A machine learning pipeline that fuses two feature-extraction methods—Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT)—classified loving-kindness meditation (LKM) versus non-meditation states from EEG data more accurately than either method alone. Using multiple sessions of EEG from 12 participants, the combined pipeline achieved an overall accuracy of 72.9%, compared to 67.1% for CSP alone and 67.8% for STFT alone. The highest mean accuracy for a single participant was 75.5% for LKM-Self versus non-meditation with five sessions, and the top individual accuracy reached 88.9%. Classification improved as the number of training sessions increased from two to four.
Classifying meditation versus non-meditation from EEG data across different people and multiple sessions is feasible but less accurate than classifying within a single person. Twelve participants each completed five sessions of loving-kindness meditation (directed toward self or others) and non-meditation. Machine learning algorithms trained on session data from a common pool achieved mean accuracies up to 62.3% for distinguishing self-directed meditation from non-meditation, compared to 72.1% in previous within-subject work. Combining two feature extraction methods (common spatial patterns and short-time Fourier transform) outperformed either method alone in 83.3% of test instances, and using more training sessions improved accuracy in 75.0% of instances. Results varied widely depending on which sessions were selected for training and testing.
Consciousness arises from brain and nervous system activity, producing subjective experiences and awareness of specific content. Researchers have used neuroimaging to identify neural correlates of consciousness, but correlates are not causes. To understand the processes generating consciousness, brain activity must be manipulated. Transcranial magnetic stimulation (TMS) offers one method to examine effects on conscious states and contents. This review surveys consciousness research with emphasis on TMS studies, covering neural substrates of states of consciousness (wakefulness, sleep, disorders of consciousness) and contents of consciousness via perceptual awareness literature, highlighting controversies and future research directions.