본문 바로가기
  • Home

Input Pattern Vector Extraction and Pattern Recognition of EEG

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2006, 11(5), pp.95-104
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Lee Yong Gu 1 CHOI , WOO SEUNG 2 이선엽 3

1한림성심대학교
2경원대학교
3한림대학교

Candidate

ABSTRACT

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize EEG pattern vectors. The frequency and amplitude of alpha rhythms and beta rhythms are used to compose the input pattern vectors. And the algorithm for EEG pattern recognition is used SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of the subclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights between subclass layer and output layer is learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors of EEG, the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

Citation status

* References for papers published after 2023 are currently being built.