Neural Information Processing. Models and Applications: 17th by Sercan Taha Ahi, Natsue Yoshimura, Hiroyuki Kambara,

By Sercan Taha Ahi, Natsue Yoshimura, Hiroyuki Kambara, Yasuharu Koike (auth.), Kok Wai Wong, B. Sumudu U. Mendis, Abdesselam Bouzerdoum (eds.)

The quantity set LNCS 6443 and LNCS 6444 constitutes the lawsuits of the seventeenth foreign convention on Neural details Processing, ICONIP 2010, held in Sydney, Australia, in November 2010. The 146 commonplace consultation papers offered have been conscientiously reviewed and chosen from 470 submissions. The papers of half I are geared up in topical sections on neurodynamics, computational neuroscience and cognitive technology, facts and textual content processing, adaptive algorithms, bio-inspired algorithms, and hierarchical tools. the second one quantity is based in topical sections on mind laptop interface, kernel tools, computational improve in bioinformatics, self-organizing maps and their functions, laptop studying functions to photograph research, and applications.

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Additional resources for Neural Information Processing. Models and Applications: 17th International Conference, ICONIP 2010, Sydney, Australia, November 22-25, 2010, Proceedings, Part II

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9–16, 2010. c Springer-Verlag Berlin Heidelberg 2010 10 W. Tu and S. Sun calibration and feedback sessions will also strongly degrades the performance of EER features. Thus adaptive learning is necessary to boost up the performance of existing classifiers and feature extractors. Due to the time-varying characteristic of EEG signals during different sessions, it is reasonable to utilize samples in the test session to increase the classification ability on test samples. One way to realize this goal is to combine the brain signals recorded recently in the test session and samples which were labeled in the training session to enhance the classification ability.

Reducing the Calibration Time of P300-Based BCI 7 Table 1. 55 4 8 12 16 20 Number of CalibraƟon LeƩers Spelled by the Test Subject Fig. 3. Average accuracy values obtained with four different training approaches. Note that Database only corresponds to zero calibration, and therefore the system performance does not depend on the amount of the subject-specific calibration data. Table 2. Number of feature vectors in the corresponding training set and the average training time (in seconds) of the machine learning algorithm.

The EEG signals were recorded from five subjects. 118 EEG channels are measured at positions of the extended international 10/20-system (Nch = 118). Signals were band-pass Tensor Based Simultaneous Feature Extraction and Sample Weighting 31 Algorithm 1. Tensor based feature extraction/sample weighting Input: X Parameter: ra , lb and ub (a ∈ A, b ∈ B) Output: Pa , Db (a ∈ A, b ∈ B) Initialization: Pa (∀a ∈ A): identity matrix or simple PCA, Db (∀b ∈ B): identity matrix, k = 0: no. of iteration. calculate initial cost L0 from the cost function f2 .

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