Backpropagation and EEG Data

Abstract

The development of neural networks has pursued a myriad of different courses reflecting the interests of a large number of researchers from highly varied backgrounds. This paper would like to focus on one point of this 'many faceted gem', as Stephen Grossberg described the field. The point of focus will be to address some of the practical results of applying a backpropagation trained net to raw electroencephalogram (EEG) data. Much important work on more efficient training rules has been done; however, equally critical is consideration of the information content of the data, the net size, number of hidden nodes and order of training data. This paper explores some of the training issues raised by applying backpropagation to this very complex data.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1988
Accession Number
ADA279073

Entities

People

  • Glenn F. Wilson
  • Paul E. Morton

Organizations

  • Armstrong Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Audio Tones
  • Biomedical Research
  • Classification
  • Cognitive Workload
  • Electroencephalography
  • Engineering
  • Government Procurement
  • Governments
  • Human Factors Engineering
  • Neural Networks
  • Standards
  • Technical Information Centers
  • Test Sets
  • Training
  • Workload

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks