IEEE transactions on biomedical circuits and systems最新文献

{"title":"An Ultrasonic Transceiver for Non-Invasive Intracranial Pressure Sensing","authors":"Gerald Topalli;Yingying Fan;Matt Y. Cheung;Ashok Veeraraghavan;Mohammad Hirzallah;Taiyun Chi","doi":"10.1109/TBCAS.2024.3481414","DOIUrl":"10.1109/TBCAS.2024.3481414","url":null,"abstract":"This paper presents a 9-mW ultrasonic through-transmission transceiver (TRX) for portable, non-invasive intracranial pressure (ICP) sensing. It employs two ultrasound transducers placed at the temporal bone windows to measure changes in the ultrasonic time-of-flight (ToF), based on which the skull expansion and the corresponding ICP waveform are derived. Key components include a high-efficiency Class-DE power amplifier (PA) with 95% efficiency and an output swing of 15.8 \u0000<inline-formula><tex-math>$V_{PP}$</tex-math></inline-formula>\u0000, along with a successive approximation register (SAR) delay-locked loop (DLL)-based time-to-digital converter (TDC) with 29.8 ps resolution and 122 ns range. Other than electrical characterization, the sensor is validated through two demonstrations using a water tank setup and a human head phantom setup, respectively. It demonstrates a high correlation of \u0000<inline-formula><tex-math>$R^{2}=0.93$</tex-math></inline-formula>\u0000 with a medical-grade invasive ICP sensor. The proposed system offers high accuracy, low power consumption, and reliable performance, making it a promising solution for real-time, portable, non-invasive ICP monitoring in various clinical settings.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 6","pages":"1220-1232"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484140","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}

{"title":"BrainForest: Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classification Processor","authors":"Gerard O’Leary;Jamie Koerner;Mustafa Kanchwala;Jose Sales Filho;Jianxiong Xu;Taufik A. Valiante;Roman Genov","doi":"10.1109/TBCAS.2024.3481160","DOIUrl":"10.1109/TBCAS.2024.3481160","url":null,"abstract":"Personalized brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Medical implants that deliver therapeutic stimulation in response to detected seizures have already been deployed for the treatment of epilepsy. These devices require low-power integrated circuits for life-long operation. This constraint impedes the integration of machine-learning driven classifiers that could improve treatment outcomes. This paper introduces BrainForest, a neuromorphic multiplier-less bit-serial weight-memory-optimized brain-state classification processor. The architecture achieves state-of-the-art energy efficiency using two layers of neuron models to implement the spectral and temporal functions needed for classification: 1) resonate-and-fire neurons are used to extract physiological signal band energy EEG biomarkers 2) leaky integrator neurons are used to build multi-timescale representations for classification. Sparse neural model firing activity is used to clock-gate device logic, thereby decreasing power consumption by 93%. An energy-optimized 1024-tree boosted decision forest performs the classification used to trigger stimulation in response to detected pathological brain states. The IC is implemented in 65nm CMOS with state-of-the-art power consumption (best case: 9.6µW, typical: 118µW), achieving a seizure sensitivity of 97.5% with a false detection rate of 2.08 per hour.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"55-67"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484141","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}

{"title":"A Memristive Spiking Neural Network Circuit for Bio-inspired Navigation Based on Spatial Cognitive Mechanisms.","authors":"Zhanfei Chen, Xiaoping Wang, Zilu Wang, Chao Yang, Tingwen Huang, Jingang Lai, Zhigang Zeng","doi":"10.1109/TBCAS.2024.3480272","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3480272","url":null,"abstract":"<p><p>Cognitive navigation, a high-level and crucial function for organisms' survival in nature, enables autonomous exploration and navigation within the environment. However, most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation, capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking; the action cell module determines the direction of subsequent movement; and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application, the entire SNN is quantized and deployed on a real memristive hardware platform, achieving about a 21× reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484139","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}

{"title":"GAPses: Versatile smart glasses for comfortable and fully-dry acquisition and parallel ultra-low-power processing of EEG and EOG.","authors":"Sebastian Frey, Mattia Alberto Lucchini, Victor Kartsch, Thorir Mar Ingolfsson, Andrea Helga Bernardi, Michael Segessenmann, Jakub Osieleniec, Simone Benatti, Luca Benini, Andrea Cossettini","doi":"10.1109/TBCAS.2024.3478798","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3478798","url":null,"abstract":"<p><p>Recent advancements in head-mounted wearable technology are revolutionizing the field of biopotential measurement, but the integration of these technologies into practical, user-friendly devices remains challenging due to issues with design intrusiveness, comfort, reliability, and data privacy. To address these challenges, this paper presents GAPSES, a novel smart glasses platform designed for unobtrusive, comfortable, and secure acquisition and processing of electroencephalography (EEG) and electrooculography (EOG) signals.We introduce a direct electrode-electronics interface within a sleek frame design, with custom fully dry soft electrodes to enhance comfort for long wear. The fully assembled glasses, including electronics, weigh 40 g and have a compact size of 160 mm × 145 mm. An integrated parallel ultra-low-power RISC-V processor (GAP9, Greenwaves Technologies) processes data at the edge, thereby eliminating the need for continuous data streaming through a wireless link, enhancing privacy, and increasing system reliability in adverse channel conditions. We demonstrate the broad applicability of the designed prototype through validation in a number of EEG-based interaction tasks, including alpha waves, steady-state visual evoked potential analysis, and motor movement classification. Furthermore, we demonstrate an EEG-based biometric subject recognition task, where we reach a sensitivity and specificity of 98.87% and 99.86% respectively, with only 8 EEG channels and an energy consumption per inference on the edge as low as 121 μJ. Moreover, in an EOG-based eye movement classification task, we reach an accuracy of 96.68% on 11 classes, resulting in an information transfer rate of 94.78 bit/min, which can be further increased to 161.43 bit/min by reducing the accuracy to 81.43%. The deployed implementation has an energy consumption of 40 μJ per inference and a total system power of only 12.4 mW, of which only 1.61% is used for classification, allowing for continuous operation of more than 22 h with a small 75 mAh battery.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402471","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}

{"title":"78.8 pJ/b, 100 Mb/s Noncoherent IR-UWB Receiver for Multichannel Neurorecording Implants.","authors":"Razieh Eskandari, Mohamad Sawan","doi":"10.1109/TBCAS.2024.3471818","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3471818","url":null,"abstract":"<p><p>In this article, we present a novel approach for designing a low-power, low-area impulse radio ultra-wideband (IR-UWB) noncoherent receiver capable of achieving a data rate of 100 Mbps. Our proposed receiver demonstrates the ability to demodulate ON-OFF keying pulse streams across the entire lower frequency band defined by the Federal Communication Commission for UWB applications. The key components of the proposed receiver include a reconfigurable differential two-stage low-noise amplifier, a fully differential squarer, narrow-band interface rejection filters, and variable gain baseband amplifiers. These circuits work cohesively to ensure efficient signal reception and processing. To validate the performance of the proposed receiver, we implemented the design using TSMC 40-nm CMOS process technology. A short-range communication including a 1.5 cm tissue layer is tested utilizing a typical upconversion UWB transmitter fabricated in the same technology. Remarkably, the proposed receiver achieves a data rate of 100 Mbps with an impressively low energy efficiency of 78.8 pJ/b and occupies an area of 0.705 mm². The compact size, remarkable energy efficiency, and high data rate capabilities of the proposed receiver meet the stringent requirements of neural recording implants.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368028","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}

{"title":"EPOC: A 28-nm 5.3 pJ/SOP Event-driven Parallel Neuromorphic Hardware with Neuromodulation-based Online Learning.","authors":"Faquan Chen, Qingyang Tian, Lisheng Xie, Yifan Zhou, Ziren Wu, Liangshun Wu, Rendong Ying, Fei Wen, Peilin Liu","doi":"10.1109/TBCAS.2024.3470520","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3470520","url":null,"abstract":"<p><p>Bio-inspired neuromorphic hardware with learning ability is highly promising to achieve human-like intelligence, particularly in terms of high energy efficiency and strong environmental adaptability. Though many customized prototypes have demonstrated learning ability, learning on neuromorphic hardware still lacks a bio-plausible and unified learning framework, and inherent spike-based sparsity and parallelism have not been fully exploited, which fundamentally limits their computational efficiency and scale. Therefore, we develop a unified, event-driven, and massively parallel multi-core neuromorphic online learning processor, namely EPOC. We present a neuromodulation-based neuromorphic online learning framework to unify various learning algorithms, and EPOC supports high-accuracy local/global supervised Spike Neural Network (SNN) learning with a low-memory-demand streaming single-sample learning strategy through different neuromodulator formulations. EPOC leverages a novel event-driven computation method that fully exploits spike-based sparsity throughout the forward-backward learning phases, and parallel multi-channel and multi-core computing architecture, bringing 9.9× time efficiency improvement compared with the baseline architecture. We synthesize EPOC in a 28-nm CMOS process and perform extensive benchmarking. EPOC achieves state-of-the-art learning accuracy of 99.2%, 98.2%, and 94.3% on the MNIST, NMNIST, and DVS-Gesture benchmarks, respectively. Local-learning EPOC achieves 2.9× time efficiency improvement compared with the global learning counterpart. EPOC operates at a typical clock frequency of 100 MHz, providing a peak 328 GOPS/51 GSOPS throughput and a 5.3 pJ/SOP energy efficiency.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368029","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}

{"title":"IEEE Transactions on Biomedical Circuits and Systems Publication Information","authors":"","doi":"10.1109/TBCAS.2024.3463213","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3463213","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 5","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324354","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}

{"title":"TechRxiv: Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TBCAS.2024.3464773","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3464773","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 5","pages":"1190-1190"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695471","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322031","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}

{"title":"Together, We are advance technology","authors":"","doi":"10.1109/TBCAS.2024.3464777","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3464777","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 5","pages":"1192-1192"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324347","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}

{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TBCAS.2024.3464769","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3464769","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 5","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695473","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322032","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}