Modeling Light Propagation through the Tissues of the Head Taking Account of Scattering Anisotropy to Optimize the Positioning of Irradiation Detectors and Sources in a Brain–Computer Interface Based on Near Infrared Spectroscopy
We describe use of the Monte Carlo modeling method to specify the parameters of near infrared light propagation though the tissues of the head, which is needed for optimizing the operation of brain–computer interfaces. The studies used a four-layer spherical model of the head consisting of skin, bone, gray matter, and white matter. The relationship between the parameters of the radiation recorded and the distance between the source and detector were obtained.
Solving the ECoG inverse problem reconstructs neural source activity , which may improve decoding in neural prostheses and can be used for accurate delineation of eloquent cortex in neurosurgical planning.
ECoG data was recorded from 8x8 ECoG grid located over the left sensorimotor cortex of a patient with epilepsy conducting flexion-extension motions of right-hand digits for 1 minute. Digit trajectories were by Perception Neuron . Anatomical model was based on structural MRI and CT. Lead-field matrix was computed with OpenMEEG . Five approaches of solving the inverse problem were implemented in MATLAB : MNE, wMNE, sLORETA, eLORETA, and LCMV beamformer [5, 6, 7]. Decoding was done using instantaneous power values in 8 narrow bands of the sensor or source-reconstructed ECoG data. Data was divided into training and test sets, Pearson correlation …
Students’ cognitive processes while their learning activities strongly influence the effectiveness of education. The indicators of such processes can be determined using an electroencephalogram analysis and, as a consequence, the success of educational process as a whole can be predicted. For this analysis, alpha and beta rhythms are traditionally used. It may be of interest to use complex indicators, characterizing several rhythms, and alternative types of rhythm, for example, theta rhythm, to improve measurement accuracy. To make it possible to use theta rhythm and to assess the level of understanding and interest of the text viewed, the preliminary experiment was carried out. The results of the experiment demonstrate that the maximum value of the dominant frequency of theta rhythm was observed in case of uninteresting or unfamiliar reading. The effect of font size on the performance of cognitive processes was also studied.
Brain-computer interfaces find application in a number of different areas and have the potential to be used for research as well as for practical purposes. The clinical use of BCI includes current studies on neurorehabilitation ([Frolov et al., 2013; Ang et al., 2010]), and there is the prospect of using BCI to restore movement and communication capabilities, providing alternative effective pathways to those that may be lost due to injury or illness. The processing of electrophysiological data requires analysis of high-dimensional, nonstationary, noisy signals reflecting complex underlying processes and structures. We have shown that for non-invasive neuroimaging methods such as EEG the potential improvement lies in the field of machine learning and involves designing data analysis algorithms that can model physiological and psychoemotional variability of the user. The development of such algorithms can be conducted in different ways, including the classical Bayesian paradigm as well as modern deep learning architectures. The interpretation of nonlinear decision rules implemented by multilayer structures would enable automatic and objective knowledge extraction from the neurocognitive experiments data. Despite the advantages of non-invasive neuroimaging methods, a radical increase in the bandwidth of the BCI communication channel and the use of this technology for the prosthesis control is possible only through invasive technologies. Electrocorticogram (ECoG) is the least invasive of such technologies, and in the final part of this work we demonstrate the possibility of using ECoG to decode the kinematic characteristics of the finger movement.
Objective. Brain-computer interface (BCI) systems are known to be vulnerable to variabilities in background states of a user. Usually, no detailed information on these states is available even during the training stage. Thus there is a need in a method which is capable of taking background states into account in an unsupervised way. Approach. We propose a latent variable method that is based on a probabilistic model with a discrete latent variable. In order to estimate the model's parameters, we suggest to use the expectation maximization (EM) algorithm. The proposed method is aimed at assessing characteristics of background states without any corresponding data labeling. In the context of asynchronous motor imagery paradigm, we applied this method to the real data from twelve able-bodied subjects with open/closed eyes serving as background states. Main results. We found that the latent variable method improved classication of target states compared to the baseline method (in seven of twelve subjects). In addition, we found that our method was also capable of background states recognition (in six of twelve subjects). Signicance. Without any supervised information on background states, the latent variable method provides a way to improve classication in BCI by taking background states into account at the training stage and then by making decisions on target states weighted by posterior probabilities of background states at the prediction stage.
We study the thermodynamics of the three-dimensional Hubbard model at half filling on approach to the Néel transition by means of large-scale unbiased diagrammatic determinant Monte Carlo simulations. We obtain the transition temperature in the strongly correlated regime, as well as the temperature dependence of the energy, entropy, double occupancy, and nearest-neighbor spin correlation function. Our results improve the accuracy of previous unbiased studies and present accurate benchmarks in the ongoing effort to realize the antiferromagnetic state of matter with ultracold atoms in optical lattices.