Multivariate Statistical Analysis in Neuroscience | Advanced Mathematical Modeling Applied to Electroencephalographic Signals in Complex Data Problems

Marco Ivaldi (u. a.)

ISBN 10: 3656973768 / ISBN 13: 9783656973768

Published by GRIN Verlag, 2015

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Multivariate Statistical Analysis in Neuroscience | Advanced Mathematical Modeling Applied to Electroencephalographic Signals in Complex Data Problems | Marco Ivaldi (u. a.) | Taschenbuch | 184 S. | Englisch | 2015 | GRIN Verlag | EAN 9783656973768 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: Multivariate Statistical Analysis in Neuroscience | Advanced Mathematical Modeling Applied to Electroencephalographic Signals in Complex Data Problems
• Publisher: GRIN Verlag
• Publication Date: 2015
• Language: English
• ISBN 10: 3656973768
• ISBN 13: 9783656973768
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 14D millimeters 210 millimeters width by 148 millimeters height by 14 millimeters depth
• Weight: 275 grams
• Seller catalogs:
  • Bücher

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Synopsis

Research Paper (postgraduate) from the year 2015 in the subject Medicine - Other, grade: II Level Master, University of Pavia (Unit of Medical and Genomic Statistics), course: Medical and Genomic Statistics, language: English, abstract: Electroencephalography, commonly called 'EEG', estimates through the application of electrodes, the electrical activity of the brain (which is the sum of the electrical activity of each neuron). In recent years, with the goal of making more reliable the EEG, many researchers have turned their interest in the development of tools, methods and software. This thesis describes some best procedures for the experimental design, data visualization and descriptive or inferential statistical analysis. The application of statistical models to single or multiple subjects study-design are also described, including parametric and non-parametric approaches. Methods for processing multivariate data (PCA, ICA, clustering) were described. Re-sampling methods (bootstrap) using many randomly software-generated samples were also described. The aim of this work is to provide, with statistical concepts and examples, information on the qualitative and quantitative approaches related to the electroencephalographic signals. The work consists into three parts: INTRODUTION TO ELECTROENCEPHALOGRAPHY (GENERAL CHARACTERISTICS); DATA MINING AND STATISTICAL ANALYSIS; EXPERIMENTAL STUDY DESIGNS. The six works included in the section called "EXPERIMENTAL STUDY DESIGNS" analyze EEG alterations in the protocols: Electrocortical activity in dancers and non-dancers listening to different music genre and during imaginative dance motor activity; Electrocortical activity during monosynaptic reflex in athletes; Monitoring of electrocortical activity for evaluation of seasickness; Electrocortical activity in different body positions; Electrocortical activity in athletes and non-athletes during body balance tasks; Electrocortical responses in volunteers with and without specif