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Journal articleHadjipanayi C, Yin M, Bannon A, et al., 2024,
Remote gait analysis using ultra-wideband radar technology based on joint range-Doppler-time representation
, IEEE Transactions on Biomedical Engineering, Vol: 71, Pages: 2854-2865, ISSN: 0018-9294Objective: In recent years, radar technology has been extensively utilized in contactless human behavior monitoring systems. The unique capabilities of ultra-wideband (UWB) radars compared to conventional radar technologies, due to time-of-flight measurements, present new untapped opportunities for in-depth monitoring of human movement during overground locomotion. This study aims to investigate the deployability of UWB radars in accurately capturing the gait patterns of healthy individuals with no known walking impairments.Methods: A novel algorithm was developed that can extract ten clinical spatiotemporal gait features using the Doppler information captured from three monostatic UWB radar sensors during a 6-meter walking task. Key gait events are detected from lower-extremity movements based on the joint range-Doppler-time representation of recorded radar data. The estimated gait parameters were validated against a gold-standard optical motion tracking system using 12 healthy volunteers.Results: On average, nine gait parameters can be consistently estimated with 90-98% accuracy, while capturing 94.5% of participants' gait variability and 90.8% of inter-limb symmetry. Correlation and Bland-Altman analysis revealed a strong correlation between radar-based parameters and the ground-truth values, with average discrepancies consistently close to 0.Conclusion: Results prove that radar sensing can provide accurate biomarkers to supplement clinical human gait assessment, with quality similar to gold standard assessment.Significance: Radars can potentially allow a transition from expensive and cumbersome lab-based gait analysis tools toward a completely unobtrusive and affordable solution for in-home deployment, enabling continuous long-term monitoring of individuals for research and healthcare applications.
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Journal articleMiao A, Luo T, Hsieh B, et al., 2024,
Brain clearance is reduced during sleep and anesthesia
, Nature Neuroscience, Vol: 27, Pages: 1046-1050, ISSN: 1097-6256It has been suggested that the function of sleep is to actively clear metabolites and toxins from the brain. Enhanced clearance is also said to occur during anesthesia. Here, we measure clearance and movement of fluorescent molecules in the brains of male mice and show that movement is, in fact, independent of sleep and wake or anesthesia. Moreover, we show that brain clearance is markedly reduced, not increased, during sleep and anesthesia.
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Journal articleKhan S, Anderson W, Constandinou T, 2024,
Surgical Implantation of Brain Computer Interfaces
, JAMA SURGERY, Vol: 159, Pages: 219-220, ISSN: 2168-6254 -
Journal articleTossell K, Yu X, Giannos P, et al., 2023,
Somatostatin neurons in prefrontal cortex initiate sleep preparatory behavior and sleep via the preoptic and lateral hypothalamus
, Nature Neuroscience, Vol: 26, Pages: 1805-1819, ISSN: 1097-6256The prefrontal cortex (PFC) enables mammals to respond to situations, including internal states, with appropriate actions. One such internal state could be ‘tiredness’. Here, using activity tagging in the mouse PFC, we identified particularly excitable, fast-spiking, somatostatin-expressing, γ-aminobutyric acid (GABA) (PFCSst-GABA) cells that responded to sleep deprivation. These cells projected to the lateral preoptic (LPO) hypothalamus and the lateral hypothalamus (LH). Stimulating PFCSst-GABA terminals in the LPO hypothalamus caused sleep-preparatory behavior (nesting, elevated theta power and elevated temperature), and stimulating PFCSst-GABA terminals in the LH mimicked recovery sleep (non-rapid eye-movement sleep with higher delta power and lower body temperature). PFCSst-GABA terminals had enhanced activity during nesting and sleep, inducing inhibitory postsynaptic currents on diverse cells in the LPO hypothalamus and the LH. The PFC also might feature in deciding sleep location in the absence of excessive fatigue. These findings suggest that the PFC instructs the hypothalamus to ensure that optimal sleep takes place in a suitable place.
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Journal articleZhang Z, Feng P, Oprea A, et al., 2023,
Calibration-free and hardware-efficient neural spike detection for brain machine interfaces
, IEEE Transactions on Biomedical Circuits and Systems, Vol: 17, Pages: 725-740, ISSN: 1932-4545Recent translational efforts in brain-machine interfaces (BMI) are demonstrating the potential to help people with neurological disorders. The current trend in BMI technology is to increase the number of recording channels to the thousands, resulting in the generation of vast amounts of raw data. This in turn places high bandwidth requirements for data transmission, which increases power consumption and thermal dissipation of implanted systems. On-implant compression and/or feature extraction are therefore becoming essential to limiting this increase in bandwidth, but add further power constraints – the power required for data reduction must remain less than the power saved through bandwidth reduction. Spike detection is a common feature extraction technique used for intracortical BMIs. In this paper, we develop a novel firing-rate-based spike detection algorithm that requires no external training and is hardware efficient and therefore ideally suited for real-time applications. Key performance and implementation metrics such as detection accuracy, adaptability in chronic deployment, power consumption, area utilization, and channel scalability are benchmarked against existing methods using various datasets. The algorithm is first validated using a reconfigurable hardware (FPGA) platform and then ported to a digital ASIC implementation in both 65 nm and 0.18MU m CMOS technologies. The 128-channel ASIC design implemented in a 65 nm CMOS technology occupies 0.096 mm2 silicon area and consumes 4.86MU W from a 1.2 V power supply. The adaptive algorithm achieves a 96% spike detection accuracy on a commonly used synthetic dataset, without the need for any prior training.
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Journal articleZhang Z, Constandinou TG, 2023,
Firing-rate-modulated spike detection and neural decoding co-design
, JOURNAL OF NEURAL ENGINEERING, Vol: 20, ISSN: 1741-2560 -
Journal articleMartinez S, Veirano F, Constandinou TGG, et al., 2023,
Trends in Volumetric-Energy Efficiency of Implantable Neurostimulators: A Review From a Circuits and Systems Perspective
, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 17, Pages: 2-20, ISSN: 1932-4545 -
Journal articleZhang Z, Constandinou TG, 2023,
Firing-rate-modulated spike detection and neural decoding co-design
<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>Translational efforts on spike-signal-based implantable brain-machine interfaces (BMIs) are increasingly aiming to minimise bandwidth while maintaining decoding performance. Developing these BMIs requires advances in neuroscience and electronic technology, as well as using low-complexity spike detection algorithms and high-performance machine learning models. While some state-of-the-art BMI systems jointly design spike detection algorithms and machine learning models, it remains unclear how the detection performance affects decoding.</jats:p></jats:sec><jats:sec><jats:title>Approach</jats:title><jats:p>We propose the co-design of the neural decoder with an ultra-low complexity spike detection algorithm. The detection algorithm is designed to attain a target firing rate, which the decoder uses to modulate the input features preserving statistical invariance.</jats:p></jats:sec><jats:sec><jats:title>Main results</jats:title><jats:p>We demonstrate a multiplication-free fixed-point spike detection algorithm with nearly perfect detection accuracy and the lowest complexity among studies we have seen. By co-designing the system to incorporate statistically invariant features, we observe significantly improved long-term stability, with decoding accuracy degrading by less than 10% after 80 days of operation. Our analysis also reveals a nonlinear relationship between spike detection and decoding performance. Increasing the detection sensitivity improves decoding accuracy and long-term stability, which means the activity of more neurons is beneficial despite the detection of more noise. Reducing the spike detection sensitivity still provides acceptable decoding accuracy whilst reducing the bandwidth by at least 30%.</jats:p></jats:sec><jats:sec><jats:title>Signifi
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Conference paperWong SS, Radford J, Faccio D, et al., 2023,
Multielectrode Multiplexing for Bioimpedance Surface Topography Mapping
, IEEE BioSensors Conference (BioSensors), Publisher: IEEE -
Conference paperHyanda MH, Ahmadi N, Charlton PH, et al., 2023,
A Comparative Evaluation of Video Codecs for rPPG-based Heart Rate Estimation
, Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC), Publisher: IEEE, Pages: 243-247, ISSN: 2309-9402 -
Conference paperMeimandi A, Feng P, Carminati M, et al., 2023,
Implantable Biosensor for Brain Dopamine using Microwire-Based Electrodes
, IEEE BioSensors Conference (BioSensors), Publisher: IEEE -
Conference paperWong SS, Radford J, Binner P, et al., 2023,
Multimodal Approaches for Real-time Mesoscopic Tissue Differentiation
, IEEE BioSensors Conference (BioSensors), Publisher: IEEE -
Conference paperOzbek B, Constandinou TG, 2023,
An Autonomous Zero-Mask Unique ID Generation System for Next-Generation Neural Interfaces
, 21st IEEE Interregional NEWCAS Conference (NEWCAS), Publisher: IEEE, ISSN: 2472-467X -
Journal articleWong SS, Malik A, Ekanayake J, et al., 2023,
Towards Real-time Multiplexed Bioimpedance Tumour-Tissue Margin Analysis
, 2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC, ISSN: 1557-170X -
Conference paperNairac Z, Constandinou TG, 2023,
Design of a Novel, Low-Cost System for Neural Electrical Impedance Tomography
, 11th International IEEE EMBS Conference on Neural Engineering (IEEE/EMBS NER), Publisher: IEEE, ISSN: 1948-3546 -
Conference paperMifsud A, Constandinou TG, 2023,
Towards a CMOS-Process-Portable ReRAM PDK
, 21st IEEE Interregional NEWCAS Conference (NEWCAS), Publisher: IEEE, ISSN: 2472-467X -
Journal articleStanchieri GDP, De Marcellis A, Battisti G, et al., 2022,
A Multilevel Synchronized Optical Pulsed Modulation for High Efficiency Biotelemetry
, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 16, Pages: 1313-1324, ISSN: 1932-4545- Author Web Link
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Journal articleSavolainen OW, Zhang Z, Constandinou TG, 2022,
Ultra Low Power, Event-Driven Data Compression of Multi-Unit Activity
<jats:title>Abstract</jats:title><jats:p>Recent years have demonstrated the feasibility of using intracortical Brain-Machine Interfaces (iBMIs), by decoding thoughts, for communication and cursor control tasks. iBMIs are increasingly becoming wireless due to the risk of infection and mechanical failure, typically associated with percutaneous connections. The wireless communication itself, however, increases the power consumption further; with the total dissipation being strictly limited due to safety heating limits of cortical tissue. Since wireless power is typically proportional to the communication bandwidth, the output Bit Rate (BR) must be minimised. Whilst most iBMIs utilise Multi-Unit activity (MUA), i.e. spike events, and this in itself significantly reduces the output BR (compared to raw data), it still limits the scalability (number of channels) that can be achieved. As such, additional compression for MUA signals are essential for fully-implantable, high-information-bandwidth systems. To meet this need, this work proposes various hardware-efficient, ultra-low power MUA compression schemes. We investigate them in terms of their BRs and hardware requirements as a function of various on-implant conditions such as MUA Binning Period (BP) and number of channels. It was found that for BPs ≤ 10 ms, the delta-asynchronous method had the lowest total power and reduced the BR by almost an order of magnitude relative to classical methods (e.g. to approx. 151 bps/channel for a BP of 1 ms and 1000 channels on-implant.). However, at larger BPs the synchronous method performed best (e.g. approx. 29 bps/channel for a BP of 50 ms, independent of channel count). As such, this work can guide the choice of MUA data compression scheme for BMI applications, where the BR can be significantly reduced in hardware efficient ways. This enables the next generation of wireless iBMIs, with small implant sizes, high channel counts, low-power, and small hardware foo
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Conference paperMifsud A, Shen J, Feng P, et al., 2022,
A CMOS-based characterisation platform for emerging RRAM technologies
, 2022 IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 75-79Mass characterisation of emerging memory devices is an essential step in modelling their behaviour for integration within a standard design flow for existing integrated circuit designers. This work develops a novel characterisation platform for emerging resistive devices with a capacity of up to 1 million devices on-chip. Split into four independent sub-arrays, it contains on-chip column-parallel DACs for fast voltage programming of the DUT. On-chip readout circuits with ADCs are also available for fast read operations covering 5-decades of input current (20nA to 2mA). This allows a device’s resistance range to be between 1kΩ and 10MΩ with a minimum voltage range of ±1.5V on the device.
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Journal articleSavolainen O, Zhang Z, Feng P, et al., 2022,
Hardware-efficient compression of neural multi-unit activity
, IEEE Access, Vol: 10, Pages: 117515-117529, ISSN: 2169-3536Brain-machine interfaces (BMI) are tools for measuring neural activity in the brain, used to treat numerous conditions. It is essential that the next generation of intracortical BMIs is wireless so as to remove percutaneous connections, i.e. wires, and the associated mechanical and infection risks. This is required for the effective translation of BMIs into clinical applications and is one of the remaining bottlenecks. However, due to cortical tissue thermal dissipation safety limits, the on-implant power consumption must be strictly limited. Therefore, both the neural signal processing and wireless communication power should be minimal, while the implants should provide signals that offer high behavioural decoding performance (BDP). The Multi-Unit Activity (MUA) signal is the most common signal in modern BMIs. However, with an ever-increasing channel count, the raw data bandwidth is becoming prohibitively high due to the associated communication power exceeding the safety limits. Data compression is therefore required. To meet this need, this work developed hardware-efficient static Huffman compression schemes for MUA data. Our final system reduced the bandwidth to 27 bps/channel, compared to the standard MUA rate of 1 kbps/channel. This compression is over an order of magnitude more than has been achieved before, while using only 0.96 uW/channel processing power and 246 logic cells. Our results were verified on 3 datasets and less than 1% loss in BDP was observed. As such, with the use of effective data compression, an order more of MUA channels can be fitted on-implant, enabling the next generation of high-performance wireless intracortical BMIs.
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