IEEE transactions on biomedical circuits and systems最新文献_第5页

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。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-26 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。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-23 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":"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, 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}

引用次数: 0

A 0.48$^{circ}$ Phase Error 91.1 dB 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。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-21 DOI: 10.1109/TBCAS.2025.3572374

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

{"title":"A 0.48$^{circ}$ 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}

引用次数: 0

From Wearables to Implantables: Harnessing Sensor Technologies for Continuous Health Monitoring 从可穿戴设备到植入式设备：利用传感器技术进行持续健康监测。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-09 DOI: 10.1109/TBCAS.2025.3568754

Asish Koruprolu;Tyler Hack;Omid Ghadami;Aditi Jain;Drew A. Hall

引用次数: 0

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

IEEE transactions on biomedical circuits and systems Pub Date : 2025-02-11 DOI: 10.1109/TBCAS.2025.3538049

引用次数: 0

Guest Editorial: Ultralow-Power Technologies for Edge Computing in Human-Machine Interface Applications 嘉宾评论：人机界面应用中的边缘计算超低功耗技术

IEEE transactions on biomedical circuits and systems Pub Date : 2025-02-11 DOI: 10.1109/TBCAS.2025.3533805

Elisa Donati;Bo Zhao;Simone Benatti;Andrea Cossettini

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

IEEE transactions on biomedical circuits and systems Pub Date : 2025-02-11 DOI: 10.1109/TBCAS.2025.3538047

引用次数: 0

Erratum to “Design of an Extreme Low Cutoff Frequency Highpass Frontend for CMOS ISFET via Direct Tunneling Principle” “利用直接隧道原理设计CMOS ISFET的极低截止频率高通前端”的勘误

IEEE transactions on biomedical circuits and systems Pub Date : 2025-02-11 DOI: 10.1109/TBCAS.2024.3411913

Jing Liang;Yuanqi Hu

引用次数: 0

Real-Time Imaging Enhancement of Handheld Photoacoustic System With FeRAM Crossbar Array Based Neuromorphic Design 基于FeRAM交叉棒阵列的手持式光声系统实时成像增强神经形态设计。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-02-04 DOI: 10.1109/TBCAS.2025.3538578

Zhengyuan Zhang;Tiancheng Cao;Siyu Liu;Haoran Jin;Wensong Wang;Xiangjun Yin;Chen Liu;Wang Ling Goh;Yuan Gao;Yuanjin Zheng

{"title":"Real-Time Imaging Enhancement of Handheld Photoacoustic System With FeRAM Crossbar Array Based Neuromorphic Design","authors":"Zhengyuan Zhang;Tiancheng Cao;Siyu Liu;Haoran Jin;Wensong Wang;Xiangjun Yin;Chen Liu;Wang Ling Goh;Yuan Gao;Yuanjin Zheng","doi":"10.1109/TBCAS.2025.3538578","DOIUrl":"10.1109/TBCAS.2025.3538578","url":null,"abstract":"The miniaturization and real time imaging capability have always been the desired properties of photoacoustic imaging (PAI) system, which unlocked vast potential for personalized healthcare and diagnostics. While the imaging quality and resolution in such systems are inferior due to physics and system volume constraints, which limited its wide deployment and application. This paper proposes a novel platform to enhance the imaging quality of handheld PAI system in real time, integrating MultiResU-Net imaging enhancement algorithm with Ferroelectric random-access memory (FeRAM) crossbar array. The FeRAM crossbar array enables in memory computing, which is highly suitable for accelerating deep neural network where extensive matrix multiplications are involved. The hardware implementation of the algorithm is optimized for low-power operation on edge devices, a specifically designed algorithmic strategy is further introduced to accurately simulate the impact of hardware variation on the computation in the array with time complexity of <italic>O(mn). The feasibility and effectiveness of this method are demonstrated through simulation data (synthesized through physical model) and <italic>in vivo data, the experimental results demonstrate more than 10 times of imaging resolution improvement. The execution of neural network inference has been significantly accelerated and can be completed within a few microseconds, fully covering the imaging speed in handheld PAI system and satisfying the real time imaging capability. The whole platform can be integrated into a compact size of 25<inline-formula><tex-math>$times$</tex-math></inline-formula>25<inline-formula><tex-math>$times$</tex-math></inline-formula>20 <bold>cm3, which is a portable system with real time and high resolution imaging capability.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"1031-1044"},"PeriodicalIF":4.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545248","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 Active Microchannel Neural Interface for Implantable Electrical Stimulation and Recording 用于植入式电刺激和记录的主动微通道神经接口。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-01-27 DOI: 10.1109/TBCAS.2025.3533612

Maryam Habibollahi;Dai Jiang;Henry Thomas Lancashire;Andreas Demosthenous

{"title":"An Active Microchannel Neural Interface for Implantable Electrical Stimulation and Recording","authors":"Maryam Habibollahi;Dai Jiang;Henry Thomas Lancashire;Andreas Demosthenous","doi":"10.1109/TBCAS.2025.3533612","DOIUrl":"10.1109/TBCAS.2025.3533612","url":null,"abstract":"A mm-sized, implantable neural interface for bidirectional control of the peripheral nerves with microchannel electrodes is presented in this paper. The application-specific integrated circuit (ASIC) developed in a 0.18 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m CMOS technology is designed to achieve highly selective, concurrent control of 300-<inline-formula><tex-math>$mu$</tex-math></inline-formula>m-wide groups of small nerve sections. It has <italic>in-situ, high-voltage-compliant (45 V) electrical stimulation and low-voltage (1.8 V) neural recording in each channel. Biphasic stimulus current pulses up to 124 <inline-formula><tex-math>$mu$</tex-math></inline-formula>A, with a 2 <inline-formula><tex-math>$mu$</tex-math></inline-formula>A resolution are generated between 7.4 Hz and 20 kHz frequencies to stimulate and block neural activity. Action potentials are measured across a 10 kHz bandwidth with a variable gain response that ranges up to 72 dB. The neural recording front-end implements a low-power and low-noise biopotential amplifier with an input-referred noise (IRN) of 2.74 <inline-formula><tex-math>$mu$</tex-math></inline-formula>Vrms across the full measurement bandwidth. Automatic detection and reduction of stimulus artifacts is realised using a pole-shifting mechanism with a 1-ms amplifier recovery time. Versatile control of concurrently-operating channels is achieved in a two-channel, 2.31 mm2 interface ASIC using local control that allows up to seven devices to operate in parallel. <italic>In vitro validation of the active interface shows feasibility for closed-loop peripheral nerve control, while <italic>ex vivo analyses of concurrent stimulation and recording demonstrates the measured neural response to electrical stimuli.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"1018-1030"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545027","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