Recent studies have shown that integrating individualized templates into a template-matching target identification method could significantly improve the performance of a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). However, collecting the template (or calibration) data for each individual can be time-consuming and laborious. This issue can be alleviated by employing phase-coded visual stimuli because phase information could be discriminated by using templates synthesized from the template induced by a visual stimulus. Minimizing phase intervals between two adjacent visual stimuli could increase the number of stimuli without increasing the calibration cost. Nonetheless, no study has investigated the effects of the phase interval on the classification performance. This study compared the classification accuracy of SSVEPs with five different phase intervals (0.1 π, 0.2 π, 0.3 π, 0.4 π, and 0.5 π) using synthesized individual templates with task-related component analysis (TRCA)-based spatial filtering. From a public 12-class SSVEP dataset, phase-adjusted SSVEP data were created by adding time shifts according to the five phase intervals. The classification results showed that the accuracy was sufficiently high when the phase intervals were over 0.3 π, suggesting the use of up to six phase-shifted visual stimuli at a given frequency.
We report a case of carcinosarcoma of the gallbladder. A 53-year-old female visited the hospital because of upper abdominal distension and right back pain. A 13.5×9.5cm cystic tumor expanding downward from the liver bed was pointed out by ultrasonography, computed tomography and magnetic resonance imaging. At laparotomy, the direct invasion into the liver and anterior wall of duodenum was found, but there was no invasion into the hepatoduodenal ligament. With the partial resection of the liver and local resection of the duodenum, the tumor was excised. The tumor was histologically carcinosarcoma composed of intermingled poorly differentiated adenocarcinoma and fibrosarcoma. Carcinosarcoma is very rare and highly malignant. Only 24 cases have been reported in Japan. The prognosis of this disease is so poor that the average survival after the operation is 6 months. Carcinosarcoma and so-called carcinosarcoma are art to be confused. Not only microscopic findings but immunohistochemical and electron microscopic findings are useful to distinguish between carcinosarcoma and so-called carcinosarcoma. The general concept of carcinosarcoma, however, is still obscure. In this paper, the clinical and clinicopathological features of carcinosarcoma of the gallbladder are presented, with a review of 24 cases seen in the Japanese literature.
In the above paper [1] , a method has been proposed to use the correlated component analysis (CORCA) to learn spatial filters with multiple blocks of individual training data for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) scenario. In order to evaluate the performance of CORCA, the task-related component analysis (TRCA)-based method was used as a baseline method [2] . For a fair and convincing comparison, the MATLAB codes on the website ( https://github.com/mnakanishi/TRCA-SSVEP ) for implementing TRCA method provided by Dr. Masaki Nakanishi, the first author of [2] , were used to take the role of the TRCA method. At that time, the proposed CORCA-based method outperforms the TRCA-based method [1] .
