Statistical Signal Processing in Neuroscience
نویسندگان
چکیده
منابع مشابه
Statistical Signal Processing in Neuroscience
When the three of us decided to coedit a special issue on Statistical Signal Processing in Neuroscience, we implicitly knew that the submitted papers would span a wide field, the field measured in coordinate system with disparate axes: engineering-oriented or scientific; analysis of single neuron, populations or aggregate activity recordings; and known signal processing techniques or novel ones...
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Neuroscience, like many other areas of science, is experiencing a data explosion, propelled both by improvements in existing recording technologies, such as electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI), and by the advent of new and relatively new measurement and recording technologies, such as mu...
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This selection and arrangement of content as a collection is copyrighted by Clayton Scott. It is licensed under the Creative Commons Attribution 1.0 license
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Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coeecients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework based on wavelet-domain hidden Markov models (HMMs). The framework enables us to concisely model the statistical dependencies and nonGa...
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This set of notes is the primary source material for the course “Estimation, filtering and detection” taught by Prof. Alfred Hero over the period 1998-2004 at the University of Michigan Ann Arbor. The course pack is the property of Alfred O. Hero III and is copyright protected. Author can be reached at Dept. EECS, University of Michigan, Ann Arbor, MI 48109-2122 Tel: 734-763-0564. email hero@ee...
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ژورنال
عنوان ژورنال: EURASIP Journal on Advances in Signal Processing
سال: 2010
ISSN: 1687-6180
DOI: 10.1155/2009/105086