The effect of brief and focused mindfulness meditation on young adults using EEG approach with machine learning

Our study seeks to investigate the neural mechanisms underlying brief mindfulness meditation through the utilization of electroencephalogram (EEG) recordings and machine learning techniques. Twenty-four participants underwent mindfulness training, with pre- and post-practice physiological assessments and EEG recordings. Deep learning was used to analyze EEG data to predict mindfulness states. Models like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) classified meditation vs. rest, with LSTM achieving 72.8% accuracy, MLP 62.4%, and CNN 64.1%. In addition, EEG analysis revealed that reduced frontal delta activity and increased alpha, beta, and gamma activity during a brief and focused mindfulness meditation. The power spectral density (PSD) of focused mindfulness meditation state in the left (F7) and right frontal (F4, TP10) was positively correlated with difference in heart rate before and after meditation. Our results in EEG and physiological measurements suggested that brief and focused mindfulness meditation could reduce anxiety and stress by inducing a hypometabolic state of the body and a tranquil but alert state of the mind. The LSTM model is a reliable data-driven approach to predict the state of focused mindfulness meditation.