Normal and Hypoxia EEG Recognition Based on a Chaotic Olfactory Model

  • Meng Hu
  • Jiaojie Li
  • Guang Li
  • Xiaowei Tang
  • Walter J. Freeman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


The KIII model of the chaotic dynamics of the olfactory system was designed to simulate pattern classification required for odor perception. It was evaluated by simulating the patterns of action potentials and EEG waveforms observed in electrophysiological experiments. It differs from conventional artificial neural networks in relying on a landscape of chaotic attractors for its memory system and on a high-dimensional trajectory in state space for virtually instantaneous access to any low-dimensional attractor. Here we adapted this novel neural network as a diagnostic tool to classify normal and hypoxic EEGs.


Feature Vector Chaotic Attractor Wavelet Packet Olfactory System Wavelet Packet Decomposition 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Meng Hu
    • 1
  • Jiaojie Li
    • 2
  • Guang Li
    • 3
  • Xiaowei Tang
    • 1
  • Walter J. Freeman
    • 4
  1. 1.Department of PhysicsZhejiang UniversityHangzhouChina
  2. 2.Hangzhou Sanitarium of PLA AirforceHangzhouChina
  3. 3.National Laboratory of Industrial Control Technology, Institute of Advanced Process ControlZhejiang UniversityHangzhouChina
  4. 4.Division of NeurobiologyUniversity of California at Berkeley, LSA 142BerkeleyUSA

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