FAST: Implicit function as squashing time for eeg analysis-theory

Massimo Buscema, Paolo Maria Rossini, Enzo Grossi, Claudio Babiloni

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter presents the innovative use of special types of Artificial Neural Networks assembled in a novel methodology, capable of compressing the temporal sequence of EEG data into spatial invariants. The spatial content of the EEG voltage recorded from 19 channels along 60 seconds is extracted by a stepwise procedure using ANNs (SEMEION

Original languageEnglish
Title of host publicationArtificial Adaptive Systems in Medicine: New Theories and Models for New Applications in the Real World
PublisherBentham Science Publishers Ltd.
Pages90-103
Number of pages14
ISBN (Print)9781608053926
DOIs
Publication statusPublished - 2009

Keywords

  • Alzheimer's disease (AD)
  • Artificial neural networks (ANNs)
  • Electroencephalography (EEG)
  • IFAST
  • Mild cognitive impairment (MCI)
  • Spatial invariants

ASJC Scopus subject areas

  • Medicine(all)

Fingerprint Dive into the research topics of 'FAST: Implicit function as squashing time for eeg analysis-theory'. Together they form a unique fingerprint.

  • Cite this

    Buscema, M., Rossini, P. M., Grossi, E., & Babiloni, C. (2009). FAST: Implicit function as squashing time for eeg analysis-theory. In Artificial Adaptive Systems in Medicine: New Theories and Models for New Applications in the Real World (pp. 90-103). Bentham Science Publishers Ltd.. https://doi.org/10.2174/978160805042010901010090