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Movement direction for Brain Machine Interface (BMI) can be decoded successfully using Local Field Potentials (LFP) and Single Unit Activity (SUA). A major challenge when dealing with the intra-cortical recordings is to develop decoders that are robust in time. In this paper we present for the first time a technique that uses the qualitative information derived from multiple LFP channels rather than...
In this paper we use the modified and integrated version of the balloon model in the analysis of fMRI data. We propose a new state space model realization for this balloon model and represent it with the standard A,B,C and D matrices widely used in system theory. A second order Padé approximation with equal numerator and denominator degree is used for the time delay approximation in the modeling...
The functional connectivity of the brain is investigated through the study of multivariate autoregressive models (MVAR) applied to multichannel EEG recordings. After the identification of the model, different indices can be calculated that are able to quantify direct and indirect functional connections between cortical areas. These methodology is used for the investigation of possible connectivity...
Autoregulation refers to the automatic adjustment of blood flow to supply the required oxygen and glucose and remove waste, in proportion to the tissue's requirement at any instant of time. For the brain, cerebral autoregulation is an active process by which cerebral blood flow is controlled at an approximately steady level despite changes in the arterial blood pressure. Robust assessment of the cerebral...
A new adaptive motor controller was constructed, and tested on the control of a 2-wheeled balancing robot in simulation and real world. The controller consists of a feedback (PD) controller and a cerebellar neuronal network model. The structure of the cerebellar model was configured based upon known anatomical neuronal connection in the cerebellar cortex. Namely it consists of 120 granular (Gr) cells,...
Transcranial Magnetic Stimulation (TMS) in the rat is a powerful tool for investigating brain function. However, the state-of-the-art experiments are considerably limited because the stimulation usually affects undesired anatomical structures. A simulation of a conductive shield plate placed between the coil stimulator and the rat brain during TMS is presented. The Finite Element (FE) method is used...
The brain is a biological system with dynamic interactions between its sub-systems. The complexity of this system poses a challenge for identifying functional networks underlying observed neural activity. Current imaging approaches index local neural activity very well, but there is an increasing need for methods that quantify the interaction between regional activations. In this paper, we focus on...
Glioma is the most aggressive type of brain tumor. Several mathematical models have been developed during the last two decades, towards simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. The proposed diffusive models have mainly used finite differences...
We present a reduced order finite element (FE) algorithm suitable for real-time nonlinear simulation of soft tissues. A dynamic FE formulation with explicit time integration is employed. We demonstrate significant computation acceleration by performing the time integration in a low-dimensional generalised basis, generated from a set of a priori training simulations. The key mechanism for the acceleration...
Brain machine interfaces with chronically implanted microelectrode arrays for signal acquisition require algorithms for successful detection and classification of neural spikes. During the design of such algorithms, signals with a priori known characteristics need to be present. A common way to establish such signals is to model the recording environment, simulate the recordings and store ground truth...
This study presents a preliminary analysis of the relationship between electroencephalographic (EEG) and electrocorticographic (ECoG) event-related potentials (ERPs) recorded from from a single patient using a brain-computer interface (BCI) speller. The patient had medically intractable epilepsy and underwent temporary placement of an intracranial ECoG grid electrode array to localize seizure foci...
Motor unit layout algorithms have a significant effect on motor unit fibre densities recorded. Motor unit fibre densities are affected by both the method used to place the motor unit territories, and the mechanism by which muscle fibres are assigned to motor units. The first of these should emulate the process by which separate motor neurons create overlapping territories that cover the muscle cross...
In this paper, a hybrid EEG-based brain computer interface (BCI) is designed for two-dimensional cursor control. In our approach, two brain activity patterns, i.e., motor imagery and P300 potential, are used for controlling the horizontal and the vertical movements of the cursor respectively. A real-time BCI system based on this approach is implemented and evaluated through an online experiment. Six...
A re-entrant resonant cavity applicator system for non-invasive brain tumor hyperthermia treatments was presented. We have already confirmed the effectiveness of the heating properties of this heating system with cylindrical agar phantoms and with computer simulations. This paper describes the specific absorption rate (SAR) analysis of this heating system for non-invasive brain tumor hyperthermia...
The responsible pathological mechanisms of essential tremor are not yet clear. In order to understand the mechanisms of the central network its sources need to be found. The cortical sources of both the basic and first “harmonic” frequency of essential tremor are addressed in this paper. The power and coherence were estimated using the multitaper method for EEG and EMG data from 6 essential tremor...
Graphical models are powerful tools to infer statistical relationships between simultaneously observed random variables. Here, we used Dynamic Bayesian Networks (DBN) to infer causal relationships between simultaneously recorded neurons in the rat somatosensory (barrel) cortex in response to whisker stimulation. DBNs attempt to explain the activity of the observed neurons by searching for the best...
This paper presents a new method to estimate dynamic neural activity from EEG signals. The method is based on a Kalman filter approach, using physiological models that take both spatial and temporal dynamics into account. The filter's performance (in terms of estimation error) is analyzed for the cases of linear and nonlinear models having either time invariant or time varying parameters. The best...
Deep Brain Stimulation (DBS) is an effective surgical therapy for the treatment of movement disorders in Parkinson's disease (PD) and other neurological pathologies. DBS is known to modulate the spiking activity of the neurons within the basal ganglia, but how such modulation impacts the primary sensorimotor cortex is still uncertain. In this study a monkey was stimulated with DBS at several frequencies...
This paper investigates the applicability of multilevel macroscopic models for simulating solid tumor growth in the invasive glioblastoma multiforme (GBM) case. The continuum case approach tumor model based on the diffusion reaction equation is evaluated on a pre-segmented tomographic atlas where all tissue properties are known a priori. The atlas is further registered on a real clinical case where...
The problem of model falsification or model invalidation appears in several areas where we are interested in distinguishing among an eligible set of dynamic systems. In the context of fMRI studies of brain activity, modeling the haemodynamic response function (HRF) is a critical step. The estimation of the dynamic system describing a biophysical model of the HRF may leave much uncertainty on the exact...
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