Abstract
Laughter is known to improve mental well-being. Laughter is generally categorized into self-induced and externally induced laughter, and there is a lack of empirical evidence differentiating the two. There are limited studies on the use of brain signals to differentiate frequency patterns related to these two types of laughter, and to explore their role in mental health and well-being. This study aims to address this gap using brain frequency wave responses. Brain frequency data were collected from fifty participants using a Muse headband. MNE-Python, Independent Component Analysis, and time-frequency were used for exploratory analysis. Machine learning and deep learning techniques (Random Forest, Gradient Boosting, LSTM, and Logistic Regression) were used to classify EEG trends. Random Forest revealed greater accuracy of 74%. Brainwave trends differed notably between the two types of laughter. Brain signals during Self-induced displayed prominent beta and gamma responses, while externally induced showed significant alpha and theta values. Thus, the self-induced laughter has a stronger impact on brainwaves connected to cognitive engagement and mental health compared to externally induced laughter. The research provides evidence that laughter can be prescribed to improve mental health and well-being. This research aids the utilization of EEG data for laughter analysis and unlocks paths for future studies into the therapeutic use of laughter for mental health advancements.