EEG-Based Stress Detection Using Random Forest Algorithm

This paper aims to investigate the efficacy of EEG-based stress detection using a Random Forest classifier during the Stroop Test, a key psychological assessment probing cognitive functions such as processing speed, cognitive flexibility, and attentional capacity. The study involved 10 participants (5 male,5 female), aged 18 to 30, all with normal or corrected-to-normal vision and no documented cognitive impairments. EEG data was recorded using PsychoPy software and standardized Stroop Test stimuli. Signal processing, including notch and bandpass filtering, was applied to eliminate noise and emphasize relevant brainwave frequencies. Artifact rejection, notably Independent Component Analysis (ICA), was utilized to remove physiological artifacts, and epoching was used to segment the EEG data for detailed analysis. A Random Forest classifier, trained on the preprocessed EEG data, achieved an accuracy of 83.33% in distinguishing between different cognitive states. These findings highlight the efficacy of combining signal processing and machine learning techniques for cognitive state detection through EEG analysis.