Frequency based EEG signal analysis for mental fatigue detection using welch's method of power spectral density

Mental fatigue is a cognitive condition that results in a decline in attention, performance, and alertness due to prolonged mental activity. It is particularly critical in safety-sensitive environments, such as aviation, transportation, and healthcare. Early mental fatigue detection is essential to prevent accidents and improve operational efficiency. Electroencephalogram (EEG) is a non-invasive method for monitoring brain activity and detecting cognitive states, including mental fatigue. EEG signals exhibit frequency-specific changes when an individual experiences fatigue, especially in the alpha, beta, and theta bands. This paper proposes a frequency-based EEG signal analysis for mental fatigue detection using the Power Spectral Density (PSD) method. The PSD, computed using Welch's algorithm, estimates the power distribution across frequency bands, highlighting fatigue-related changes in brain activity. Welch's method enhances accuracy by averaging periodograms over overlapping EEG segments, enabling effective and real-time mental fatigue detection.