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22nd IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Alkan, Sarper Yarman-Vural, Fatos T. Cankaya Univ Bilissel Bilimer Ana Bilim Dali Yenimahalle Ankara Turkey Cankaya Univ Mekatron Muhendisligi Bolumu Yenimahalle Ankara Turkey Cankaya Univ Orta Dogu Tekn Univ Yenimahalle Ankara Turkey Orta Dogu Tekn Univ Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781479948741

In this study, we provide a methodology to localize the brain regions that contribute to semantic category classification. For this purpose we first cluster the data using spectral clustering. Then we extract local features within each cluster by using mesh-arc descriptors. Finally, we test the classification accuracy of each cluster against a hypothesis testing measure we provide here. We have found that, for the experimental task at hand, calcerine fissure and angular gyrus were most effective in classification. These results are shown to be match well with the nature of the experiment. Thus the validity of our approach is confirmed.

关键词： Functional Magnetic Resonance Imaging (fMRI) feature extraction feature clustering brain decoding classification hypothesis testing Multi Voxel Pattern Analysis (MVPA)

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Classification of inter-subject fMRI data based on graph kernels

Classification of inter-subject fMRI data based on graph ker...

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4th International Workshop on Pattern Recognition in Neuroimaging (PRNI)

作者： Vega-Pons, Sandro Avesani, Paolo Andric, Michael Hasson, Uri Fdn Bruno Kessler NeuroInformat Lab NILab Trento Italy Univ Trento CIMeC Trento Italy

ISBN: (纸本)9781479941490

f The analysis of human brain connectivity networks has become an increasingly prevalent task in neuroimaging. A few recent studies have shown the possibility of decoding brain states based on brain graph classification. Graph kernels have emerged as a powerful tool for graph comparison that allows the direct use of machine learning classifiers on brain graph collections. They allow classifying graphs with different number of nodes and therefore the inter-subject analysis without any kind of previous alignment of individual subject's data. Using whole-brain fMRI data, in this paper we present a method based on graph kernels that provides above-chance accuracy results for the inter-subject discrimination of two different types of auditory stimuli. We focus our research on determining whether this method is sensitive to the relational information in the data. Indeed, we show that the discriminative information is not only coming from topological features of the graphs like node degree distribution, but also from more complex relational patterns in the neighborhood of each node. Moreover, we investigate the suitability of two different graph representation methods, both based on data-driven parcellation techniques. Finally, we study the influence of noisy connections in our graphs and provide a way to alleviate this problem.

关键词： brain decoding connectivity graphs graph kernels brain parcellation inter-subject classification

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decoding visual brain states from fMRI using an ensemble of classifiers

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PATTERN RECOGNITION 2012年第6期45卷 2064-2074页

作者： Cabral, Carlos Silveira, Margarida Figueiredo, Patricia Univ Tecn Lisboa Inst Super Tecn Lisbon Portugal Inst Syst & Robot Lisbon Portugal

decoding perceptual or cognitive states based on brain activity measured using functional magnetic resonance imaging (fMRI) can be achieved using machine learning algorithms to train classifiers of specific stimuli. However, the high dimensionality and intrinsically low signal to noise ratio (SNR) of fMRI data poses great challenges to such techniques. The problem is aggravated in the case of multiple subject experiments because of the high inter-subject variability in brain function. To address these difficulties, the majority of current approaches uses a single classifier. Since, in many cases, different stimuli activate different brain areas, it makes sense to use a set of classifiers each specialized in a different stimulus. Therefore, we propose in this paper using an ensemble of classifiers for decoding fMRI data. Each classifier in the ensemble has a favorite class or stimulus and uses an optimized feature set for that particular stimulus. The output for each individual stimulus is therefore obtained from the corresponding classifier and the final classification is achieved by simply selecting the best score. The method was applied to three empirical fMRI datasets from multiple subjects performing visual tasks with four classes of stimuli. Ensembles of GNB and k-NN base classifiers were tested. The ensemble of classifiers systematically outperformed a single classifier for the two most challenging datasets. In the remaining dataset, a ceiling effect was observed which probably precluded a clear distinction between the two classification approaches. Our results may be explained by the fact that different visual stimuli elicit specific patterns of brain activation and indicate that an ensemble of classifiers provides an advantageous alternative to commonly used single classifiers, particularly when decoding stimuli associated with specific brain areas. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： fMRI Retinotopic mapping Visual localizer brain decoding Machine learning Ensemble of classifiers

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Mesh Learning for Object Classification using fMRI Measurements

Mesh Learning for Object Classification using fMRI Measureme...

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20th IEEE International Conference on Image Processing (ICIP)

作者： Ekmekci, Omer Firat, Orhan Ozay, Mete Oztekin, Ilke Vural, Fatos T. Yarman Oztekin, Uygar Middle E Tech Univ Dept Comp Engn TR-06531 Ankara Turkey Koc Univ Dept Psychol TR-34450 Istanbul Turkey Google Inc Mountain View CA USA

ISBN: (纸本)9781479923410

Machine learning algorithms have been widely used as reliable methods for modeling and classifying cognitive processes using functional Magnetic Resonance Imaging (fMRI) data. In this study, we aim to classify fMRI measurements recorded during an object recognition experiment. Previous studies focus on Multi Voxel Pattern Analysis (MVPA) which feeds a set of active voxels in a concatenated vector form to a machine learning algorithm to train and classify the cognitive processes. In most of the MVPA methods, after an image preprocessing step, the voxel intensity values are fed to a classifier to train and recognize the underlying cognitive process. Sometimes, the fMRI data is further processed for de-noising or feature selection where techniques, such as Generalized Linear Model (GLM), Independent Component Analysis (ICA) or Principal Component Analysis are employed. Although these techniques are proved to be useful in MVPA, they do not model the spatial connectivity among the voxels. In this study, we attempt to represent the local relations among the voxel intensity values by forming a mesh network around each voxel to model the relationship of a voxel and its surroundings. The degree of connectivity of a voxel to its surroundings is represented by the arc weights of each mesh. The arc weights, which are estimated by a linear regression model, are fed to a classifier to discriminate the brain states during an object recognition task. This approach, called Mesh Learning, provides a powerful tool to analyze various cognitive states using fMRI data. Compared to traditional studies which focus either merely on multi-voxel pattern vectors or their reduced-dimension versions, the suggested Mesh Learning provides a better representation of object recognition task. Various machine learning algorithms are tested to compare the suggested Mesh Learning to the state-of-the art MVPA techniques. The performance of the Mesh Learning is shown to be higher than that of the availabl

关键词： Functional Magnetic Resonance Imaging (fMRI) feature extraction machine learning brain decoding classification Multi Voxel Pattern Analysis (MVPA)

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Discrete Cosine Transform for MEG Signal decoding

Discrete Cosine Transform for MEG Signal Decoding

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3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI)

作者： Kia, Seyed Mostafa Olivetti, Emanuele Avesani, Paolo Bruno Kessler Fdn Neuroinformat Lab NILab Trento Italy

ISBN: (纸本)9780769550619

In this study, we propose the discrete cosine transform coefficients as a new and effective set of features for recognizing patterns of brain activity in MEG recording. We claim that computing DCT coefficients on the time-frequency representation of MEG signals is an efficient technique to reduce the dimensionality of feature space without losing discriminative power in brain decoding tasks. Our classification results on single-trial MEG decoding suggest that DCT is a viable method comparing to standard methods and it improves decoding accuracy by preserving the dynamic patterns of signal in time, frequency and space domains.

关键词： pattern recognition brain decoding MEG DCT

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Mesh Learning for Object Classification using fMRI Measurements

Mesh Learning for Object Classification using fMRI Measureme...

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IEEE International Conference on Image Processing

作者： Omer Ekmekci Orhan Firat Mete Ozay Ilke Oztekin Fatos T. Yarman Vural Uygar Oztekin the Department of Computer Engineering Middle East Technical University Inonu Bulvari 06531 Ankara Turkey the Department of Psychology Koc University Rumeli Feneri Yolu Sariyer 34450 Istanbul Turkey Google Inc. California USA

ISBN: (纸本)9781479923427

关键词： Functional Magnetic Resonance Imaging (fMRI) feature extraction machine learning brain decoding classification Multi Voxel Pattern Analysis (MVPA) pixel Feature extraction Machine learning algorithms fMRI object recognition cognitive process Medical Subject Headings Classifiers Mesh Mesh Mesh networks

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decoding brain States Using Functional Magnetic Resonance Imaging

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BIOMEDICAL ENGINEERING LETTERS 2011年第2期1卷 82-88页

作者： Lee, Dongha Park, Bumhee Jang, Changwon Park, Hae-Jeong Yonsei Univ Coll Med Brain Korea Project Med Sci 21 Seoul South Korea Yonsei Univ Coll Med Severance Biomed Sci Inst Dept RadiolNucl Med Seoul South Korea

Most leading research in basic and clinical neuroscience has been carried out by functional magnetic resonance imaging ( fMRI), which detects the blood oxygenation level dependent signals associated with neural activities. Among new fMRI applications, brain decoding is an emerging research area, which infers mental states from fMRI signals. brain decoding using fMRI includes classification, identification, and reconstruction of brain states. It is generally conducted using multi-voxel pattern analysis based on neuroscientific evidence that brain functions are mediated by distributed activation patterns. brain decoding techniques have been successful in diverse applications such as the brain computer interface, patient monitoring, and neurofeedback. These techniques have expanded our understanding of how the brain encodes distinct information. In the current paper, we reviewed recent fMRI-based brain decoding techniques and applications. We also discussed the potential implications of brain decoding in neuroscience.

关键词： fMRI Mind reading brain decoding Neurofeedback Real time fMRI

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decoding brain states from fMRI connectivity graphs

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NEUROIMAGE 2011年第2期56卷 616-626页

作者： Richiardi, Jonas Eryilmaz, Hamdi Schwartz, Sophie Vuilleumier, Patrik Van de Ville, Dimitri Ecole Polytech Fed Lausanne Med Image Proc Lab CH-1015 Lausanne Switzerland Univ Geneva Med Image Proc Lab CH-1211 Geneva 4 Switzerland Univ Geneva Lab Neurol & Imaging Cognit CH-1211 Geneva 4 Switzerland

Functional connectivity analysis of fMRI data can reveal synchronised activity between anatomically distinct brain regions. Here, we extract the characteristic connectivity signatures of different brain states to perform classification, allowing us to decode the different states based on the functional connectivity patterns. Our approach is based on polythetic decision trees, which combine powerful discriminative ability with interpretability of results. We also propose to use ensemble of classifiers within specific frequency subbands, and show that they bring systematic improvement in classification accuracy. Exploiting multi-band classification of connectivity graphs is also proposed, and we explain theoretical reasons why the technique could bring further improvement in classification performance. The choice of decision trees as classifier is shown to provide a practical way to identify a subset of connections that distinguishes best between the conditions, permitting the extraction of very compact representations for differences between brain states, which we call discriminative graphs. Our experimental results based on strict train/test separation at all stages of processing show that the method is applicable to inter-subject brain decoding with relatively low error rates for the task considered. (C) 2010 Elsevier Inc. All rights reserved.

关键词： fMRI brain decoding Functional connectivity Graphs Decision tree

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Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity

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NEUROIMAGE 2012年第3期62卷 2021-2033页

作者： Richiardi, Jonas Gschwind, Markus Simioni, Samanta Annoni, Jean-Marie Greco, Beatrice Hagmann, Patric Schluep, Myriam Vuilleumier, Patrik Van De Ville, Dimitri Univ Geneva Dept Radiol & Med Informat Geneva Switzerland Ecole Polytech Fed Lausanne Inst Bioengn CH-1015 Lausanne Switzerland CHU Vaudois Dept Clin Neurosci CH-1011 Lausanne Switzerland Univ Lausanne Lausanne Switzerland HUG Dept Neurol Geneva Switzerland Univ Geneva Univ Med Ctr CMU Dept Neurosci Geneva Switzerland Univ Fribourg Neurol Unit CH-1700 Fribourg Switzerland Merck Serono Geneva Switzerland CHU Vaudois Dept Med Radiol CH-1011 Lausanne Switzerland Ecole Polytech Fed Lausanne Signal Proc Lab 5 CH-1015 Lausanne Switzerland

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc;our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (< 0.11 Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p < 0.005) and specificity of 86% (p < 0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (rho = 0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers. (C) 2012 Elsevier Inc. All rights reserved.

关键词： brain decoding brain networks Classification Functional magnetic resonance imaging Imaging marker

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Continuous decoding of grasping tasks for a prospective implantable cortical neuroprosthesis

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2012年第1期9卷 84-84页

作者： Carpaneto, Jacopo Raos, Vassilis Umilta, Maria A. Fogassi, Leonardo Murata, Akira Gallese, Vittorio Micera, Silvestro Scuola Super Sant Anna BioRobot Inst Neural Engn Area Pisa Italy Univ Crete Dept Basic Sci Fac Med Sch Hlth Sci Iraklion Greece Fdn Res & Technol Hellas Inst Appl & Computat Math Iraklion Greece Univ Parma Physiol Sect Dept Neurosci I-43100 Parma Italy Italian Inst Technol RTM Parma Italy Univ Parma Dept Psychol I-43100 Parma Italy Kinki Univ Fac Med Dept Physiol Osaka Japan Ecole Polytech Fed Lausanne Ctr Neuroprosthet Translat Neural Engn Lab Lausanne Switzerland Ecole Polytech Fed Lausanne Inst Bioengn Lausanne Switzerland

Background: In the recent past several invasive cortical neuroprostheses have been developed. Signals recorded from the motor cortex (area MI) have been decoded and used to control computer cursors and robotic devices. Nevertheless, few attempts have been carried out to predict different grips. A Support Vector Machines (SVMs) classifier has been trained for a continuous decoding of four/six grip types using signals recorded in two monkeys from motor neurons of the ventral premotor cortex (area F5) during a reach-to-grasp task. Findings: The results showed that four/six grip types could be extracted with classification accuracy higher than 96% using window width of 75-150 ms. Conclusions: These results open new and promising possibilities for the development of invasive cortical neural prostheses for the control of reaching and grasping.

关键词： Ventral premotor cortex (area F5) brain decoding Grasping Neuroprosthesis

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