Recognition of discriminative neural signatures and regions corresponding to emotions are important in understanding the neuron functional network underlying the human emotion process. Electroencephalogram (EEG) is a spatial discrete signal. In this paper, in order to extract the spatio-temporal characteristics and the inherent information implied by functional connections, a multichannel EEG emotion recognition method based on phase-locking value (PLV) graph convolutional neural networks (P-GCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is using PLV-based brain network to model multi-channel EEG features as graph signals and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed P-GCNN method uses the PLV connectivity of EEG signals to determine the mode of emotional-related functional connectivity, which is used to represent the intrinsic relationship between EEG channels in different emotional states. On this basis, the neural network is trained to extract effective EEG emotional features. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DEAP dataset. The experimental results demonstrate that novel framework can improve the classification accuracy on both datasets, but not so effective on DEAP as on SEED, in which with 84.35% classification accuracy for SEED, and the average accuracies of 73.31%, 77.03% and 79.20% are, respectively, obtained for valence, arousal, and dominance classifications on the DEAP database.INDEX TERMS EEG emotion recognition, phase-locking value, graph convolutional neural networks, brain network, functional connectivity.

Hydrogenation of phenol is an important strategy to produce cyclohexane or cyclohexanol as both of them are raw materials for the synthesis of nylon-6 and nylon-66.

Summary Mobile phones are equipped with a rich set of sensors, such as accelerometers, magnetometers, gyroscopes, photometers, orientation sensors, and gravity sensors. These sensors can be used for human activity recognition in the ubiquitous computing domain. Most of reported studies consider acceleration signals that are collected from a known fixed device location and orientation. This paper describes how more accurate results of basic activity recognition can be achieved with transformed accelerometer data. Based on the rotation matrix (Euler Angle Conversion) derived from the orientation angles of gyroscopes and orientation sensors, we transform input signals into a reference coordinate system. The advantage of the transformation is that it allows activity classification and recognition to be carried out independent of the orientation of sensors. We consider five user activities: staying, walking, running, ascending stairs, and descending stairs, with a phone being placed in the subject's hand, or in pants pocket, or in a handbag. The results show that an overall orientation independent accuracy of 84.77% is achieved, which is a improvement of 17.26% over those classifications without input transformation. Copyright © 2014 John Wiley & Sons, Ltd.

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