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This study examines the characteristics of the electric field (E-field) induced in the brain by electroconvulsive therapy (ECT) and magnetic seizure therapy (MST). The electric field induced by five ECT electrode configurations (bilateral, bifrontal, right unilateral, focal electrically administered seizure therapy, and frontomedial) as well as an MST coil configuration (circular) was computed in...
Transcranial direct current stimulation (tDCS) has been used to affect the excitability of neurons within the cerebral cortex. Improvements in motor learning have been found in multiple studies when tDCS was applied to the motor cortex during or before task learning is performed. The application of tDCS to motor imagery, a cognitive task showing activation in similar areas to motor execution, has...
In this paper, we introduced an automated TMS system with robot control and optical sensor combined with neuronavigation software. By using the robot, the TMS coil can be accurately positioned over any preselected brain region. The neuronavigation system provides an accurate positioning of a magnetic coil in order to induce a specific cortical excitation. An infrared optical measurement device is...
Neuroscience is an increasingly multidisciplinary and highly cooperative field where neuroimaging plays an important role. Neuroimaging rapid evolution is demanding for a growing number of computing resources and skills that need to be put in place at every lab. Typically each group tries to setup their own servers and workstations to support their neuroimaging needs, having to learn from Operating...
Transcranial magnetic stimulation (TMS) was proposed in 1985. Nevertheless, its wider use in the treatment of several neurologic diseases has been hindered by its inability to stimulate deep-brain regions. This is mainly due to the physical limiting effect arising from the presence of surface discontinuities, particularly between the scalp and air. Here, we present the optimization of a system of...
Biomedical signals aid in the diagnosis of different disorders and abnormalities. When targeting lossy compression of such signals, the medically relevant information that lies within the data should maintain its accuracy and thus its reliability. In fact, signal models that are inspired by the biophysical properties of the signals at hand allow for a compression that preserves more naturally the...
Coupled nonlinear oscillator models of EEG signals during resting eyes-closed and eyes-open conditions are presented based on Duffing-van der Pol oscillator dynamics. The frequency and information entropy contents of the output of the nonlinear model and the actual EEG signal is matched through an optimization algorithm. The framework is used to model and compare EEG signals recorded from Alzheimer's...
To perform large-scale simulations of the brain or build biologically-inspired cognitive architectures, it is essential to have a succinct and flexible model of spiking neurons. The model should be able to capture the nonlinear dynamical properties of various types of neurons and the nonstationary properties such as the spike-timing-dependent plasticity (STDP). In this paper, we propose a generalized...
This paper reports diffusion weighted MRI measurements of cyclohexane in a novel diffusion tensor MRI phantom composed of hollow coaxial electrospun fibers (average diameter 10.2 μm). Recent studies of the phantom demonstrated its potential as a calibration standard at low b values (less than 1000 s/mm2) for mean diffusivity and fractional anisotropy. In this paper, we extend the characterization...
Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for singlechannel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal...
EEG signals, which can record the electrical activity along the scalp, provide researchers a reliable channel for investigating human emotional states. In this paper, a new algorithm, manifold regularized extreme learning machine (MRELM), is proposed for recognizing human emotional states (positive, neutral and negative) from EEG data, which were previously evoked by watching different types of movie...
Thalamus is a very important part of the human brain. It has been reported to act as a relay for the messaging taking place between the cortical and sub-cortical regions of the brain. In the present study, we analyze the functional network between both hemispheres of the brain with the focus on thalamus. We used conditional Granger causality (CGC) and time-resolved partial directed coherence (tPDC)...
Independent component analysis (ICA) has been widely applied to identify brain functional networks from multiple-subject fMRI. However, the best approach to handle artifacts is not yet clear. In this work, we study and compare two ICA approaches for artifact removal using simulations and real fMRI data. The first approach, recommended by the human connectome project, performs ICA on individual data...
Multivariate Pattern Analysis (MVPA) is frequently used to decode cognitive states from brain activities in fMRI study. Due to the discrepancy between sample and feature size, MVPA methods are suffered from the overfitting problem. This paper addresses this issue by introducing sparse modelling along with its advanced decoding method, Compressive Sensing (CS). As brain voxels have highly correlated...
While scalp EEG/MEG source imaging have been extensively studied in the last two decades, the case of source localization from invasive measurements has resulted in few works to date. Yet there is a lot to gain from stereo-electroencephalographic (SEEG) recordings, providing high signal to noise ratio measurements of the explored brain structures. The SEEG setup consists in multi-contact electrodes...
The current treatment for ambulation after spinal cord injury (SCI) is to substitute the lost behavior with a wheelchair; however, this can result in many co-morbidities. Thus, novel solutions for the restoration of walking, such as brain-computer interfaces (BCI) and functional electrical stimulation (FES) devices, have been sought. This study reports on the first electroencephalogram (EEG) based...
Despite the prevalence of stroke-induced gait impairment due to foot drop, current rehabilitative practices to improve gait function are limited, and orthoses can be uncomfortable and do not provide long-lasting benefits. Therefore, novel modalities that may facilitate lasting neurological and functional improvements, such as brain-computer interfaces (BCIs), have been explored. In this article, we...
A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as...
In brain tumor surgery, soft-tissue deformation, known as brain shift, introduces inaccuracies in the application of the preoperative surgical plan and impedes the advancement of image-guided surgical (IGS) systems. Considerable progress in using patient-specific biomechanical models to update the preoperative images intraoperatively has been made. These model-update methods rely on accurate intraoperative...
We present a computational model of the optic pathway which has been adapted to simulate cortical responses to visual-prosthetic stimulation. This model reproduces the statistically observed distributions of spikes for cortical recordings of sham and maximum-intensity stimuli, while simultaneously generating cellular receptive fields consistent with those observed using traditional visual neuroscience...
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