Introduction to machine learning for brain imaging

Steven Lemm, Benjamin Blankertz, Thorsten Dickhaus, Klaus Robert Müller

Research output: Contribution to journalArticle

381 Citations (Scopus)

Abstract

Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences.

Original languageEnglish
Pages (from-to)387-399
Number of pages13
JournalNeuroImage
Volume56
Issue number2
DOIs
Publication statusPublished - 2011 May 15
Externally publishedYes

Keywords

  • Cross validation
  • Machine learning
  • Model selection
  • Pattern recognition

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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