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    ABSTRACT: KIII model is an olfactory neural networks bionic model proposed by Walter J. Freeman. Its architecture simulates that of olfactory neural system, which is different from other artificial neural networks. Through simplifying KIII model, a color texture generating algorithm is proposed combining with RGB. In RGB space, the tricolor of each pixel (red, green and blue) is used as the model input and the model output is composed as the tricolor of corresponding pixel in generated texture image. Experimental results show that simplified KIII model can generate beautiful color texture images.
    Computer and Information Science, 2009. ICIS 2009. Eighth IEEE/ACIS International Conference on; 07/2009
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    ABSTRACT: This study addresses Brain-Computer Interface (BCI) systems meant to permit communication for those who are severely locked-in. The current study attempts to evaluate and compare the efficiency of different translating algorithms. The setup used in this study detects the elicited P300 evoked potential in response to six different stimuli. Performance is evaluated in terms of error rates, bit-rates and runtimes for four different translating algorithms; Bayesian Linear Disciminant Analysis (BLDA), Linear Discriminant Analysis (LDA), Perceptron Batch (PB), and nonlinear Support Vector Machines (SVMs) were used to train the classifier whilst an N-fold cross validation procedure was used to test each algorithm. A communication channel based on Electroencephalography (EEG) is made possible using various machine learning algorithms and advanced pattern recognition techniques. All algorithms converged to 100% accuracy for seven of the eight subjects. While all methods obtained fairly good results, BLDA and PB were superior in terms of runtimes, where the average runtimes for BLDA and PB were 13 ± 2 and 15.6 ± 6 seconds, respectively. In terms of bit-rates, BLDA obtained the highest average value (22 ± 12 bits/minute), where the average bit-rate for all subjects, all sessions, and all algorithms was 18.76 ± 10 bits/minute.
    J Health Med Informat. 01/2013; 4(2).
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    ABSTRACT: Any brain–computer interface (BCI) system must translate signals from the users brain into messages or commands (see Fig. 1). Many signal processing and machine learning techniques have been developed for this signal translation, and this chapter reviews the most common ones. Although these techniques are often illustrated using electroencephalography (EEG) signals in this chapter, they are also suitable for other brain signals.
    Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction, Edited by Bernhard Graimann, Brenda Allison, Gert Pfurtscheller, 10/2011: pages 305-330; Springer.

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