Machine Learning from Mistakes: Self-Improving Attention Classifier Using Error-Related Potentials

The detection of an individual's attentional state via a Brain-Computer Interface (BCI) holds significant promise, offering a multitude of possibilities, including enhancing the usability of applications and enabling timely alerts in hazardous situations. However, the variability of EEG data between individuals and the dynamic nature of recordings pose practical challenges for achieving reliable results with BCIs. Thus, conventional methods often require the collection of each person's training data prior to usage, which is then used to train an individual model for detection. Such training data collection, makes it difficult to achieve practical use. To overcome this challenge, we propose a self-improving online learning system that personalizes a person-independent model for detecting attentional state in real-time during runtime. This eliminates the need for collecting individual training data prior to usage and instead generates the necessary labels for adaptation using automatically detected error-related potentials. The system was developed based on pre-trained models of two classifiers and used to evaluate different strategies of adaptation and label generation. A statistically significant accuracy improvement of 0.088 was achieved across all available subjects, based on simulations with pre-recorded data. These results suggest that person-dependent models for attentional state detection could in the future be substituted by self-improving classifiers that do not require a dedicated training data collection.