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In the rapidly evolving digital age, human-machine interface technologies are continuously being improved. Traditional methods of computer interaction, such as a mouse and a keyboard, are being supplemented and even replaced by more intuitive methods, including eye-tracking technologies. Conventional eye-tracking methods utilize cameras to monitor the direction of gaze but have their limitations. An alternative and promising approach for eye-tracking involves the use of electroencephalography, a technique for measuring brain activity. Historically, EEG was primarily limited to laboratory conditions. However, mobile and accessible EEG devices are entering the market, offering a more versatile and effective means of recording bioelectric potentials. This paper introduces a gaze localization method using EEG obtained from a mobile EEG recorder in the form of a wearable headband (provided by BrainBit). The study aims to decode neural patterns associated with different gaze directions using advanced machine learning methods, particularly neural networks. Pattern recognition is performed using both ground truth data collected from wearable camera-based eye-tracking glasses and unlabeled data. The results obtained in this research demonstrate a relationship between eye movement and EEG, which can be described and recognized through a predictive model. This integration of mobile EEG technology with eye-tracking methods offers a portable and convenient solution that can be applied in various fields, including medical research and the development of more intuitive computer interfaces.
Romaniuk et al. (Thu,) studied this question.