Brain-computer interfaces (BCIs) have been attracting attention as a research topic. BCI has various applications, such as at home and in the medical sector. BCI is an interconnection between the human brain and a computer, which is a communication pathway between external peripheral devices. Brainwave sensors play a significant role when applying BCIs in practice. In this study, data from such sensors are analyzed to classify the mental states of users. This study used two different brainwave sensors: Neurosky MindWave Mobile and Emotiv EPOC+. Several types of machine-learning techniques (support vector machine, random forest, and long short-term memory) have been applied to classify brainwave data. This study aimed to compare the accuracy of the two sensors, analyze data, and identify the most accurate machine-learning method. Finally, a BCI toy with MaBeee, which is a battery-type internet-of-things device, was designed as a BCI application that reflected the analysis results.