This paper proposes a novel fixed low-rank spatial filter estimation for brain computer interface (BCI) systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a “bottom-up” manner, under a regularized loss minimization problem. The loss function is explicitly derived from the conventional BCI approach and solves its minimization by optimization with a nonconvex fixed low-rank constraint. For evaluation, an experiment was conducted to induce emotions by movies for dozens of young adult subjects and estimated the emotional states using the proposed method. The advantage of the proposed method is that it combines feature selection, feature extraction, and classification into a monolithic optimization problem with a fixed low-rank regularization, which implicitly estimates optimal spatial filters. The proposed method shows competitive performance against the best CSP-based alternatives.

Sufficient data is required for research on advanced AI. In the field of medicine, especially clinical medicine, information retrieval is necessary to utilize the data fully since the data-mainly clinical records-uses natural language. The corpus we developed in this study has the following strong points: (i) The corpus consists of 45,000 case reports, which is the largest to our knowledge, and (ii) not only did we standardized the terminology and the method for annotation, we also annotated "factness," which notes whether or not a disease name is actually the state of the patient in a case

We introduce a novel parameterization of facial expressions by using elastic surface model. The elastic surface model has been used as a deformation tool especially for nonrigid organic objects. The parameter of expressions is either retrieved from existing articulated face models or obtained indirectly by manipulating facial muscles. The obtained parameter can be applied on target face models dissimilar to the source model to create novel expressions. Due to the limited number of control points, the animation data created using the parameterization require less storage size without affecting the range of deformation it provides. The proposed method can be utilized in many ways: (1) creating a novel facial expression from scratch, (2) parameterizing existing articulation data, (3) parameterizing indirectly by muscle construction, and (4) providing a new animation data format which requires less storage.

We propose a novel method to reconstruct B-spline surfaces from generalized cylindrical meshes by skinning. Skinning is a well known surface creation technique and has been used in CAD and CG modeling. However there are few papers which address the issue of automated creation and preparation of sectional curves for skinning. Although our method is only applicable to generalized cylindrical meshes, there are many real world objects which can be created or reconstructed by skinning. The proposed surface reconstruction method is fully automated with minimal user interventions. We have evaluated the validity of this method by reconstructing B-spline surfaces from various polygonal meshes varying in shapes and geometries. The final results show the effectiveness of our proposed method.

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