Comparing three meditation traditions—Himalayan Yoga, Isha Shoonya, and Vipassana—using EEG to build functional brain networks and graph measures reveals distinct neural patterns. Classifying traditions against a control group with support vector machines achieved up to 90% accuracy (alpha band for Isha Shoonya). Key findings include higher delta connectivity in Vipassana meditators, stronger synchronization of left anterior frontal theta networks across traditions, greater gamma2 processing in Himalayan and Vipassana meditators, increased left frontal activity in theta and gamma bands for all meditators, and extensive modularity in gamma processing. The work suggests implications for neurotechnology to guide novice meditators.
A lightweight convolutional neural network (CNN) is proposed to classify cognitive states from EEG recordings. The pipeline first converts neural time-series signals into 2D spectral images that preserve electrode relationships and spectral properties, then uses a network with standard, depth-wise, and separable convolutions to reduce parameters. Tested on an open-access meditation dataset with expert, nonexpert, and control states, the model achieves comparable classification performance to six machine learning classifiers and four deep learning models while using less than 4% of their parameters, making it suitable for real-time neurofeedback.
Machine learning models can detect moments of distraction during meditation by analyzing EEG signals. Using data from 24 novice meditators performing breath focus meditation, researchers extracted twelve linear and non-linear EEG features and tested ten supervised classifiers. Linear features achieved up to 86% accuracy in distinguishing awake from sleepy states, while non-linear features reached nearly 78% accuracy in distinguishing awake from mind-wandering states. Unsupervised t-SNE visualization confirmed distinct clusters for each condition. These findings support the development of mobile EEG neurofeedback protocols that alert meditators when they become distracted.