Noninvasive brain stimulation during EEG improves machine learning classication in chronic stroke.

Background: In individuals with chronic stroke and hemiparesis, noninvasive brain stimulation (NIBS) may be used as an adjunct to therapy for improving motor recovery. Specific states of movement during motor recovery are more responsive to brain stimulation than others, thus a system that could auto-detect movement state would be useful in correctly identifying the most effective stimulation periods. The aim of this study was to compare the performance of different machine learning models in classifying movement periods during EEG recordings of hemiparetic individuals receiving noninvasive brain stimulation.

Theta oscillons in behaving rats.

Recently discovered constituents of the brain waves-the -provide high-resolution representation of the extracellular field dynamics. Here we study the most robust, highest-amplitude oscillons that manifest in actively behaving rats and generally correspond to the traditional -waves. We show that the resemblances between -oscillons and the conventional -waves apply to the ballpark characteristics-mean frequencies, amplitudes, and bandwidths.

Theta oscillons in behaving rats.

Recently discovered constituents of the brain waves-the -provide high-resolution representation of the extracellular field dynamics. Here we study the most robust, highest-amplitude oscillons that manifest in actively behaving rats and generally correspond to the traditional -waves. We show that the resemblances between -oscillons and the conventional -waves apply to the ballpark characteristics-mean frequencies, amplitudes, and bandwidths.

Rapid Spectral Dynamics in Hippocampal Oscillons.

Neurons in the brain are submerged into oscillating extracellular potential produced by synchronized synaptic currents. The dynamics of these oscillations is one of the principal characteristics of neurophysiological activity, broadly studied in basic neuroscience and used in applications. However, our interpretation of the brain waves' structure and hence our understanding of their functions depend on the mathematical and computational approaches used for data analysis.

The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface.

This study presents a novel application of soft-computing through intelligent, neural networks backpropagated by Levenberg-Marquardt scheme (NNs-BLMS) to solve the mathematical model of unsteady thin film flow of magnetized Maxwell fluid with thermo-diffusion effects and chemical reaction (TFFMFTDECR) over a horizontal rotating disk. The expression for thermophoretic velocity is accounted. Energy expression is deliberated with the addition of non-uniform heat source.