By Cheng-Lin Liu, Amir Hussain, Bin Luo, Kay Chen Tan, Yi Zeng, Zhaoxiang Zhang
This booklet constitutes the refereed court cases of the eighth overseas convention on mind encouraged Cognitive platforms, BICS 2016, held in Beijing, China, in November 2016. The 32 complete papers provided have been conscientiously reviewed and chosen from forty three submissions. They talk about the rising components and demanding situations, current the state-of-the-art of brain-inspired cognitive structures learn and functions in assorted fields through protecting many issues in mind encouraged cognitive platforms similar learn together with biologically encouraged platforms, cognitive neuroscience, versions recognition, and neural computation.
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Extra info for Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings
A. Train and test HCNN on data acquired from one same subject (shown in Fig. 4). B. Train the HCNN on the data acquired from three other subjects, and then test HCNN on a new subject. C. Pre-train the HCNN on the data of three other subjects, and ﬁne-tune it by the data of a new subject, and then test the HCNN on the new subject. 4 Results Before showing the classiﬁcation performance, we plan to illustrate the complexity of the previous task. We introduce Adjusted Cosine Similarity (ACS) as criterion to measure the similarity of features for three emotion states.
Since the work of Hinton and Krizhevsky in 2012 , Deep Learning (DL) has dominated the machine learning research, and becomes the absolute winner in complex tasks such as image classiﬁcation  and machine translation . DL is capable of learning features automatically, because the DL structures trained under explicit goals (minimize the classiﬁcation error) in turn possess powerful representational ability. The most important structures include HCNN , SAE , and DBN . We could analyze EEG signal either in the time domain or in the frequency domain, or the combination of them.
2 25 Two-Dimensional Feature Organization In order to maintain the information of EEG placement as much as possible, we organize features (DE in this paper) extracted from 62 channels as two-dimensional maps at a time interval of one second. The conﬁguration of the DE map is illustrated in Fig. 1. In this paper, we organize the DE features in such conﬁguration to feed HCNN for training. However, the map size is two small and the ‘pixel values’ are too ‘concentrated’, so we introduce sparsity to generate sparse DE maps that are more suitable for HCNN dispose: all-zero rows and columns are added on alternate rows and columns.