User experience of a seizure risk forecasting app: A mixed methods investigation.

OBJECTIVE: Over recent years, there has been a growing interest in exploring the utility of seizure risk forecasting, particularly how it could improve quality of life for people living with epilepsy. This study reports on user experiences and perspectives of a seizure risk forecaster app, as well as the potential impact on mood and adjustment to epilepsy. METHODS: Active app users were asked to complete a survey (baseline and 3-month follow-up) to assess perspectives on the forecast feature as well as mood and adjustment. Post-hoc, nine neutral forecast users (neither agreed nor disagreed it was useful) completed semi-structured interviews, to gain further insight into their perspectives of epilepsy management and seizure forecasting. Non-parametric statistical tests and inductive thematic analyses were used to analyse the quantitative and qualitative data, respectively. RESULTS: Surveys were completed by 111 users. Responders consisted of "app users" (n = 58), and "app and forecast users" (n = 53). Of the "app and forecast users", 40 % believed the forecast was accurate enough to be useful in monitoring for seizure risk, and 60 % adopted it for purposes like scheduling activities and helping mental state. Feeling more in control was the most common response to both high and low risk forecasted states. In-depth interviews revealed five broad themes, of which 'frustrations with lack of direction' (regarding their current epilepsy management approach), 'benefits of increased self-knowledge' and 'current and anticipated usefulness of forecasting' were the most common. SIGNIFICANCE: Preliminary results suggest that seizure risk forecasting can be a useful tool for people with epilepsy to make lifestyle changes, such as scheduling daily events, and experience greater feelings of control. These improvements may be attributed, at least partly, to the improvements in self-knowledge experienced through forecast use.

Feasibility of endovascular stimulation of the femoral nerve using a stent-mounted electrode array.

Objective.Electrical stimulation of peripheral nerves has long been a treatment option to restore impaired neural functions that cannot be restored by conventional pharmacological therapies. Endovascular neurostimulation with stent-mounted electrode arrays is a promising and less invasive alternative to traditional implanted electrodes, which typically require invasive implantation surgery. In this study, we investigated the feasibility of endovascular stimulation of the femoral nerve using a stent-mounted electrode array and compared its performance to that of a commercially available pacing catheter.Approach.In acute animal experiments, a pacing catheter was implanted unilaterally in the femoral artery to stimulate the femoral nerve in a bipolar configuration. Electromyogram of the quadriceps and electroneurogram of a distal branch of the femoral nerve were recorded. After retrieval of the pacing catheter, a bipolar stent-mounted electrode array was implanted in the same artery and the recording sessions were repeated.Main Results.Stimulation of the femoral nerve was feasible with the stent-electrode array. Although the threshold stimulus intensities required with the stent-mounted electrode array (at 100-500µs increasing pulse width, 2.17 ± 0.87 mA-1.00 ± 0.11 mA) were more than two times higher than the pacing catheter electrodes (1.05 ± 0.48 mA-0.57 ± 0.28 mA), we demonstrated that, by reducing the stimulus pulse width to 100µs, the threshold charge per phase and charge density can be reduced to 0.22 ± 0.09µC and 24.62 ± 9.81µC cm-2, which were below the tissue-damaging limit, as defined by the Shannon criteria.Significance.The present study is the first to reportin vivofeasibility and efficiency of peripheral nerve stimulation using an endovascular stent-mounted electrode array.

Disrupting abnormal neuronal oscillations with adaptive delayed feedback control.

Closed-loop neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson's disease. However, at the moment, standard stimulation protocols rely on continuous open-loop stimulation and the design of adaptive controllers is an active field of research. Delayed feedback control (DFC), a popular method used to control chaotic systems, has been proposed as a closed-loop technique for desynchronisation of neuronal populations but, so far, was only tested in computational studies. We implement DFC for the first time in neuronal populations and access its efficacy in disrupting unwanted neuronal oscillations. To analyse in detail the performance of this activity control algorithm, we used specialised in vitro platforms with high spatiotemporal monitoring/stimulating capabilities. We show that the conventional DFC in fact worsens the neuronal population oscillatory behaviour, which was never reported before. Conversely, we present an improved control algorithm, adaptive DFC (aDFC), which monitors the ongoing oscillation periodicity and self-tunes accordingly. aDFC effectively disrupts collective neuronal oscillations restoring a more physiological state. Overall, these results support aDFC as a better candidate for therapeutic closed-loop brain stimulation.

Haemodynamics of stent-mounted neural interfaces in tapered and deformed blood vessels.

The endovascular neural interface provides an appealing minimally invasive alternative to invasive brain electrodes for recording and stimulation. However, stents placed in blood vessels have long been known to affect blood flow (haemodynamics) and lead to neointimal growth within the blood vessel. Both the stent elements (struts and electrodes) and blood vessel wall geometries can affect the mechanical environment on the blood vessel wall, which could lead to unfavourable vascular remodelling after stent placement. With increasing applications of stents and stent-like neural interfaces in venous blood vessels in the brain, it is necessary to understand how stents affect blood flow and tissue growth in veins. We explored the haemodynamics of a stent-mounted neural interface in a blood vessel model. Results indicated that blood vessel deformation and tapering caused a substantial change to the lumen geometry and the haemodynamics. The neointimal proliferation was evaluated in sheep implanted with an endovascular neural interface. Analysis showed a negative correlation with the mean Wall Shear Stress pattern. The results presented here indicate that the optimal stent oversizing ratio must be considered to minimise the haemodynamic impact of stenting.

Multi-frequency steady-state visual evoked potential dataset.

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.

Understanding visual processing of motion: completing the picture using experimentally driven computational models of MT.

Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by which we can better understand and predict the principles of neural computation. Computational modeling of the neuronal pathways of the visual cortex has been successful in developing theories of biological motion processing. This review describes a range of computational models that have been inspired by neurophysiological experiments. Theories of local motion integration and pattern motion processing are presented, together with suggested neurophysiological experiments designed to test those hypotheses.

Wearable Transmitter Coil Design for Inductive Wireless Power Transfer to Implantable Devices.

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.

Predictive Shared Control of Robotic Arms Using Simulated Brain-Computer Interface Inputs.

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.

Experimental validation on dual-frequency outperforms single-frequency SSVEP with large numbers of targets within a given frequency range.

Multi-frequency steady-state visual evoked potential (SSVEP) aims to increase the number of targets in SSVEP-based brain-computer interfaces. However, the effectiveness of multi-frequency SSVEP when there is a large number of targets compared to traditional single-frequency SSVEP has not been demonstrated to date. It is also unclear the degree to which multi-frequency SSVEP outperforms single-frequency SSVEP as the number of targets increases. This study directly compares single-frequency and dual-frequency SSVEPs for different numbers of targets within a fixed (5 Hz) frequency range. Our results demonstrate that dual-frequency SSVEP maintains its performance at a high level of accuracy in the range while single-frequency SSVEP performance falls as the number of targets becomes very high within the given frequency range. In this particular study, dual-frequency SSVEP has a clear advantage when there are more than 120 targets in a 5 Hz frequency range.

Computational Fluid Dynamics of Stent-Mounted Neural Interfaces in an Idealized Cerebral Venous Sinus.

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.

Establishing the Calibration Curve of a Compressive Ophthalmodynamometry Device.

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.

Observers for Phenomenological Models of Epileptic Seizures.

Progress towards effective treatment of epileptic seizures has seen much improvement in the past decade. In particular, the emergence of phenomenological models of epileptic seizures specifically designed to capture the electrical seizure dynamics in the Epileptor model is inspiring new approaches to predicting and controlling seizures. These new models present in various forms and contain important but unmeasurable variables that control the occurrence of seizures. These models have been used mostly as nodes in large networks to study the complex brain behaviour of seizures. In order to use this model for the purposes of seizure forecasting or to control seizures through deep brain stimulation, the states of the model will need to be estimated. Although devices such as EEG electrodes can be related to some of the states of the model, most remain unmeasured and would require an observer (as defined in control theory) for their estimation. Additionally, we would like to consider the case for large nodes of systems where the number of electrodes is far smaller than the number of nodes being estimated. In this paper, we provide methods towards obtaining the full states of these phenomenological models using nonlinear observers. In particular, we explore the effectiveness of the Extended Kalman Filter for small networks of nodes of a smoothed sixth order Epileptor model. We show that observer design is possible for this family of systems and identify the difficulties in doing so.Clinical relevance-The methods presented here can be applied with an individual epileptic patient's EEG to reveal previously hidden biomarkers of epilepsy for seizure forecasting.

Comparison of Sub-Scalp EEG and Endovascular Stent-Electrode Array for Visual Evoked Potential Brain-Computer Interface.

Brain-computer interfaces (BCI) have the potential to improve the quality of life for persons with paralysis. Sub-scalp EEG provides an alternative BCI signal acquisition method that compromises between the limitations of traditional EEG systems and the risks associated with intracranial electrodes, and has shown promise in long-term seizure monitoring. However, sub-scalp EEG has not yet been assessed for suitability in BCI applications. This study presents a preliminary comparison of visual evoked potentials (VEPs) recorded using sub-scalp and endovascular stent electrodes in a sheep. Sub-scalp electrodes recorded comparable VEP amplitude, signal-to-noise ratio and bandwidth to the stent electrodes.Clinical relevance-This is the first study to report a comparision between sub-scalp and stent electrode array signals. The use of sub-scalp EEG electrodes may aid in the long-term use of brain-computer interfaces.

Model Parameter Estimation As Features to Predict the Duration of Epileptic Seizures From Onset.

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.

Effect of alpha range activity on SSVEP decoding in brain-computer interfaces.

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.

Feasibility of Using Source-Level Brain Computer Interface for People with Multiple Sclerosis.

This work evaluates the feasibility of using a source level Brain-Computer Interface (BCI) for people with Multiple Sclerosis (MS). Data used was previously collected EEG of eight participants (one participant with MS and seven neurotypical participants) who performed imagined movement of the right and left hand. Equivalent current dipole cluster fitting was used to assess related brain activity at the source level and assessed using dipole location and power spectrum analysis. Dipole clusters were resolved within the motor cortices with some notable spatial difference between the MS and control participants. Neural sources that generate motor imagery originated from similar motor areas in the participant with MS compared to the neurotypical participants. Power spectral analysis indicated a reduced level of alpha power in the participant with MS during imagery tasks compared to neurotypical participants. Power in the beta band may be used to distinguish between left and right imagined movement for users with MS in BCI applications.Clinical Relevance- This paper demonstrates the cortical areas activated during imagined BCI-type tasks in a participant with Multiple Sclerosis (MS), and is a proof of concept for translating BCI research to potential users with MS.

Autoregressive models for biomedical signal processing.

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.

Inhibitory stabilized network behaviour in a balanced neural mass model of a cortical column.

Strong inhibitory recurrent connections can reduce the tendency for a neural network to become unstable. This is known as inhibitory stabilization; networks that are unstable in the absence of strong inhibitory feedback because of their unstable excitatory recurrent connections are known as Inhibition Stabilized Networks (ISNs). One of the characteristics of ISNs is their "paradoxical response", where perturbing the inhibitory neurons with additional excitatory input results in a decrease in their activity after a temporal delay instead of increasing their activity. Here, we develop a model of populations of neurons across different layers of cortex. Within each layer, there is one population of inhibitory neurons and one population of excitatory neurons. The connectivity weights across different populations in the model are derived from a synaptic physiology database provided by the Allen Institute. The model shows a gradient of excitation-inhibition balance across different layers in the cortex, where superficial layers are more inhibitory dominated compared to deeper layers. To investigate the presence of ISNs across different layers, we measured the membrane potentials of neural populations in the model after perturbing inhibitory populations. The results show that layer 2/3 in the model does not operate in the ISN regime but layers 4 and 5 do operate in the ISN regime. These results accord with neurophysiological findings that explored the presence of ISNs across different layers in the cortex. The results show that there may be a systematic macroscopic gradient of inhibitory stabilization across different layers in the cortex that depends on the level of excitation-inhibition balance, and that the strength of the paradoxical response increases as the model moves closer to bifurcation points.

Electrochemistry in a Two- or Three-Electrode Configuration to Understand Monopolar or Bipolar Configurations of Platinum Bionic Implants.

Electrodes are used in vivo for chemical sensing, electrophysiological recording, and stimulation of tissue. The electrode configuration used in vivo is often optimised for a specific anatomy and biological or clinical outcomes, not electrochemical performance. Electrode materials and geometries are constrained by biostability and biocompatibility issues and may be required to function clinically for decades. We performed benchtop electrochemistry, with changes in reference electrode, smaller counter-electrode sizes, and three- or two-electrode configurations. We detail the effects different electrode configurations have on typical electroanalytical techniques used on implanted electrodes. Changes in reference electrode required correction by application of an offset potential. In a two-electrode configuration with similar working and reference/counter-electrode sizes, the electrochemical response was dictated by the rate-limiting charge transfer step at either electrode. This could invalidate calibration curves, standard analytical methods, and equations, and prevent use of commercial simulation software. We provide methods for determining if an electrode configuration is affecting the in vivo electrochemical response. We recommend sufficient details be provided in experimental sections on electronics, electrode configuration, and their calibration to justify results and discussion. In conclusion, the experimental limitations of performing in vivo electrochemistry may dictate what types of measurements and analyses are possible, such as obtaining relative rather than absolute measurements.

Continuous synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.

Objective. Brain-computer interfaces can restore various forms of communication in paralyzed patients who have lost their ability to articulate intelligible speech. This study aimed to demonstrate the feasibility of closed-loop synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.Approach. Ten participants with intractable epilepsy were temporarily implanted with intracranial electrode arrays over cortical surfaces. A decoding model that predicted audible outputs directly from patient-specific neural feature inputs was trained during overt word reading and immediately tested with overt, mimed and imagined word reading. Predicted outputs were later assessed objectively against corresponding voice recordings and subjectively through human perceptual judgments.Main results. Artificial speech sounds were successfully synthesized during overt and mimed utterances by two participants with some coverage of the precentral gyrus. About a third of these sounds were correctly identified by naïve listeners in two-alternative forced-choice tasks. A similar outcome could not be achieved during imagined utterances by any of the participants. However, neural feature contribution analyses suggested the presence of exploitable activation patterns during imagined speech in the postcentral gyrus and the superior temporal gyrus. In future work, a more comprehensive coverage of cortical surfaces, including posterior parts of the middle frontal gyrus and the inferior frontal gyrus, could improve synthesis performance during imagined speech.Significance.As the field of speech neuroprostheses is rapidly moving toward clinical trials, this study addressed important considerations about task instructions and brain coverage when conducting research on silent speech with non-target participants.