Multi-source deep domain adaptation ensemble framework for cross-dataset motor imagery EEG transfer learning.

Objective. Electroencephalography (EEG) is an important kind of bioelectric signal for measuring physiological activities of the brain, and motor imagery (MI) EEG has significant clinical application prospects. Convolutional neural network has become a mainstream algorithm for MI EEG classification, however lack of subject-specific data considerably restricts its decoding accuracy and generalization performance. To address this challenge, a novel transfer learning (TL) framework using auxiliary dataset to improve the MI EEG classification performance of target subject is proposed in this paper.Approach. We developed a multi-source deep domain adaptation ensemble framework (MSDDAEF) for cross-dataset MI EEG decoding. The proposed MSDDAEF comprises three main components: model pre-training, deep domain adaptation, and multi-source ensemble. Moreover, for each component, different designs were examined to verify the robustness of MSDDAEF.Main results. Bidirectional validation experiments were performed on two large public MI EEG datasets (openBMI and GIST). The highest average classification accuracy of MSDDAEF reaches 74.28% when openBMI serves as target dataset and GIST serves as source dataset. While the highest average classification accuracy of MSDDAEF is 69.85% when GIST serves as target dataset and openBMI serves as source dataset. In addition, the classification performance of MSDDAEF surpasses several well-established studies and state-of-the-art algorithms.Significance. The results of this study show that cross-dataset TL is feasible for left/right-hand MI EEG decoding, and further indicate that MSDDAEF is a promising solution for addressing MI EEG cross-dataset variability.

[INDENA SPA company's patent portfolio of Ginkgo biloba preparation].

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[Global patent overview of Ginkgo biloba preparation].

With related global patent data as analysis samples, worldwide patent overview of Ginkgo biloba preparation is analyzed in application, applicant, technical distribution and so on. This research shows that the most important areas of G. biloba preparation are Europe and China. The European applicants start earliest along with developing smoothly, moreover, their patents have best quality. The Chinese applicants start late along with the fastest growing, and have already certain research capabilities, moreover, their patents' quality needs to be improved. This research result provides reference for development of G. biloba preparation. The author suggest that Chinese applicants learn techniques and layout experiences of other's patents fully to enhance the level of new drug development and patent protection.