The results reveal certain aspects that may affect the success of speech imagery classification from EEG signals, such as sound, meaning and word complexity. This can potentially extend the capability of utilizing speech imagery in future BCI applications. The dataset of speech imagery collected from total 15 subjects is also published.

Abstract-Taking an image of an object is at its core a lossy process. The rich information about the three-dimensional structure of the world is flattened to an image plane and decisions such as viewpoint and camera parameters are final and not easily revertible. As a consequence, possibilities of changing viewpoint are limited. Given a single image depicting an object, novel-view synthesis is the task of generating new images that render the object from a different viewpoint than the one given. The main difficulty is to synthesize the parts that are disoccluded; disocclusion occurs when parts of an object are hidden by the object itself under a specific viewpoint. In this work, we show how to improve novel-view synthesis by making use of the correlations observed in 3D models and applying them to new image instances. We propose a technique to use the structural information extracted from a 3D model that matches the image object in terms of viewpoint and shape. For the latter part, we propose an efficient 2D-to-3D alignment method that associates precisely the image appearance with the 3D model geometry with minimal user interaction. Our technique is able to simulate plausible viewpoint changes for a variety of object classes within seconds. Additionally, we show that our synthesized images can be used as additional training data that improves the performance of standard object detectors.

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α-Glucosidase plays a role in hydrolyzing complex carbohydrates into glucose, which is easily absorbed, causing postprandial hyperglycemia. Inhibition of α-glucosidase is therefore an ideal approach to preventing this condition. A novel polyprenylated benzoylphloroglucinol, which we named schomburgkianone I (1), was isolated from the fruit of Garcinia schomburgkiana, along with an already-reported compound, guttiferone K (2). The structures of the two compounds were determined using NMR and HRESIMS analysis, and comparisons were made with previous studies. Compounds 1 and 2 exhibited potent α-glucosidase inhibition (IC50s of 21.2 and 34.8 µM, respectively), outperforming the acarbose positive control. Compound 1 produced wide zones of inhibition against Staphylococcus aureus and Enterococcus faecium (of 21 and 20 mm, respectively), compared with the 19 and 20 mm zones of compound 2, at a concentration of 50 µg/mL. The MIC value of compound 1 against S. aureus was 13.32 µM. An in silico molecular docking model suggested that both compounds are potent inhibitors of enzyme α-glucosidase and are therefore leading candidates as therapies for diabetes mellitus.

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