Cognitive task prediction using parametric spectral analysis of EEG signals

R. Palaniappan, P. Raveendran

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


In this paper, we are proposing a method to predict cognitive tasks performed by the human brain using spectral analysis of electrical signals extracted from the scalp of the brain. These electrical signals, which are generated by the synapses and neurons in the brain, are also known as Electroencephalogram (EEG) signals. The EEG signals are analysed using autoregressive spectral analysis, a type of modern parametric spectral analysis method, which comparatively yield better power spectrum over the classical Fourier methods. Power spectral densities of the EEG signals are used to train a Fuzzy ARTMAP network to predict the respective cognitive tasks. In our experimental study, we have analysed 3 subjects performing 2 different cognitive tasks and our average results of 72.22% to 93.05% for each subject show that it is highly possible to predict cognitive tasks based on EEG signals. This can be used as a mode of communication or wheelchair control for paralysed patients and also in EEG biofeedback systems.

Original languageEnglish
Pages (from-to)58-67
Number of pages10
JournalMalaysian Journal of Computer Science
Issue number1
Publication statusPublished - 2001
Externally publishedYes


  • Autoregressive spectral analysis
  • Burg's algorithm
  • Cognitive task
  • EEG
  • Fuzzy ARTMAP

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