Biomedical Engineering - Research Publications
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ItemMulti-frequency steady-state visual evoked potential dataset(NATURE PORTFOLIO, 2024-01-04)The Steady-State Visual Evoked Potential (SSVEP) is a widely used modality in Brain-Computer Interfaces (BCIs). Existing research has demonstrated the capabilities of SSVEP that use single frequencies for each target in various applications with relatively small numbers of commands required in the BCI. Multi-frequency SSVEP has been developed to extend the capability of single-frequency SSVEP to tasks that involve large numbers of commands. However, the development on multi-frequency SSVEP methodologies is falling behind compared to the number of studies with single-frequency SSVEP. This dataset was constructed to promote research in multi-frequency SSVEP by making SSVEP signals collected with different frequency stimulation settings publicly available. In this dataset, SSVEPs were collected from 35 participants using single-, dual-, and tri-frequency stimulation and with three different multi-frequency stimulation variants.
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ItemNo Preview AvailableComputational Fluid Dynamics of Stent-Mounted Neural Interfaces in an Idealized Cerebral Venous Sinus.(IEEE, 2023-07)Hemodynamic changes in stented blood vessels play a critical role in stent-associated complications. The majority of work on the hemodynamics of stented blood vessels has focused on coronary arteries but not cerebral venous sinuses. With the emergence of endovascular electrophysiology, there is a growing interest in stenting cerebral blood vessels. We investigated the hemodynamic impact of a stent-mounted neural interface inside the cerebral venous sinus. The stent was virtually implanted into an idealized superior sagittal sinus (SSS) model. Local venous blood flow was simulated. Results showed that blood flow was altered by the stent, generating recirculation and low wall shear stress (WSS) around the device. However, the effect of the electrodes on blood flow was not prominent due to their small size. This is an early exploration of the hemodynamics of a stent-mounted neural interface. Future work will shed light on the key factors that influence blood flow and stenting outcomes.Clinical Relevance-The study investigates blood flow through a stent-based electrode array inside the cerebral venous sinus. The hemodynamic impact of the stent can provide insight into neointimal growth and thrombus formation.
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ItemNo Preview AvailablePredictive Shared Control of Robotic Arms Using Simulated Brain-Computer Interface Inputs(IEEE, 2023)Low decoding accuracy makes brain-computer interface (BCI) control of a robotic arm difficult. Shared control (SC) can overcome limitations of a BCI by leveraging external sensor data and generating commands to assist the user. Our study explored whether reaching targets with a robot end-effector was easier using SC rather than direct control (DC). We simulated a motor imagery BCI using a joystick with noise introduced to explicitly control interface accuracy to be 65% or 79%. Compared to DC, our prediction-based implementation of SC led to a significant reduction in the trajectory length of successful reaches for 4 (3) out of 5 targets using the 65% (79%) accurate interface, with failure rates being equivalent to DC for 2 (1) out of 5 targets. Therefore, this implementation of SC is likely to improve reaching efficiency but at the cost of more failures. Additionally, the NASA Task Load Index results suggest SC reduced user workload.Clinical relevance-Shared control can minimise the impact of BCI decoder errors on robot motion, making robotic arm control using noninvasive BCIs more viable.
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ItemNo Preview AvailableEstablishing the Calibration Curve of a Compressive Ophthalmodynamometry Device(IEEE, 2023)The relationship between externally applied force and intraocular pressure was determined using an ex-vivo porcine eye model (N=9). Eyes were indented through the sclera with a convex ophthalmodynamometry head (ODM). Intraocular pressure and ophthalmodynamometric force were simultaneously recorded to establish a calibration curve of this indenter head. A calibration coefficient of 0.140 ± 0.009 mmHg/mN was established and was shown to be highly linear (r = 0.998 ± 0.002). Repeat application of ODM resulted in a 0.010 ± 0.002 mmHg/mN increase to the calibration coefficient.Clinical Relevance- ODM has been highlighted as a potential method of non-invasively estimating intracranial pressure. This study provides relevant data for the practical performance of ODM with similar compressive devices.
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ItemNo Preview AvailableEffect of alpha range activity on SSVEP decoding in brain-computer interfaces(IEEE, 2023)Brain-computer interfaces (BCIs) facilitate direct communication between the brain and external devices. For BCI technology to be commercialized for wide scale applications, BCIs should be accurate, efficient, and exhibit consistency in performance for a wide variety of users. A core challenge is the physiological and anatomical differences amongst people, which causes a high variability amongst participants in BCI studies. Hence, it becomes necessary to analyze the mechanisms causing this variability and address them by improving the decoding algorithms. In this paper, a publicly available steady-state visual evoked potential (SSVEP) dataset is analyzed to study the effect of SSVEP flicker on the endogenous alpha power and the subsequent overall effect on the classification accuracy of the participants. It was observed that the participants with classification accuracy below 95% showed increased alpha power in their brain activities. Incorrect prediction in the decoding algorithm was observed a maximum number of times when the predicted frequency was in the range 9-12 Hz. We conclude that frequencies between 9-12 Hz may result in below par performance in some participants when canonical correlation analysis is used for classification.Clinical relevance-If alpha-band frequencies are used for SSVEP stimulation, alpha power interference in EEG may alter BCI accuracy for some users.
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ItemNo Preview AvailableAutoregressive models for biomedical signal processing.(IEEE, 2023-07)Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially, this data is subject to measurement errors as well as uncertainties in the underlying system model. As a result, standard signal processing using autoregressive model estimators may be biased. We present a framework for autoregressive modelling that incorporates these uncertainties explicitly via an overparameterised loss function. To optimise this loss, we derive an algorithm that alternates between state and parameter estimation. Our work shows that the procedure is able to successfully denoise time series and successfully reconstruct system parameters.Clinical relevance- This new paradigm can be used in a multitude of applications in neuroscience such as brain-computer interface data analysis and better understanding of brain dynamics in diseases such as epilepsy.
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ItemNo Preview AvailableWearable Transmitter Coil Design for Inductive Wireless Power Transfer to Implantable Devices.(IEEE, 2023-07)Wireless endovascular sensors and stimulators are emerging biomedical technologies for applications such as endovascular pressure monitoring, hyperthermia, and neural stimulations. Recently, coil-shaped stents have been proposed for inductive power transfer to endovascular devices using the stent as a receiver. However, less work has been done on the external transmitter components, so the maximum power transferable remains unknown. In this work, we design and evaluate a wearable transmitter coil that allows 50 mW power transfer in simulation.Clinical Relevance-This allows more accurate measurements and precise control of endovascular devices.
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ItemNo Preview AvailableModel Parameter Estimation As Features to Predict the Duration of Epileptic Seizures From Onset.(IEEE, 2023-07)The durations of epileptic seizures are linked to severity and risk for patients. It is unclear if the spatiotemporal evolution of a seizure has any relationship with its duration. Understanding such mechanisms may help reveal treatments for reducing the duration of a seizure. Here, we present a novel method to predict whether a seizure is going to be short or long at its onset using features that can be interpreted in the parameter space of a brain model. The parameters of a Jansen-Rit neural mass model were tracked given intracranial electroencephalography (iEEG) signals, and were processed as time series features using MINIROCKET. By analysing 2954 seizures from 10 patients, patient-specific classifiers were built to predict if a seizure would be short or long given 7 s of iEEG at seizure onset. The method achieved an area under the receiver operating characteristic curve (AUC) greater than 0.6 for five of 10 patients. The behaviour in the parameter space has shown different mechanisms are associated with short/long seizures.Clinical relevance-This shows that it is possible to classify whether a seizure will be short or long based on its early characteristics. Timely interventions and treatments can be applied if the duration of the seizures can be predicted.
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ItemNo Preview AvailableA debate on the neuronal origin of focal seizures(Wiley, 2023-12)A critical question regarding how focal seizures start is whether we can identify particular cell classes that drive the pathological process. This was the topic for debate at the recent International Conference for Technology and Analysis of Seizures (ICTALS) meeting (July 2022, Bern, CH) that we summarize here. The debate has been fueled in recent times by the introduction of powerful new ways to manipulate subpopulations of cells in relative isolation, mostly using optogenetics. The motivation for resolving the debate is to identify novel targets for therapeutic interventions through a deeper understanding of the etiology of seizures.
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ItemNo Preview AvailableEvaluation of Optimal Stimuli for SSVEP-Based Augmented Reality Brain-Computer Interfaces(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023)