275 results found
Khan S, Anderson W, Constandinou T, 2024, Surgical Implantation of Brain Computer Interfaces., JAMA Surg, Vol: 159, Pages: 219-220
Tossell 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: 1097-6256
The 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.
Zhang 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: 1932-4545
Recent 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.
Wong SS, Malik A, Ekanayake J, et al., 2023, Towards Real-time Multiplexed Bioimpedance Tumour-Tissue Margin Analysis., Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2023, Pages: 1-5
Bioimpedance varies with physical tissue characteristics. As such it can be used for real-time tissue discrimination. This has led to its application as a surgical mapping tool to differentiate between healthy and abnormal tissue intraoperatively during tumour resection. Here, we build on previous work implementing a probe-based tetrapolar bioimpedance systems demonstrator, now extracting additional information for margin analysis with imperfect bioimpedance visibility. Through finite element analysis, we show preliminary findings using a single measurement with a multiplexed tetrapolar bioimpedance probe for identifying tissue boundaries, applied to porcine tissue as a surrogate for a tumour-tissue interface.
Zhang Z, Constandinou TG, 2023, Firing-rate-modulated spike detection and neural decoding co-design, JOURNAL OF NEURAL ENGINEERING, Vol: 20, ISSN: 1741-2560
Martinez 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
Zhang 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
Wong SS, Radford J, Faccio D, et al., 2023, Multielectrode Multiplexing for Bioimpedance Surface Topography Mapping
Traditional bioimpedance point scanning for tissue identification successively measures tissue electrical properties at discrete locations; this process is not optimised for developing a bioimpedance map with sufficient spatial resolution in a short time frame to reflect the underlying tissue topography. We build on previous work implementing a tetrapolar bioimpedance measuring system, with a novel combination of computational bioimpedance multiplexing and a multi-electrode architecture. Experimental results were obtained from porcine tissue, using a 12-electrode probe with 14.3 seconds of recorded data sampled at 1MSPS. The reconstructed bioimpedance image resembles the tissue sample RGB image and can provide mesoscopic tissue structural information, surpassing the spatial resolution previously limited by electrode pitch size. The spatial resolution obtained with our approach scales exponentially with the number of electrodes, with an adaptable electrode geometry to achieve optimal reconstructed bioimpedance maps for real-time tissue identification.
Hyanda MH, Ahmadi N, Charlton PH, et al., 2023, A Comparative Evaluation of Video Codecs for rPPG-based Heart Rate Estimation, Pages: 243-247
Remote photoplethysmography (rPPG) is a noninvasive technology that can be used to extract heart rate (HR) from video data. Video codecs are typically used for compressing video, which allows for efficient data storage, but could negatively impact the accuracy of HR estimation. To date, only limited number of studies investigate the impact of video codecs on rPPG-based HR estimation. In this paper, we present a comprehensive comparative evaluation of video codecs for rPPG-based HR estimation. Several factors being compared include video encoders, video containers, quality rate mode, and resolution. Experimental results using in-house dataset show that the lossy encoder like VP9 yield good performance nearly similar to the lossless counterparts. Additionally, the.avi container is preferable because it can contain all encoders tested except x265 (HEVC). Varying the resolution significantly affects the file size and accuracy of HR estimation. This study provides useful information for the creation of rPPG dataset and development of rPPG algorithm.
Wong SS, Radford J, Binner P, et al., 2023, Multimodal Approaches for Real-time Mesoscopic Tissue Differentiation
Multi-modality imaging techniques that integrate sensor data from different domains have shown immense potential in the field of clinical diagnostics. This paper aims to provide a comprehensive approach to delineating surface topology and tissue boundaries resolved at the mesoscopic scale with electrical (bioimpedance), mechanical (force-related haptics) and optical technologies (diffuse correlation spectroscopy and time-domain near-infrared spectroscopy). This preliminary work lays the foundations for comparing and combining next-generation approaches for real-time tissue identification.
Meimandi A, Feng P, Carminati M, et al., 2023, Implantable Biosensor for Brain Dopamine using Microwire-Based Electrodes
This paper systematically demonstrates the feasibility of wirelessly monitoring dopamine concentration in the brain with an implantable biosensor. The biosensor was realized using microwires, and then, the dopamine concentration was measured in-vitro ranging from 0.3 μ M to 2 μ M, corresponding to the physio-pathological concentration range in human brain. The obtained results were used to design and optimise a full-custom CMOS sensor interface for in-vivo dopamine monitoring. The key component of this interface is a potentiostat with a maximum power consumption of 10.24 μ W in a 10kHz sampling frequency. The CMOS interface automatically subtracts the background current up to 2.34 μ A. The obtained sensitivity in dopamine detection has been evaluated in 150μ A/μ M, with a Limit of Detection (LoD) of 33 nM, thus being suitable for dopamine monitoring in human brain.
Ozbek 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
Nairac 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
Mifsud A, Constandinou TG, 2023, Towards a CMOS-Process-Portable ReRAM PDK, 21st IEEE Interregional NEWCAS Conference (NEWCAS), Publisher: IEEE, ISSN: 2472-467X
Stanchieri 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
Savolainen 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
Mifsud 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: 75-79
Mass 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.
Savolainen O, Zhang Z, Feng P, et al., 2022, Hardware-efficient compression of neural multi-unit activity, IEEE Access, Vol: 10, Pages: 117515-117529, ISSN: 2169-3536
Brain-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.
Zaaimi B, Turnbull M, Hazra A, et al., 2022, Closed-loop optogenetic control of the dynamics of neural activity in non-human primates, NATURE BIOMEDICAL ENGINEERING, ISSN: 2157-846X
Lauteslager T, Tommer M, Lande TS, et al., 2022, Dynamic Microwave Imaging of the Cardiovascular System Using Ultra-Wideband Radar-on-Chip Devices, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 69, Pages: 2935-2946, ISSN: 0018-9294
Rapeaux A, Syed O, Cuttaz E, et al., 2022, Preparation of rat sciatic nerve for ex vivo neurophysiology, Jove-Journal of Visualized Experiments, Vol: 185, Pages: 1-14, ISSN: 1940-087X
Ex vivo preparations enable the study of many neurophysiological processes in isolation from the rest of the body while preserving local tissue structure. This work describes the preparation of rat sciatic nerves for ex vivo neurophysiology, including buffer preparation, animal procedures, equipment setup and neurophysiological recording. This work provides an overview of the different types of experiments possible with this method. The outlined method aims to provide 6 h of stimulation and recording on extracted peripheral nerve tissue in tightly controlled conditions for optimal consistency in results. Results obtained using this method are A-fibre compound action potentials (CAP) with peak-to-peak amplitudes in the millivolt range over the entire duration of the experiment. CAP amplitudes and shapes are consistent and reliable, making them useful to test and compare new electrodes to existing models, or the effects of interventions on the tissue, such as the use of chemicals, surgical alterations, or neuromodulatory stimulation techniques. Both conventional commercially available cuff electrodes with platinum-iridium contacts and custom-made conductive elastomer electrodes were tested and gave similar results in terms of nerve stimulus strength-duration response.
Zhang Z, Constandinou TG, 2022, Selecting an effective amplitude threshold for neural spike detection., Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2022, Pages: 2328-2331
This paper assesses and challenges whether commonly used methods for defining amplitude thresholds for spike detection are optimal. This is achieved through empirical testing of single amplitude thresholds across multiple recordings of varying SNR levels. Our results suggest that the most widely used noise-statistics-driven threshold can suffer from parameter deviation in different noise levels. The spike-noise-driven threshold can be an ideal approach to set the threshold for spike detection, which suffers less from the parameter deviation and is robust to sub-optimal settings.
Teversham J, Wong SS, Hsieh B, et al., 2022, Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding., Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2022, Pages: 208-213
This study details the development of a novel, approx. £20 electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer a financially and operationally accessible device that can be deployed on a mass scale to facilitate education and public engagement in the domain of EEG sensing and neurotechnologies. Real-time decoding of steady-state visual evoked potentials (SSVEPs) is achieved using variations of the widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA and generalised CCA. All BCI functionality is executed on board an inexpensive ESP32 microcontroller. SSVEP decoding accuracy of 95.56 ± 3.74% with an ITR of 102 bits/min was achieved with modest calibration.
Oprea A, Zhang Z, Constandinou TG, 2022, Hardware evaluation of spike detection algorithms towards wireless brain machine interfaces
<jats:title>Abstract</jats:title><jats:p>The current trend for implantable Brain Machine Interfaces (BMIs) is to increase the channel count, towards next generation devices that improve on information transfer rate. This however increases the raw data bandwidth for wired or wireless systems that ultimately impacts the power budget (and thermal dissipation). On-implant feature extraction and/or compression are therefore becoming essential to reduce the data rate, however the processing power is of concern. One common feature extraction technique for intracortical BMIs is spike detection. In this work, we have empirically compared the performance, resource utilization, and power consumption of three hardware efficient spike emphasizers, Non-linear Energy Operator (NEO), Amplitude Slope Operator (ASO) and Energy of Derivative (ED), and two common statistical thresholding mechanisms (using mean or median). We also propose a novel median approximation to address the issue of the median operator not being hardware-efficient to implement. These have all been implemented and evaluated on reconfigurable hardware (FPGA) to estimate their hardware efficiency in an ultimate ASIC design. Our results suggest that ED with average thresholding provides the most hardware efficient (low power/resource) choice, while using median has the advantage of improved detection accuracy and higher robustness on threshold multiplier settings. This work is significant because it is the first to implement and compare the hardware and algorithm trade-offs that have to be made before translating the algorithms into hardware instances to design wireless implantable BMIs.</jats:p>
Rapeaux A, Constandinou T, 2022, HFAC dose repetition and accumulation leads to progressively longer block carryover effect in rat sciatic nerve, Frontiers in Neuroscience, Vol: 16, ISSN: 1662-453X
This paper describes high-frequency nerve block experiments carried out on rat sciatic nerves to measure the speed of recoveryof A fibres from block carryover. Block carryover is the process by which nerve excitability remains suppressed temporarily afterHigh Frequency Alternative (HFAC) block is turned off following its application. In this series of experiments 5 rat sciatic nerveswere extracted and prepared for ex-vivo stimulation and recording in a specially designed perfusion chamber. For each nerverepeated HFAC block and concurrent stimulation trials were carried out to observe block carryover after signal shutoff. The nervewas allowed to recover fully between each trial. Time to recovery from block was measured by monitoring for when relativenerve activity returned to within 90% of baseline levels measured at the start of each trial. HFAC block carryover duration wasfound to be dependent on accumulated dose by statistical test for two different HFAC durations. The carryover property of HFACblock on A fibres could enable selective stimulation of autonomic nerve fibres such as C fibres for the duration of carryover. Blockcarryover is particularly relevant to potential chronic clinical applications of block as it reduces power requirements forstimulation to provide the blocking effect. This work characterises this process towards the creation of a model describing itsbehaviour.
Zamora M, Toth R, Morgante F, et al., 2022, DyNeuMo Mk-1: Design and pilot validation of an investigational motion-adaptive neurostimulator with integrated chronotherapy, EXPERIMENTAL NEUROLOGY, Vol: 351, ISSN: 0014-4886
Savolainen OW, Zhang Z, Feng P, et al., 2022, Hardware-Efficient Compression of Neural Multi-Unit Activity
<jats:title>Abstract</jats:title><jats:p>Brain-machine interfaces (BMI) are tools for treating neurological disorders and motor-impairments. 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.</jats:p>
Ahmadi N, Adiono T, Purwarianti A, et al., 2022, Improved spike-based brain-machine interface using bayesian adaptive kernel smoother and deep learning, IEEE Access, Vol: 10, Pages: 29341-29356, ISSN: 2169-3536
Multiunit activity (MUA) has been proposed to mitigate the robustness issue faced by single-unit activity (SUA)-based brain-machine interfaces (BMIs). Most MUA-based BMIs still employ a binning method for estimating firing rates and linear decoder for decoding behavioural parameters. The limitations of binning and linear decoder lead to suboptimal performance of MUA-based BMIs. To address this issue, we propose a method which consists of Bayesian adaptive kernel smoother (BAKS) as the firing rate estimation algorithm and deep learning, particularly quasi-recurrent neural network (QRNN), as the decoding algorithm. We evaluated the proposed method for reconstructing (offline) hand kinematics from intracortical neural data chronically recorded from the primary motor cortex of two non-human primates. Extensive empirical results across recording sessions and subjects showed that the proposed method consistently outperforms other combinations of firing rate estimation algorithm and decoding algorithm. Overall results suggest the effectiveness of the proposed method for improving the decoding performance of MUA-based BMIs.
Zhang Z, Savolainen OW, Constandinou TG, 2022, Algorithm and hardware considerations for real-time neural signal on-implant processing, JOURNAL OF NEURAL ENGINEERING, Vol: 19, ISSN: 1741-2560
Teversham J, Wong SS, Hsieh B, et al., 2022, Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding
<jats:title>Abstract</jats:title><jats:p>This study details the development of a novel, approx. £20 electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer a financially and operationally accessible device that can be deployed on a mass scale to facilitate education and public engagement in the domain of EEG sensing and neurotechnologies. Real-time decoding of steady-state visual evoked potentials (SSVEPs) is achieved using variations of the widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA and generalised CCA. All BCI functionality is executed on board an inexpensive ESP32 microcontroller. SSVEP decoding accuracy of 95.56 ± 3.74% with an ITR of 102 bits/min was achieved with modest calibration.</jats:p>
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.