42 research outputs found
Simultaneous Matrix Diagonalization for Structural Brain Networks Classification
This paper considers the problem of brain disease classification based on
connectome data. A connectome is a network representation of a human brain. The
typical connectome classification problem is very challenging because of the
small sample size and high dimensionality of the data. We propose to use
simultaneous approximate diagonalization of adjacency matrices in order to
compute their eigenstructures in more stable way. The obtained approximate
eigenvalues are further used as features for classification. The proposed
approach is demonstrated to be efficient for detection of Alzheimer's disease,
outperforming simple baselines and competing with state-of-the-art approaches
to brain disease classification
Improving the Quality of EEG Data in Patients With Alzheimers Disease Using ICA
Does Independent Component Analysis (ICA) denature EEG
signals? We applied ICA to two groups of subjects (mild Alzheimer
patients and control subjects). The aim of this study was to examine
whether or not the ICA method can reduce both group di®erences and
within-subject variability. We found that ICA diminished Leave-One-
Out root mean square error (RMSE) of validation (from 0.32 to 0.28),
indicative of the reduction of group di®erence. More interestingly, ICA
reduced the inter-subject variability within each group (¾ = 2:54 in the
± range before ICA, ¾ = 1:56 after, Bartlett p = 0.046 after Bonfer-
roni correction). Additionally, we present a method to limit the impact
of human error (' 13:8%, with 75.6% inter-cleaner agreement) during
ICA cleaning, and reduce human bias. These ¯ndings suggests the novel
usefulness of ICA in clinical EEG in Alzheimer's disease for reduction of
subject variability
An Introduction to EEG Source Analysis with an illustration of a study on Error-Related Potentials
International audienceOver the last twenty years blind source separation (BSS) has become a fundamental signal processing tool in the study of human electroencephalography (EEG), other biological data, as well as in many other signal processing domains such as speech, images, geophysics and wireless communication (Comon and Jutten, 2010). Without relying on head modeling BSS aims at estimating both the waveform and the scalp spatial pattern of the intracranial dipolar current responsible of the observed EEG, increasing the sensitivity and specificity of the signal received from the electrodes on the scalp. This chapter begins with a short review of brain volume conduction theory, demonstrating that BSS modeling is grounded on current physiological knowledge. We then illustrate a general BSS scheme requiring the estimation of second-order statistics (SOS) only. A simple and efficient implementation based on the approximate joint diagonalization of covariance matrices (AJDC) is described. The method operates in the same way in the time or frequency domain (or both at the same time) and is capable of modeling explicitly physiological and experimental source of variations with remarkable flexibility. Finally, we provide a specific example illustrating the analysis of a new experimental study on error-related potentials