Action real-time strategy gaming (ARSG) is a cognitively demanding task which requires attention, sensorimotor skills, team cooperation, and strategy-making abilities. A recent study found that ARSG experts had superior visual selective attention (VSA) for detecting the location of a moving object that could appear in one of 24 different peripheral locations (Qiu et al., 2018), suggesting that ARSG experience is related to improvements in the spatial component of VSA. However, the influence of ARSG experience on the temporal component of VSA-the detection of an item among a sequence of items presented consecutively and quickly at a single location-still remains understudied. Using behavioral and electrophysiological measures, this study examined whether ARSG experts had superior temporal VSA performance compared to non-experts in an attentional blink (AB) task, which is typically used to examine temporal VSA. The results showed that the experts outperformed the non-experts in their detection rates of targets. Furthermore, compared to the non-experts, the experts had faster information processing as indicated by earlier P3 peak latencies in an AB period, more attentional resources distributed to targets as indicated by stronger P3 amplitudes, and a more flexible deployment of attentional resources. These findings suggest that experts were less prone to the AB effect. Thus, long-term ARSG experience is related to improvements in temporal VSA. The current findings support the benefit of video gaming experience on the development of VSA.

Objective. Motor imagery (MI) classification is an important task in the brain–computer interface (BCI) field. MI data exhibit highly dynamic characteristics and are difficult to obtain. Therefore, the performance of the classification model will be challenged. Recently, convolutional neural networks (CNNs) have been employed for MI classification and have demonstrated favorable performances. However, the traditional CNN model uses an end-to-end output method, and part of the feature information is discarded during the transmission process. Approach. Herein, we propose a novel algorithm, that is, a combined long short-term memory generative adversarial networks (LGANs) and multi-output convolutional neural network (MoCNN) for MI classification, and an attention network for improving model performance. Specifically, the proposed method comprises three steps. First, MI data are obtained, and preprocessing is performed. Second, additional data are generated for training. Here, a data augmentation method based on a LGAN is designed. Last, the MoCNN is proposed to improve the classification performance. Main results. The BCI competition IV datasets 2a and 2b are employed to evaluate the performance of the proposed method. With multiple evaluation indicators, the proposed generative model can generate more realistic data. The expanded training set improves the performance of the classification model. Significance. The results show that the proposed method improves the classification of MI data, which facilitates motion imagination.

No abstract

No abstract

In this paper necessary conditions and sufficient conditions are given for a linear operator to be a positive operator of an Extended Lorentz cone. Similarities and differences with the positive operators of Lorentz cones are investigated. * 2010