Selection of Relevant Features for Classification of Movements From Single Movement-Related Potentials Using a Genetic Algorithm

Abstract

Classification of movement-related potentials recorded from the scalp to their corresponding limb is a crucial task in brain-computer interfaces based on such potentials. This paper demonstrates how the features for such a task can be selected from a large bank of features using a genetic algorithm. We show that it is possible to differentiate between the movements of contralateral fingers with a classification accuracy of 77% using a small number of features (10-20) selected from a bank containing roughly 1000 features.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA412420

Entities

People

  • E. Yom-tov
  • G. F. Inbar

Organizations

  • Technion – Israel Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Brain-Computer Interfaces
  • Classification
  • Computational Complexity
  • Data Acquisition
  • Detectors
  • Electrical Engineering
  • Electrodes
  • Engineering
  • Feature Extraction
  • Feature Selection
  • Genetic Algorithms
  • Machine Learning
  • Standards
  • Supervised Machine Learning
  • Time Intervals

Fields of Study

  • Computer science

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Neural Network Machine Learning.
  • Trauma Surgery or Emergency Medicine.

Technology Areas

  • AI & ML
  • Biotechnology