Analysis of Electroencephalogram Parameters to Determine Human Drowsiness

This study examines the assessment of human drowsiness using single-channel data from a forehead electrode processed with a spectral analysis algorithm. Spectral band analysis allows for the identification of key parameters related to drowsiness. Eye blink frequency was identified as a useful parameter based on the analysis of signal amplitude and time-frequency characteristics. Data were obtained under two conditions - after 20 hours of wakefulness and after a full night of sleep while participants read an e-book. Spectral analysis calculations and Random Forest and statistical algorithms were used for signal processing to identify the most informative features for the expert decision-making system. The analysis showed a close correlation between spectral indicators (especially alpha and beta bands) and subjective ratings of the Karolinska Sleepiness Scale. Eye blink frequency was also successfully determined using biopotential and video analysis. Expert judgment complements the logical relationships of the parameters for real-time fatigue monitoring, with applications in safety-critical situations and human-computer interaction.