Single-channel wearable EEG using low-power Qvar sensor and machine learning for drowsiness detection

This work deals with the fabrication and validation of an innovative wearable single-channel EEG system, designed for real-time monitoring of specific brain activity. It is based on the use of a low-power sensor (Qvar) integrated in a miniaturized electronic platform, and on Machine Learning algorithms developed on purpose. The study demonstrates the accuracy of Qvar in capturing EEG signals by systematic comparison with a gold standard and the comprehensive analyses in time and frequency domains confirm its reliability across the various EEG frequency bands. In this work, the specific application of drowsiness detection is addressed, leveraging Machine Learning algorithms trained and validated on public datasets and, at a more preliminary stage, on real-world data collected specifically for this study under the supervision of trained personnel. The results outline the system promise for domestic and outdoor monitoring of specific neurological conditions and applications, such as fatigue management and cognitive state assessment. The Qvar represents a significant step toward accessible and practical wearable EEG technologies, combining portability, accuracy and low power consumption to enhance user experience, enable massive screening, and broaden the scope of EEG applications.