However, we are able to show that the classification accuracy and ease of setup are promising paths toward mobile consumer-oriented BCI usage. Our case study with 12 participants did not fully support the assumption that the BCI improves usability. We present the first mobile BCI system that uses a smartphone and a low-cost EEG device with few electrodes to provide attention awareness to an AR application. The classification results are used to adapt the behavior of the translation app, which uses the smartphone’s camera to display translated text as augmented reality elements. The attentional state of the user is classified in terms of internally and externally directed attention by using the Muse 2 electroencephalography headband with four frontal electrodes. In this work, we implemented a system that integrates user attentional state awareness into a smartphone application for an AR written language translator. In previous research, augmented reality (AR) scenarios have been identified to profit from additional user state information-such as that provided by a BCI. The combination of portable smartphones and easy-to-use EEG dry electrode headbands offers intriguing new applications and methods of human–computer interaction. As lightweight, low-cost EEG headsets emerge, the feasibility of consumer-oriented brain–computer interfaces (BCI) increases.
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