Thermal Influence on Cognitive Responses: An EEG and Machine Learning Analysis

This study uses electroencephalography (EEG) as the primary collection technique to carefully evaluate the relationship between changes in ambient temperature and brainwave patterns. The study uses an EEG device for neural signal conversion and quantification, and it rates alpha and beta waves, as well as different brainwave patterns, on a special scale from 0 to 100. The experiment makes use of EEG technology, computational tools for data analysis and storage, frictionless data sharing via Bluetooth, and rigorous thermal regulation with an air conditioner. P and t tests, as well as the creation of predictive models using machine learning algorithms, are all part of statistical analysis. The outstanding 98.08% accuracy of the decision tree classifier reveals the complicated interplay between temperature changes and cognitive brain responses.