M.D. Srinath

Publications (2)2.28 Total impact

• Source

  Available from: unl.edu

  Article: Multichannel fusion models for the parametric classification of differential brain activity

  L. Gupta, Beomsu Chung, M.D. Srinath, D.L. Molfese, Hyunseok Kook
  [show abstract] [hide abstract]
  ABSTRACT: This paper introduces parametric multichannel fusion models to exploit the different but complementary brain activity information recorded from multiple channels in order to accurately classify differential brain activity into their respective categories. A parametric weighted decision fusion model and two parametric weighted data fusion models are introduced for the classification of averaged multichannel evoked potentials (EPs). The decision fusion model combines the independent decisions of each channel classifier into a decision fusion vector and a parametric classifier is designed to determine the EP class from the discrete decision fusion vector. The data fusion models include the weighted EP-sum model in which the fusion vector is a linear combination of the multichannel EPs and the EP-concatenation model in which the fusion vector is a vector-concatenation of the multichannel EPs. The discrete Karhunen-Loeve transform (DKLT) is used to select features for each channel classifier and from each data fusion vector. The difficulty in estimating the probability density function (PDF) parameters from a small number of averaged EPs is identified and the class conditional PDFs of the feature vectors of averaged EPs are, therefore, derived in terms of the PDFs of the single-trial EPs. Multivariate parametric classifiers are developed for each fusion strategy and the performances of the different strategies are compared by classifying 14-channel EPs collected from five subjects involved in making explicit match/mismatch comparisons between sequentially presented stimuli. It is shown that the performance improves by incorporating weights in the fusion rules and that the best performance is obtained using multichannel EP concatenation. It is also noted that the fusion strategies introduced are also applicable to other problems involving the classification of multicategory multivariate signals generated from multiple sources.
  IEEE Transactions on Biomedical Engineering 12/2005; · 2.28 Impact Factor
• Article: Contour sequence moments for the classification of closed planar shapes

  L. Gupta, M.D. Srinath
  [show abstract] [hide abstract]
  ABSTRACT: A technique for classifying closed planar shapes is described in which a shape is characterized by a one-dimensional sequence that represents the Euclidean distance between the centroid and all contour pixels of the shape. Contour sequences obtained from shapes belonging to the same class are similar and are considered to be different realizations of a stochastic process. Statistical moment functions derived from contour sequences are used to characterize the process and hence the shapes. It is shown that these moment functions are invariant to shape translation, rotation and scale transformations. Examples are presented to illustrate the performance of these moment functions in the classification of noisy shapes.
  Pattern Recognition.