Neural Network Classification of Brain Hemodynamic Responses from Four Mental Tasks

Berdakh Abibullaev, Jinung An, Jeon Il Moon

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


We investigate subjects' brain hemodynamic activities during mental tasks using a nearinfrared spectroscopy. A wavelet and neural network-based methodology is presented for recognition of brain hemodynamic responses. The recognition is performed by a single layer neural network classifier according to a backpropagation algorithm with two error minimizing techniques. The performance of the classifier varied depending on the neural network model, but the performance was usually at least 90%. The classifier usually converged faster and attained a somewhat greater level of performance when an input was presented with only relevant features. The overall classification rate was higher than 94%. The study demonstrates the accurate classifiablity of human brain hemodynamic useful in various brain studies.

Original languageEnglish
Pages (from-to)340-359
Number of pages20
JournalInternational Journal of Optomechatronics
Issue number4
Publication statusPublished - Oct 2011


  • brain-computer interface
  • functional near infrared spectroscopy
  • mental task classification
  • neural networks
  • wavelet transforms

ASJC Scopus subject areas

  • Instrumentation
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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