Cognitive EEG Differentiation with Hypnosis-Based Noise Reduction and K-Harmonic Means for Personalized Brainwave Modeling

This study investigates the integration of hypnosis-based noise reduction and K-Harmonic Means (KHM) clustering for personalized brainwave modeling using Electroencephalography (EEG) data. EEG signals were collected from 100 participants using a Neurosky Mindset sensor at the FP1 (prefrontal) location, with each subject performing nine standardized cognitive tasks such as breathing, memory recall, and mathematical problem-solving. Hypnosis was applied not as a filtering method but as a behavioral protocol to standardize subject conditions and minimize physiological and environmental noise. The EEG signals were sampled at 128 Hz and analyzed using KHM clustering with K=4K = 4K=4, resulting in a Silhouette Score of 0.9515, which demonstrates strong cluster separation and robustness against noise. Compared with baseline approaches such as K-Means and Fuzzy C-Means, KHM achieved higher stability and consistency in differentiating cognitive tasks. This performance highlights the advantage of harmonic averaging in mitigating the influence of outliers during clustering. The findings suggest that hypnosis can meaningfully enhance EEG signal quality, thereby improving downstream cognitive s