AA Comparison of Dynamic Modeling Approaches for Epileptic EEG Detection and Classification.

Electroencephalogram (EEG) has been intensively used as a diagnosis tool for epilepsy. The traditional diagnostic procedure relies on a recording of EEG from several days up to a few weeks, and the recordings are visually inspected by trained medical professionals. This procedure is time consuming with a high misdiagnosis rate.

Spatially regularized machine learning for task and resting-state fMRI.

Background: Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades.

New Method: A spatially regularized support vector machine (SVM) technique was developed for the reliable brain mapping in task- and resting-state. Unlike most existing SVM-based brain mapping techniques, which implement supervised classifications of specific brain functional states or disorders, the proposed method performs a semi-supervised classification for the general brain function mapping where spatial correlation of fMRI is integrated into the SVM learning.

Data-Driven and Predefined ROI-Based Quantification of Long-Term Resting-State fMRI Reproducibility.

Resting-state functional magnetic resonance imaging (fMRI) is a promising tool for neuroscience and clinical studies. However, there exist significant variations in strength and spatial extent of resting-state functional connectivity over repeated sessions in a single or multiple subjects with identical experimental conditions. Reproducibility studies have been conducted for resting-state fMRI where the reproducibility was usually evaluated in predefined regions-of-interest (ROIs).

A Study of Long-Term fMRI Reproducibility Using Data-Driven Analysis Methods.

The reproducibility of functional magnetic resonance imaging (fMRI) is important for fMRI-based neuroscience research and clinical applications. Previous studies show considerable variation in amplitude and spatial extent of fMRI activation across repeated sessions on individual subjects even using identical experimental paradigms and imaging conditions. Most existing fMRI reproducibility studies were typically limited by time duration and data analysis techniques.

Improving brain-computer interface classification using adaptive common spatial patterns.

Common Spatial Patterns (CSP) is a widely used spatial filtering technique for electroencephalography (EEG)-based brain-computer interface (BCI). It is a two-class supervised technique that needs subject-specific training data. Due to EEG nonstationarity, EEG signal may exhibit significant intra- and inter-subject variation.

A unified machine learning method for task-related and resting state fMRI data analysis.

Functional magnetic resonance imaging (fMRI) aims to localize task-related brain activation or resting-state functional connectivity. Most existing fMRI data analysis techniques rely on fixed thresholds to identify active voxels under a task condition or functionally connected voxels in the resting state. Due to fMRI non-stationarity, a fixed threshold cannot adapt to intra- and inter-subject variation and provide a reliable mapping of brain function.

A SVM-based quantitative fMRI method for resting-state functional network detection.

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results.

A kernel machine-based fMRI physiological noise removal method.

Functional magnetic resonance imaging (fMRI) technique with blood oxygenation level dependent (BOLD) contrast is a powerful tool for noninvasive mapping of brain function under task and resting states. The removal of cardiac- and respiration-induced physiological noise in fMRI data has been a significant challenge as fMRI studies seek to achieve higher spatial resolutions and characterize more subtle neuronal changes. The low temporal sampling rate of most multi-slice fMRI experiments often causes aliasing of physiological noise into the frequency range of BOLD activation signal.

Identification and attenuation of physiological noise in fMRI using kernel techniques.

Functional magnetic resonance imaging (fMRI) techniques enable noninvasive studies of brain functional activity under task and resting states. However, the analysis of brain activity could be significantly affected by the cardiac- and respiration-induced physiological noise in fMRI data. In most multi-slice fMRI experiments, the temporal sampling rates are not high enough to critically sample the physiological noise, and the noise is aliased into frequency bands where useful brain functional signal exists, compromising the analysis.

Mapping rabbit whisker barrels using discriminant analysis of high field fMRI data.

High field (>4T) functional magnetic resonance imaging (fMRI) techniques provide increased spatial resolution that enables the noninvasive, repeatable study of the sensory cortices at the level of their basic functional units. The examination of these units is important for studies of sensory information processing, learning- or experience-related brain plasticity, or the fundamental relationship between hemodynamic and neuronal activity. However functional units cannot always be distinguished from their surrounding areas by conventional activation mapping methods such as correlation or hypothesis tests, which only consider temporal variation within each individual voxel.

Unsupervised spatiotemporal fMRI data analysis using support vector machines.

In this work we present a new support vector machine (SVM)-based method for fMRI data analysis. SVM has been shown to be a powerful, efficient data-driven tool in pattern recognition, and has been applied to the supervised classification of brain cognitive states in fMRI experiments. We examine the unsupervised mapping of activated brain regions using SVM.

Functional magnetic resonance imaging of delay and trace eyeblink conditioning in the primary visual cortex of the rabbit.

The primary sensory cortices have been shown in recent years to undergo experience- and learning-related plasticity under a variety of experimental circumstances. In this study, we used functional magnetic resonance imaging (fMRI) in parallel with both delay and trace eyeblink conditioning to image the learning-related functional activation within the primary visual cortex (V1) of awake, behaving rabbits. We expected that the differing level of forebrain dependence between these two conditioning paradigms should produce a differential blood oxygenation level-dependent (BOLD) functional response in V1.

Selecting salient frames for spatiotemporal video modeling and segmentation.

We propose a new statistical generative model for spatiotemporal video segmentation. The objective is to partition a video sequence into homogeneous segments that can be used as "building blocks" for semantic video segmentation. The baseline framework is a Gaussian mixture model (GMM)-based video modeling approach that involves a six-dimensional spatiotemporal feature space.