IEEE transactions on biomedical circuits and systems最新文献

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 : 2025-10-01 DOI: 10.1109/TBCAS.2025.3607505

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 : 2025-10-01 DOI: 10.1109/TBCAS.2025.3610936

引用次数: 0

A 192-Channel 1D CNN-Based Neural Feature Extractor in 65nm CMOS for Brain-Machine Interfaces. 一种用于脑机接口的192通道一维cnn神经特征提取器。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-09-29 DOI: 10.1109/TBCAS.2025.3615121

Steven Bulfer, Jorge Gamez, Albert Yan-Huang, Benyamin Haghi, Volnei Pedroni, Richard A Andersen, Azita Emami

{"title":"A 192-Channel 1D CNN-Based Neural Feature Extractor in 65nm CMOS for Brain-Machine Interfaces.","authors":"Steven Bulfer, Jorge Gamez, Albert Yan-Huang, Benyamin Haghi, Volnei Pedroni, Richard A Andersen, Azita Emami","doi":"10.1109/TBCAS.2025.3615121","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3615121","url":null,"abstract":"We present a 192-channel 1D convolutional neural network (1D CNN) based neural feature extractor for Brain-Machine Interfaces (BMI) that achieves state-of-the-art decoding stability at 1.8 $μ$W and 12801 $μ$m2 per channel in 65nm CMOS technology. Our device is a fully configurable, scalable, area and power efficient solution that supports models with 2-8 feature layers and a total kernel length of up to 256. This architecture reduces caching requirements by 5× over conventional computation schemes. Channels and layers are individually power-switchable to further optimize power efficiency for a given neural application. We introduce an on-chip model, FENet-66, that achieves the highest cross-validated decoding performance compared to all previously reported feature sets. We show that this model maintains superior stability over time using recorded data from tetraplegic human participants with spinal cord injury. Our features have 18% higher overall average cross-validated R2 decoding performance compared to Spiking Band Power (SBP), with 28% better performance during the 4th year. Our proposed architecture can also extract mean wavelet power features at low power and latency. We show that custom 1D-CNN kernels achieve 10% better performance compared to wavelet features while compressing the neural data stream by 38×. The models and hardware were validated in real time with a human subject in online closed-loop center-out cursor control experiments with micro-electrode arrays that were implanted for 6 years. Decoders using features generated with this work substantially improve the viability of longterm neural implants compared to other feature extraction methods currently present in low power BMI hardware.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145194188","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

An Energy-Efficient ECG Classifier with On-Chip Learning Using Binarized Convolutional Neural Network. 基于二值化卷积神经网络的片上学习高效心电分类器。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-09-17 DOI: 10.1109/TBCAS.2025.3610879

Rui Zhang, Ranran Zhou, Xinyi Han, Haifeng Qi, Yong Wang

{"title":"An Energy-Efficient ECG Classifier with On-Chip Learning Using Binarized Convolutional Neural Network.","authors":"Rui Zhang, Ranran Zhou, Xinyi Han, Haifeng Qi, Yong Wang","doi":"10.1109/TBCAS.2025.3610879","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3610879","url":null,"abstract":"In ECG classification applications, binarized convolutional neural networks (bCNNs) show great potential to achieve extremely low power consumption through 1-bit quantization. Existing bCNN approaches typically extract spatial features from the full ECG image without leveraging its sparsity, thereby introducing unnecessary computations and hardware resources. Meanwhile, inter-patient variability of ECG features degrades the classification performance due to accuracy loss caused by the binarization operation. To address these challenges, this paper proposes an energy-efficient ECG classifier based on a bCNN with on-chip learning. A patch-by-patch computation approach is used to reduce both power consumption and memory usage. Instead of processing the entire image, the ECG image is divided into small patches, and only the patches containing valid data are involved in feature extraction. An on-chip learning method is employed to improve classification accuracy among patients by updating the model weights using both the acquired bCNN features and the R-peak interval data. In addition, a reconfigurable convolutional processing element array and a base-2 softmax structure are designed to further reduce the hardware resources. The proposed classifier is verified on an FPGA, achieving a classification accuracy of 97.55% and a specificity of 89.15%. Synthesized using a 55 nm CMOS process, the ECG classifier occupies an area of 0.43 mm2. With a supply voltage of 1.2 V, the classifier consumes an average energy of 0.12 $μ$J per classification and 0.09 $μ$J per on-chip learning, making it suitable for wearable ECG classification application.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145082831","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 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

Fully Wireless ASIC with MagSonic Operation Using Magnetoelectric Transducer for Neural Stimulation and Recording. 使用磁电换能器进行神经刺激和记录的全无线专用集成电路。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-08-13 DOI: 10.1109/TBCAS.2025.3598568

Sujay Hosur, Hyunjin Lee, Tao Zhou, Mehdi Kiani

{"title":"Fully Wireless ASIC with MagSonic Operation Using Magnetoelectric Transducer for Neural Stimulation and Recording.","authors":"Sujay Hosur, Hyunjin Lee, Tao Zhou, Mehdi Kiani","doi":"10.1109/TBCAS.2025.3598568","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3598568","url":null,"abstract":"A wireless application-specific integrated circuit (ASIC), operating with the MagSonic modality using one magnetoelectric (ME) transducer, is presented for neural stimulation and recording. The ASIC integrates a bridge circuit that forms both power management and data transmitter with voltage doubling, rectification, regulation, and over voltage protection, a biphasic AC stimulator with high voltage tolerance and direct external control simplifying downlink complexities and on-chip processing overhead, an active charge balancing circuit adjusting the duration of second stimulation phase, and a continuous neural recording and uplink communication. The prototype MagSonic ASIC was fabricated in a 180 nm standard CMOS process (2×1.75 mm2 total area) and requires only one ME transducer and an external storage capacitor to operate. In measurements, a bar shaped millimeter-scale ME transducer (5.1×2.29×1.69 mm3) with length mode operation at 330 kHz was used to power the ASIC, achieving up to 8.1 mW of received power at 40 mm depth. The biphasic AC stimulator occupying only 0.027 mm2 of active chip area provided 6.6 V (2×VDD) tolerance (using 3.3 V transistors) with residual electrode voltage of < 50 mV. The amplified signals were converted into time using an analog-to-time converter and transmitted at a data rate of 186.2 kbps (< 10-3 BER) using the ME transducer's thickness mode frequency (1.66 MHz). Animal experiment results demonstrate the feasibility of ASIC's direct AC stimulation.","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":"144850104","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