Suppression of negative transfer in motor imagery brain-computer interface based on mutual information and Pearson correlation coefficient.

The focus of this paper is on the main challenges in brain-computer interface transfer learning: how to address data characteristic length and the source domain sample selection problems caused by individual differences. To overcome the negative migration that results from feature length, we propose a migration algorithm based on mutual information transfer (MIT), which selects effective features by calculating the entropy value of the probability distribution and conditional distribution, thereby reducing negative migration and improving learning efficiency. Source domain participants who differ too much from the target domain distribution can affect the overall classification performance. On the basis of MIT, we propose the Pearson correlation coefficient source domain automatic selection algorithm (PDAS algorithm). The PDAS algorithm can automatically select the appropriate source domain participants according to the target domain distribution, which reduces the negative migration of participant data among the source domain participants, improves experimental accuracy, and greatly reduces training time. The two proposed algorithms were tested offline and online on two public datasets, and the results were compared with those from existing advanced algorithms. The experimental results showed that the MIT algorithm and the MIT + PDAS algorithm had obvious advantages.

The formation mechanism of multiple vacancies and amorphous graphene under electron irradiation.

The evolution of multiple vacancies (Vns) in graphene under electron irradiation (EI) was explored systematically by long time non-equilibrium molecular dynamics simulations, with n varying from 4 to 40. The simulations showed that the Vns form haeckelites in the case with small n, while forming holes as n increases. The scale of the haeckelites, characterized by the number of pentagon-heptagon pairs, grows linearly with n. Such a linear relationship can be interpreted as a consequence of compensating the missing area, caused by the Vns, in order to maintain the area of the perfect sp(2) network by self-healing. Beyond that, the scale of the haeckelite vs. the density of missing atoms is predicted to be Sh ∼ 6Dn, where Sh and Dn are the percentage of non-hexagonal rings and missing atoms, respectively. This study provides an intuitive picture of the formation of amorphous graphene under EI and the quantitative understanding of the mechanism.

Atomistic simulation and the mechanism of graphene amorphization under electron irradiation.

Real-time reconstruction of a divacancy in graphene under electron irradiation (EI) is investigated by nonequilibrium molecular dynamic simulation (NEMD). The formation of the amorphous structure is found to be driven by the generalized Stone-Wales transformations (GSWTs), i.e. C-C bond rotations, around the defective area. The simulation reveals that each step of the reconstruction can be viewed as a quasi-thermal process and thus the reconstruction from a point defect to an amorphous structure favors the minimum energy path. On the other hand, the formation of a high energy large defective area is kinetically dominated by the balance between its expansion and shrinkage, and a kinetic model was proposed to understand the size of the defective area. The current study demonstrates that the route of the reconstruction from the point defective graphene toward an amorphous structure is predictive, though under stochastic EI.