IEEE transactions on biomedical circuits and systems最新文献

This paper presents a reconfigurable bidirectional wireless power and data transceiver (RB-WPDT) integrated circuit (IC) for wearable biomedical applications. The proposed transceiver can be reconfigured as a differential class-D power amplifier or a full-wave rectifier depending on the mode signal to facilitate power transfer between devices. Additionally, the RBWPDT system supports full-duplex (FD) data transmission via a single inductive link, enabling real-time control and monitoring between devices. The proposed FD method utilizes frequency shift-keying pulse-width modulation (FSK-PWM) for downlink and load shift-keying (LSK) for uplink, achieving simultaneous bidirectional data transmission by ensuring that the FSK-PWM downlink and LSK uplink data channels operate independently with minimal interference. The measured downlink and uplink data rates are 250 kb/s and 67 kb/s, respectively. The measured overall DC-to-DC efficiency is 49%, while the power delivered to the load (PDL) is 120 mW at a 5 mm distance. The proposed chip is fabricated using a 180-nm BCD CMOS process.

This article presents a fully integrated CMOS ferrofluidic platform featuring on-chip three-electrode electrochemical cells, temperature regulators, and magnetic sensors. The proposed platform consists of 25 ferrofluidic pixels and 2 magnetic sensors. Each ferrofluidic pixel comprises a spiral inductor, a three-electrode electrochemical cell, a temperature sensor, and a localized Joule heater. Unlike pneumatic-based platforms, this ferrofluidic platform does not require an external pneumatic pump to drive droplets. Instead, the on-chip spiral inductors generate magnetic fields to manipulate the ferrofluidic droplets. Additionally, these inductors are repurposed as heat radiators. The CMOS ferrofluidic platform is implemented using a 45-nm CMOS SOI process. Theoretical analyses of ferrofluidic control and magnetic sensing are conducted to understand the relationship between ferrofluidic movement conditions and the integrated magnetic sensor. The on-chip electrochemical potentiostat is characterized using various concentrations of methylene blue solution, and the variation in the electrochemical sensor is measured. As proof of concept, biological measurements with on-chip real-time recombinase polymerase amplification (RT-RPA) are demonstrated. The proposed platform offers a fully integrated solution for ferrofluidic manipulation, sensing, and temperature regulation without the need for external bulky equipment, thereby supporting advanced biomolecular processing. While RT-RPA is used here solely for demonstration purposes, our ferrofluidic multi-functional CMOS array platform is also capable of processing a wide range of other molecular analytes. This versatility underscores the platform's potential for broad applications in molecular diagnostics and bioanalytical research.

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.

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.

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.

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.

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.