracy drop compared to software results on the two difficult subsets. Additionally, it achieves 3.52 μJ energy consumption and 0.5 ms latency per inference. This work offers promising solutions for brain-computer interfaces systems and neural prosthesis applications in the future.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984004","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

MRDust: Wireless Implant Data Uplink & Localization via Magnetic Resonance Image Modulation. MRDust：通过磁共振图像调制的无线植入数据上行和定位。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-08-13 DOI: 10.1109/TBCAS.2025.3598682

Biqi Rebekah Zhao, Alexander Chou, Robert Peltekov, Elad Alon, Chunlei Liu, Rikky Muller, Michael Lustig

{"title":"MRDust: Wireless Implant Data Uplink & Localization via Magnetic Resonance Image Modulation.","authors":"Biqi Rebekah Zhao, Alexander Chou, Robert Peltekov, Elad Alon, Chunlei Liu, Rikky Muller, Michael Lustig","doi":"10.1109/TBCAS.2025.3598682","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3598682","url":null,"abstract":"Magnetic resonance imaging (MRI) exhibits rich and clinically useful endogenous contrast mechanisms, which can differentiate soft tissues and are sensitive to flow, diffusion, magnetic susceptibility, blood oxygenation level, and more. However, MRI sensitivity is ultimately constrained by Nuclear Magnetic Resonance (NMR) physics, and its spatiotemporal resolution is limited by SNR and spatial encoding. On the other hand, miniaturized implantable sensors offer highly localized physiological information, yet communication and localization can be challenging when multiple implants are present. This paper introduces the MRDust, an active \"contrast agent\" that integrates active sensor implants with MRI, enabling the direct encoding of highly localized physiological data into MR images to augment the anatomical images. MRDust employs a micrometer-scale on-chip coil to actively modulate the local magnetic field, enabling MR signal amplitude and phase modulation for digital data transmission. Since MRI inherently captures the anatomical tissue structure, this method has the potential to enable simultaneous data communication, localization, and image registration with multiple implants. This paper presents the underlying physical principles, design tradeoffs, and design methodology for this approach. To validate the concept, a 900 × 990 µm2 chip was designed using TSMC 28 nm technology, with an on-chip coil measuring 630 µm in diameter. The chip was tested with custom hardware in an MR750W GE3T MRI scanner. Successful voxel amplitude modulation is demonstrated with Spin-Echo Echo-Planar-Imaging (SE-EPI) sequence, achieving a contrast-to-noise ratio (CNR) of 25.58 with a power consumption of 130 µW.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850105","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

Special Section on Selected Papers From IEEE BioCAS 2024 IEEE BioCAS 2024论文精选专题

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-08-05 DOI: 10.1109/TBCAS.2025.3590820

Sohmyung Ha;Hossein Kassiri;Milin Zhang;Andrea Cossettini

引用次数: 0

IEEE Circuits and Systems Society Information IEEE电路与系统学会信息

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-08-05 DOI: 10.1109/TBCAS.2025.3590819

引用次数: 0

IEEE Transactions on Biomedical Circuits and Systems Publication Information IEEE生物医学电路和系统汇刊信息

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-08-05 DOI: 10.1109/TBCAS.2025.3576469

引用次数: 0

Erratum to “A 43.5dB Gain Unipolar a-IGZO TFT Amplifier with Parallel Bootstrap Capacitor for Bio-signals Sensing Applications” “用于生物信号传感应用的带并联自引导电容的43.5dB增益单极A - igzo TFT放大器”的校误

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-08-05 DOI: 10.1109/TBCAS.2025.3583095

Mingjian Zhao;Laiqing Li;Rui Liu;Bin Li;Rongsheng Chen;Zhaohui Wu

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。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-08-01 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":"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 computing-in-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) row-wise (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 0.348mm${}^{2}$.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":"756-766"},"PeriodicalIF":4.9,"publicationDate":"2025-08-01","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

A 40.68-MHz Fully-Integrated Voltage/Current-Mode Dual-Output PMU for Wireless Neural Implants. 用于无线神经植入物的40.68 mhz全集成电压/电流模式双输出PMU。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-07-22 DOI: 10.1109/TBCAS.2025.3591228

Yi-Han Ou-Yang, Ronald Wijermars, Pyungwoo Yeon, Tianqi Lu, Amin Arbabian, Wouter A Serdijn, Sijun Du, Dante G Muratore

{"title":"A 40.68-MHz Fully-Integrated Voltage/Current-Mode Dual-Output PMU for Wireless Neural Implants.","authors":"Yi-Han Ou-Yang, Ronald Wijermars, Pyungwoo Yeon, Tianqi Lu, Amin Arbabian, Wouter A Serdijn, Sijun Du, Dante G Muratore","doi":"10.1109/TBCAS.2025.3591228","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3591228","url":null,"abstract":"This paper presents a fully-integrated single-input dual-output power management unit operating both in voltage/ current modes for powering mm-scale wireless neural implants. The chip operates in voltage mode most of the time, using an active full-wave rectifier to regulate a low-voltage, high-load output with high power efficiency and low output ripple (<32 mVpp). It switches to current mode rectification when generating a high-voltage, low-load output. This dual-mode operation allows for flexible power distribution and configurable voltage ratios between the two outputs. The selected 40.68 MHz operating frequency reduces the required capacitances for input impedance matching and output filtering, enabling on-chip integration; the only external component is the receiver coil. A novel resonance breakup switch compatible with full-wave rectification ensures a smooth cold start-up of the chip without any external voltage supply. The chip was fabricated using 40-nm CMOS technology with an active area of 1.18 mm2and was tested in a wireless power link. Measurement results demonstrate that the chip can simultaneously regulate two outputs, $V_{LV} = text{1 V}$ and $V_{HV} = text{2 V}$, with a tested maximum output power of 10 mW and 32.6 μW on $V_{LV}$ and $V_{HV}$ , respectively. At the optimal output power condition $(P_{LV} = 4.4 sim 6.7, text{mW})$, the system achieves a peak power conversion efficiency of 85.87% and a peak end-to-end efficiency of 17.32% when regulating $V_{LV}$. The end-to-end efficiency drops by only 2.38% when regulating both outputs with $R_{LV} = 225 Omega$ and $R_{HV} = 400 ,text{k}Omega$.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692833","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

Advances and Challenges in Integrated Circuits for Electrochemical Sensing: Enabling Next-Generation Biomedical and Molecular Applications. 电化学传感集成电路的进展与挑战：实现下一代生物医学和分子应用。

IEEE transactions on biomedical circuits and systems Pub Date : 2025-07-14 DOI: 10.1109/TBCAS.2025.3589027

Qiuyang Lin, Sander Crols, Aurojyoti Das, Marcel Zevenbergen, Wim Sijbers, Nick Van Helleputte, Carolina Mora Lopez

{"title":"Advances and Challenges in Integrated Circuits for Electrochemical Sensing: Enabling Next-Generation Biomedical and Molecular Applications.","authors":"Qiuyang Lin, Sander Crols, Aurojyoti Das, Marcel Zevenbergen, Wim Sijbers, Nick Van Helleputte, Carolina Mora Lopez","doi":"10.1109/TBCAS.2025.3589027","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3589027","url":null,"abstract":"This manuscript provides a comprehensive review of the design, implementation, and advancements in integrated circuits (ICs) for electrochemical sensing, with a focus on biomedical and molecular applications. It begins by discussing the fundamental principles of electrochemical sensing and core modalities, including potentiometry, amperometry, impedimetry, and ISFET-based sensing, highlighting their unique requirements and challenges. A detailed analysis of state-of-the-art readout circuit architectures is presented, emphasizing strategies for achieving high dynamic range (DR), low noise, and enhanced stability while minimizing leakage currents. Both resistive and capacitive transimpedance amplifiers (TIAs) and current conveyor (CC)-based circuits are examined, exploring critical trade-offs between speed, power consumption, and noise performance. This review also discusses emerging applications such as DNA sequencing and molecular sensing, covering both ISFET and nanopore-based approaches, to showcase recent advancements in high-throughput, high-speed, and low-power interface circuit designs. By highlighting the challenges of the readout-circuit miniaturization, integration, and scalability, as well as the current limitations in existing approaches, this review provides a comprehensive synthesis of advancements in high-performance electrochemical readout architectures and their potential to address the evolving demands of modern biomedical applications.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144639078","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