Studies on silent speech brain-computer interface (BCI) systems are underway to facilitate communication with care recipients who have lost their language faculty or suffer from aphasia. This technology hypothesizes what a care recipient is attempting to communicate through electroencephalographic analysis when attempting to speak ‘‘without actually speaking This study shows a method of performing electroencephalographic analysis when a person is imagining a vowel sound to realize a silent speech BCI nursing care support system. To construct a system to reduce the burden on users, constructing a model that can achieve a high accuracy of brain activity with less electrode channels and a compact classifier size that can be implemented in edge devices is essential. Experiments were performed by attaching eight electrodes to the left temporal lobes of the subjects, measuring brain waves when each subject was imagining 5 sounds and a mute, i.e., a/i/u/e/0 and mute and performing analysis using four methods: support vector machine, decision tree, linear discriminant analysis, which is a commonly used machine-learning algorithm, and long short-term memory (LSTM) and EEGNet which are extensively investigated deep learning models. The performance of the system was evaluated with four healthy male subjects (ages 23-24). Consequently, the average classification accuracies of 68.8% and 80.9% for four subjects obtained using LSTM and EEGNet.