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Classification-based data analysis is receiving increasing attention in neuroimaging. Here we decode the real-life relation between senders and perceivers of facial signals of affect by measuring the similarity of their brain activity during affective communication. Our results show that the brain activity of romantic partners is more similar during ongoing affective communication than the brain activity...
We propose to approach the detection of patients affected by schizophrenia by means of dissimilarity-based classification techniques applied to brain magnetic resonance images. Instead of working with features directly, pairwise distances between expert delineated regions of interest (ROIs) are considered as representations based on which learning and classification can be performed. Experiments were...
Multivariate pattern classification is emerging as a powerful tool for analysis of fMRI group studies and has the advantage that it utilizes spatial correlation between brain voxels. However, this makes quantifying the information content of brain voxels and localizing informative brain regions difficult. In this paper we a probabilistic Gaussian process classifiers to compute a sensitive measure...
Empirical Mode Decomposition has emerged in recent years as a promising data analysis method to adaptively decompose non-linear and non-stationary signals. Here we introduce multi-EMD, to be used where there are many thousands of signals to analyse and compare, such as is common in the analysis of functional neuroimages. The number of component signals found through Empirical Mode Decomposition varies...
Brain-Computer Interfaces (BCI) that rely upon epidural electrocorticographic signals may become a promising tool for neurorehabilitation of patients with severe hemiparatic syndromes due to cerebrovascular, traumatic or tumor-related brain damage. Here, we show in a patient-based feasibility study that online classification of arm movement intention is possible. The intention to move or to rest can...
This article presents a robust method for decoding mental states from non-invasive electroencephalographic signals. We particularly address two issues related to asynchronous Brain-Computer Interfaces (BCIs). First our method based on robust learning goes beyond the usual assumption that subjects perform mental tasks with a constant accuracy along each whole trial. We show that the combination of...
While medical imaging typically provides massive amounts of data, the automatic extraction of relevant information in a given applicative context remains a difficult challenge in general. With functional MRI (fMRI), the data provide an indirect measurement of brain activity, that can be related to behavioral information. It is now standard to formulate this relation as a machine learning problem where...
Classifiers in a high dimensional space based on the signals of multiple electrodes in EEG-based BCIs suffer from the curse of dimensionality due to the limited training dataset. In order to tackle this problem, we design a framework of two-layer hidden Markov models (HMMs) for probabilistic classification of EEG signals. We first independently model the characteristics of EEG signals embedded in...
There has been a growing interest in applying pattern classification methods to discriminate patients with neurodegenerative diseases from normal controls using structural MRI data. Despite an impressive array of publications, most applications are framed as discrimination problems with categorical class decisions. For many clinical applications, probabilistic estimation would be more useful, especially...
Functional connectivity graphs are fully defined by their weighted adjacency matrix. Beyond the computation of graph-theoretical measures, we propose to use these graphs for inter-subject classification. Since they form a class of graphs with undirected edges and fixed number and ordering of vertices, vector space graph embedding techniques can be used to provide good classification performance. We...
Classification-based approaches for data analysis are provoking wide interest and increasing adoption within the neuroscience community. Topics like "brain decoding", "multi-voxel pattern analysis" and "brain-computer interface" are prominent examples of this trend. The core problem of these investigations is hypothesis testing, i.e., finding evidence of some effect produced...
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