Download e-book for iPad: Advances in Brain Inspired Cognitive Systems: 8th by Cheng-Lin Liu, Amir Hussain, Bin Luo, Kay Chen Tan, Yi Zeng,

By Cheng-Lin Liu, Amir Hussain, Bin Luo, Kay Chen Tan, Yi Zeng, Zhaoxiang Zhang

ISBN-10: 3319496840

ISBN-13: 9783319496849

ISBN-10: 3319496859

ISBN-13: 9783319496856

This ebook constitutes the refereed court cases of the eighth foreign convention on mind encouraged Cognitive structures, BICS 2016, held in Beijing, China, in November 2016. The 32 complete papers awarded have been conscientiously reviewed and chosen from forty three submissions. They speak about the rising parts and demanding situations, current the state-of-the-art of brain-inspired cognitive platforms study and purposes in varied fields via protecting many issues in mind encouraged cognitive platforms comparable study together with biologically encouraged structures, cognitive neuroscience, types awareness, and neural computation.

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Additional info for Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings

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Therefore, Delta, Theta and Alpha might be favorable in resting-state emotion reading. Beta and Gamma waves are tightly-related to rational activities, and thus good for task-evoked emotion reading. Fourier power might be a choice to characterize the signals, but EEG signal is not a stationary process. Hadjidimitriou et al. [13] employed three kinds of time-frequency distributions (spectrogram, Hilbert-Huang spectrum and Zhao-Atlas-Marks transform) as features to classify ratings of liking and familiarity.

13] employed three kinds of time-frequency distributions (spectrogram, Hilbert-Huang spectrum and Zhao-Atlas-Marks transform) as features to classify ratings of liking and familiarity. Yang Li et al. [14] applied wavelet energy to compensate for the influence of non-stationary character. Lu et al. [5] 24 J. Li et al. used DE as features to classify three emotion states: positive, negative and neutral. They organized features in one-dimensionality and used DBN (Deep Belief Networks) to realize the classifier, and then they investigated the critical frequency bands and channels.

LNCS, vol. 6670, pp. 256–277Springer, Heidelberg (2011). 1007/978-3-642-22336-5_13 4. : Differential lateralization for positive and negative emotion in the human brain: EEG spectral analysis. Neuropsychologia 23(6), 745–755 (1985) 5. : Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans. Auton. Mental Dev. 7(3), 162– 175 (2015) 6. : Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997) 7. : Imagenet classification with deep convolutional neural networks.

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Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings by Cheng-Lin Liu, Amir Hussain, Bin Luo, Kay Chen Tan, Yi Zeng, Zhaoxiang Zhang


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