Title :
A study on hybrid model of HMMs and GMMs for mirror neuron system modeling using EEG signals
Author :
Park, Seung-Min ; Park, Junheong ; Ko, Kwang-Eun ; Sim, Kwee-Bo
Author_Institution :
Sch. of Electr. Electron. Eng., Chung-Ang Univ., Seoul, South Korea
Abstract :
For our present life anytime, anywhere access to the network can communicate with the ubiquitous computing, it is essential to human life. We should be able to agree that communication will be enabled. For our present life, anytime, anywhere access to the network can communicate with the ubiquitous computing. Such as the ubiquitous era approached, interaction between the user and the computer has become an important issue. In this paper we use EEG signals to extract the user´s intention recognition data, which the Mirror Neuron System Based on HMMs and GMMs to model the convergence of the hybrid model is proposed. This is based on a kind of biological signals using EEG signals to the user´s intention recognition techniques have been studied. In addition, EEG signals is generated based on the model, using the user intention recognition method have been studied. The proposed model will be applied in the field of neuro robotics.
Keywords :
Gaussian processes; electroencephalography; hidden Markov models; human-robot interaction; medical signal processing; ubiquitous computing; EEG signal; Gaussian mixture model; biological signal; hidden Markov model; human robot interaction; mirror neuron system modeling; network access; neuro robotics; ubiquitous computing; user intention recognition data; Biological system modeling; Brain modeling; Electroencephalography; Hidden Markov models; Humans; Mirrors; Neurons; EEG; GMMs(Gaussian Mixture Models); HMMs(Hidden Markov Models); Intention recognition; Mirror Neuron System;
Conference_Titel :
Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7315-1
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2011.6007503